Since falls are a major public health problem among older people, the number of systems aimed at detecting them has increased dramatically over recent years. This work presents an extensive literature review of fall detection systems, including comparisons among various kinds of studies. It aims to serve as a reference for both clinicians and biomedical engineers planning or conducting field investigations. Challenges, issues and trends in fall detection have been identified after the reviewing work. The number of studies using context aware techniques is still increasing but there is a new trend towards the integration of fall detection into smart phones as well as the use of machine learning methods in the detection algorithm. We have also identified challenges regarding performance under real life conditions, usability, and user acceptance as well as issues related to power consumption, real time operations, sensing limitations, privacy and record of real life falls. Nikita Vidua | Prof. Avinash Sharma "Analysis of Fall Detection Systems: A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29467.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/29467/analysis-of-fall-detection-systems-a-review/nikita-vidua
Unmanned aerial vehicles (UAVs) have great potential for monitoring, detecting, and fighting forest fires due to their maneuverability, operational range, and improved safety over manned aircraft. This paper reviews technologies for UAV-based forest fire monitoring, detection, and fighting, which have progressed over the last decade. Section 1 discusses the advantages of UAVs for this application. Section 2 provides an overview of existing UAV systems for forest fire monitoring, detection, and fighting. Section 3 focuses on key technologies such as fire detection and image processing, as well as challenges. The conclusion suggests future directions for computer vision-based forest firefighting using UAVs.
IRJET - A Deep Novel Study on Different CNN Algorithms for Face Skin Disease ...IRJET Journal
This document summarizes a study that used convolutional neural networks (CNNs) to classify facial skin diseases using clinical images. Specifically:
- The study established a dataset of 2656 facial images belonging to 6 common skin diseases from a large Chinese clinical image dataset.
- Five CNN models (ResNet-50, Inception-v3, DenseNet121, Xception, Inception-ResNet-v2) were trained on the dataset with and without transfer learning from other body part images.
- The Inception-ResNet-v2 model achieved the best performance, with a mean recall of 77.0% and precision of 70.8% on the test set when using transfer learning. Some diseases like LE, B
This document summarizes a research paper on a fall detection system using a tri-axial accelerometer for wireless body area networks. The system is designed to monitor elderly individuals and detect if they have fallen. If a fall is detected, it will automatically send an SMS message with the person's location via GPS to emergency contacts and services. The proposed system uses a tri-axial accelerometer and microcontroller to classify a person's posture and detect if they have fallen based on changes in angle and acceleration. It aims to address limitations of existing fall detection methods by utilizing widely available mobile phones and providing a small, comfortable device. If a fall is detected and emergency services are quickly contacted, it could help save lives by facilitating rapid medical response.
Extreme deep learning in biosecurity the case of machine hearing for marine s...Konstantinos Demertzis
Biosafety is defined as a set of preventive measures aimed at
reducing the risk of infectious diseases’ spread to crops and
animals, by providing quarantine pesticides. Prolonged and
sustained overheating of the sea, creates significant habitat losses,
resulting in the proliferation and spread of invasive species, which invade foreign areas typically seeking colder climate. This is one of the most important modern threats to marine biosafety. The research effort presented herein, proposes an innovative approach
for Marine Species Identification, by employing an advanced
intelligent Machine Hearing Framework (MHF). The final target is the identification of invasive alien species (IAS) based on the
sounds they produce. This classification attempt, can provide
significant aid towards the protection of biodiversity, and can
achieve overall regional biosecurity. Hearing recognition is
performed by using the Online Sequential Multilayer Graph
Regularized Extreme Learning Machine Autoencoder
(MIGRATE_ELM). The MIGRATE_ELM uses an innovative Deep Learning algorithm (DELE) that is applied for the first time for the above purpose. The assignment of the corresponding class ‘native’ or ‘invasive’ in its locality, is carried out by an equally innovative approach entitled ‘Geo Location Country Based Service’ that has been proposed by our research team.
This document presents a method for detecting melanoma skin cancer using image processing and machine learning techniques. Images of skin lesions are first segmented using active contour models. Features like color, size, shape and texture are then extracted from the segmented images. Texture is analyzed using local binary patterns (LBP). The extracted features are used to classify images as melanoma or non-melanoma using a support vector machine (SVM) classifier. The goal is to develop an automated system for early detection of melanoma to help reduce death rates from this dangerous form of skin cancer.
A Review on Brain Disorder Segmentation in MR ImagesIJMER
This document reviews various methods for automatically detecting brain tumors from MRI scans using computer-aided systems. It summarizes segmentation and classification approaches that have been used, including thresholding, region growing, genetic algorithms, clustering, and neural networks. The most common techniques are thresholding, region-based segmentation, and support vector machines or neural networks for classification. While these methods have achieved some success, challenges remain in developing systems that can accurately classify tumor types with high performance on diverse datasets. Future work may explore combining discrete and continuous segmentation approaches to improve computational efficiency and detection accuracy.
IRJET- Skin Disease Detection using Image Processing with Data Mining and Dee...IRJET Journal
This document presents a skin disease detection method using image processing, data mining, and deep learning techniques. The proposed system uses a mobile application where users can upload images of affected skin areas. The images then undergo preprocessing like filtering and segmentation. Features are extracted from the images using techniques like 2D wavelet transform and GLCM. These features are classified using support vector machine (SVM) and convolutional neural network (CNN) models. The results show that CNN achieves higher overall accuracy compared to SVM, with accuracies of 99.1% for CNN vs 90.7% for SVM.
The document lists Shamik Tiwari's research publications and academic activities. It includes 5 published journal articles, 1 book chapter, and 2 accepted journal articles. It also lists that he coordinates academic monitoring, curriculum development, guides Ph.D. students, delivered lectures, and serves as a reviewer for several journals. He has also completed many online courses and achieved high student feedback for his online teaching.
Unmanned aerial vehicles (UAVs) have great potential for monitoring, detecting, and fighting forest fires due to their maneuverability, operational range, and improved safety over manned aircraft. This paper reviews technologies for UAV-based forest fire monitoring, detection, and fighting, which have progressed over the last decade. Section 1 discusses the advantages of UAVs for this application. Section 2 provides an overview of existing UAV systems for forest fire monitoring, detection, and fighting. Section 3 focuses on key technologies such as fire detection and image processing, as well as challenges. The conclusion suggests future directions for computer vision-based forest firefighting using UAVs.
IRJET - A Deep Novel Study on Different CNN Algorithms for Face Skin Disease ...IRJET Journal
This document summarizes a study that used convolutional neural networks (CNNs) to classify facial skin diseases using clinical images. Specifically:
- The study established a dataset of 2656 facial images belonging to 6 common skin diseases from a large Chinese clinical image dataset.
- Five CNN models (ResNet-50, Inception-v3, DenseNet121, Xception, Inception-ResNet-v2) were trained on the dataset with and without transfer learning from other body part images.
- The Inception-ResNet-v2 model achieved the best performance, with a mean recall of 77.0% and precision of 70.8% on the test set when using transfer learning. Some diseases like LE, B
This document summarizes a research paper on a fall detection system using a tri-axial accelerometer for wireless body area networks. The system is designed to monitor elderly individuals and detect if they have fallen. If a fall is detected, it will automatically send an SMS message with the person's location via GPS to emergency contacts and services. The proposed system uses a tri-axial accelerometer and microcontroller to classify a person's posture and detect if they have fallen based on changes in angle and acceleration. It aims to address limitations of existing fall detection methods by utilizing widely available mobile phones and providing a small, comfortable device. If a fall is detected and emergency services are quickly contacted, it could help save lives by facilitating rapid medical response.
Extreme deep learning in biosecurity the case of machine hearing for marine s...Konstantinos Demertzis
Biosafety is defined as a set of preventive measures aimed at
reducing the risk of infectious diseases’ spread to crops and
animals, by providing quarantine pesticides. Prolonged and
sustained overheating of the sea, creates significant habitat losses,
resulting in the proliferation and spread of invasive species, which invade foreign areas typically seeking colder climate. This is one of the most important modern threats to marine biosafety. The research effort presented herein, proposes an innovative approach
for Marine Species Identification, by employing an advanced
intelligent Machine Hearing Framework (MHF). The final target is the identification of invasive alien species (IAS) based on the
sounds they produce. This classification attempt, can provide
significant aid towards the protection of biodiversity, and can
achieve overall regional biosecurity. Hearing recognition is
performed by using the Online Sequential Multilayer Graph
Regularized Extreme Learning Machine Autoencoder
(MIGRATE_ELM). The MIGRATE_ELM uses an innovative Deep Learning algorithm (DELE) that is applied for the first time for the above purpose. The assignment of the corresponding class ‘native’ or ‘invasive’ in its locality, is carried out by an equally innovative approach entitled ‘Geo Location Country Based Service’ that has been proposed by our research team.
This document presents a method for detecting melanoma skin cancer using image processing and machine learning techniques. Images of skin lesions are first segmented using active contour models. Features like color, size, shape and texture are then extracted from the segmented images. Texture is analyzed using local binary patterns (LBP). The extracted features are used to classify images as melanoma or non-melanoma using a support vector machine (SVM) classifier. The goal is to develop an automated system for early detection of melanoma to help reduce death rates from this dangerous form of skin cancer.
A Review on Brain Disorder Segmentation in MR ImagesIJMER
This document reviews various methods for automatically detecting brain tumors from MRI scans using computer-aided systems. It summarizes segmentation and classification approaches that have been used, including thresholding, region growing, genetic algorithms, clustering, and neural networks. The most common techniques are thresholding, region-based segmentation, and support vector machines or neural networks for classification. While these methods have achieved some success, challenges remain in developing systems that can accurately classify tumor types with high performance on diverse datasets. Future work may explore combining discrete and continuous segmentation approaches to improve computational efficiency and detection accuracy.
IRJET- Skin Disease Detection using Image Processing with Data Mining and Dee...IRJET Journal
This document presents a skin disease detection method using image processing, data mining, and deep learning techniques. The proposed system uses a mobile application where users can upload images of affected skin areas. The images then undergo preprocessing like filtering and segmentation. Features are extracted from the images using techniques like 2D wavelet transform and GLCM. These features are classified using support vector machine (SVM) and convolutional neural network (CNN) models. The results show that CNN achieves higher overall accuracy compared to SVM, with accuracies of 99.1% for CNN vs 90.7% for SVM.
The document lists Shamik Tiwari's research publications and academic activities. It includes 5 published journal articles, 1 book chapter, and 2 accepted journal articles. It also lists that he coordinates academic monitoring, curriculum development, guides Ph.D. students, delivered lectures, and serves as a reviewer for several journals. He has also completed many online courses and achieved high student feedback for his online teaching.
Melanoma Skin Cancer Detection using Image Processing and Machine Learningijtsrd
Dermatological Diseases are one of the biggest medical issues in 21st century due to its highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. In cases of fatal diseases like Melanoma diagnosis in early stages play a vital role in determining the probability of getting cured. We believe that the application of automated methods will help in early diagnosis especially with the set of images with variety of diagnosis. Hence, in this article we present a completely automated system of dermatological disease recognition through lesion images, a machine intervention in contrast to conventional medical personnel based detection. Our model is designed into three phases compromising of data collection and augmentation, designing model and finally prediction. We have used multiple AI algorithms like Convolutional Neural Network and Support Vector Machine and amalgamated it with image processing tools to form a better structure, leading to higher accuracy of 85 . Vijayalakshmi M M ""Melanoma Skin Cancer Detection using Image Processing and Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23936.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/23936/melanoma-skin-cancer-detection-using-image-processing-and-machine-learning/vijayalakshmi-m-m
IoT-based air quality monitoring systems for smart cities: A systematic mappi...IJECEIAES
The increased level of air pollution in big cities has become a major concern for several organizations and authorities because of the risk it represents to human health. In this context, the technology has become a very useful tool in the contamination monitoring and the possible mitigation of its impact. Particularly, there are different proposals using the internet of things (IoT) paradigm that use interconnected sensors in order to measure different pollutants. In this paper, we develop a systematic mapping study defined by a five-step methodology to identify and analyze the research status in terms of IoT-based air pollution monitoring systems for smart cities. The study includes 55 proposals, some of which have been implemented in a real environment. We analyze and compare these proposals in terms of different parameters defined in the mapping and highlight some challenges for air quality monitoring systems implementation into the smart city context.
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
IRJET- Skin Cancer Prediction using Image Processing and Deep LearningIRJET Journal
This document discusses using deep learning and image processing to develop a model for skin cancer detection. It begins with an introduction to the rising problem of skin cancer cases and importance of early detection. Next, it describes the process of visual inspection and dermoscopy images currently used by dermatologists. The document then reviews literature on existing methods for skin cancer detection using machine learning approaches like convolutional neural networks (CNNs). Deeper CNN models that can learn from limited data are highlighted. Finally, the document outlines the fundamentals of different types of skin cancer and concludes by acknowledging guidance received to complete the project.
TOP CITED UBICOMPUTING ARTICLES IN 2013 - International Journal of Ubiquitous...ijujournal
International Journal of Ubiquitous Computing (IJU) is a quarterly open access peer-reviewed journal that provides excellent international forum for sharing knowledge and results in theory, methodology and applications of ubiquitous computing. Current information age is witnessing a dramatic use of digital and electronic devices in the workplace and beyond. Ubiquitous Computing presents a rather arduous requirement of robustness, reliability and availability to the end user. Ubiquitous computing has received a significant and sustained research interest in terms of designing and deploying large scale and high performance computational applications in real life. The aim of the journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field
Cybernetics is an interdisciplinary study of regulatory systems, their structures, constraints and possibilities. Cybernetics was defined by Norbert Wiener in 1948 as “the scientific study of control and communication in living organism and the machine”. Cybernetics study includes but not limited to artificial learning, adaptation, cognition, convergence, Social Control, efficacy, efficiency, connectivity and communication [1]. It is known from science fiction; technically modified organism with exceptional skills called as cyborgs -it was originated from the term “cybernetic organism”. As a matter of fact, cyborgs that incorporates technical systems with living organism are already reality. For instance, smart machines that spontaneously operate to changing dynamic conditions, computer supported designs and fabrication based on magnetic tomography datasets or surface modifications for enhanced tissue integration that allowed major development in cybernetics technology [2,3].
Human gait recognition using preprocessing and classification techniques IJECEIAES
Biometric recognition systems have been attracted numerous researchers since they attempt to overcome the problems and factors weakening these systems including problems of obtaining images indeed not appearing the resolution or the object completely. In this work, the object movement reliance was considered to distinguish the human through his/her gait. Some losing features probably weaken the system’s capability in recognizing the people, hence, we propose using all data recorded by the Kinect sensor with no employing the feature extraction methods based on the literature. In these studies, coordinates of 20 points are recorded for each person in various genders and ages, walking with various directions and speeds, creating 8404 constraints. Moreover, pre-processing methods are utilized to measure its influences on the system efficiency through testing on six types of classifiers. Within the proposed approach, a noteworthy recognition rate was obtained reaching 91% without examining the descriptors.
New Research Articles 2019 July Issue International Journal of Artificial Int...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas
IRJET- Detection of Leaf Diseases and Classifying them using Multiclass SVMIRJET Journal
This document presents a method for detecting and classifying leaf diseases using image processing techniques. The proposed method involves image acquisition, preprocessing using median filtering, segmentation using k-means clustering, feature extraction of texture features using GLCM, and classification using multiclass SVM. Median filtering is used for noise removal before segmentation. K-means clustering segments the leaf from the image. GLCM extracts statistical texture features from the segmented leaf images. These features are then classified using multiclass SVM to identify the disease, achieving an accuracy of 97%. The method provides a fast and accurate way to detect leaf diseases using digital image processing and machine learning techniques.
The document discusses the Environmental Information System (ENVIS) in India. It begins by acknowledging the support of an environmental science teacher. It then provides an overview of ENVIS, including its objectives to build an environmental information repository and strengthen information collection, processing and dissemination capabilities.
ENVIS operates as a decentralized network of specialized centers covering different environmental subject areas. The key roles of ENVIS centers and nodes are to establish information sources, create databases on assigned topics, identify information gaps, publish newsletters and bulletins, and serve as an interface for users.
Specific details are provided about the focal point located in the Ministry of Environment and Forests, which works to build the information base and respond to queries. An
An Analysis of The Methods Employed for Breast Cancer Diagnosis IJORCS
This document analyzes various methods used for breast cancer diagnosis. It discusses that artificial neural networks (ANNs) have been widely applied and are shown to increase diagnostic accuracy compared to individual methods. Multiple neural networks together provide better results than single networks. Recent approaches combine ANNs with other techniques like fuzzy logic. Overall, ANNs have become a robust and accurate system for breast cancer diagnosis and extending their use could help other diseases.
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
IRJET- Lung Cancer Detection using Digital Image Processing and Artificia...IRJET Journal
This document discusses a proposed system to detect lung cancer at early stages using digital image processing and artificial neural networks. The system consists of several steps: image acquisition, preprocessing using histogram equalization, segmentation using thresholding, dilation, image filling, feature extraction from CT images, and classification of images using an artificial neural network. The goal is to develop an automated diagnostic system that can maximize the detection of true positive lung cancer cases while minimizing false negatives to improve early detection rates and patient outcomes.
Skin Cancer Detection using Digital Image Processing and Implementation using...ijtsrd
Melanoma is a serious type of skin cancer. It starts in skin cells called melanocytes. There are 3 main types of skin cancer, Melanoma, Basal and Squamous cell carcinoma. Melanoma is more likely to spread to other parts of the body. Early detection of malignant melanoma in dermoscopy images is very important and critical, since its detection in the early stage can be helpful to cure it. Computer Aided Diagnosis systems can be very helpful to facilitate the early detection of cancers for dermatologists. Image processing is a commonly used method for skin cancer detection from the appearance of affected area on the skin. In this work, a computerised method has been developed to make use of Neural Networks in the field of medical image processing. The ultimate aim of this paper is to implement cost-effective emergency support systems to process the medical images. It is more advantageous to patients. The dermoscopy image of suspect area of skin cancer is taken and it goes under various pre-processing technique for noise removal and image enhancement. Then the image is undergone to segmentation using Thresholding method. Some features of image have to be extracted using ABCD rules. In this work, Asymmetry index and Geometric features are extracted from the segmented image. These features are given as the input to classifier. Artificial Neural Network ANN with feed forward architecture is used for classification purpose. It classifies the given image into cancerous or non-cancerous. The proposed algorithm has been tested on the ISIC International Skin Imaging Collaboration 2017 training and test datasets. The ground truth data of each image is available as well, so performance of this work can evaluate quantitatively. Khaing Thazin Oo | Dr. Moe Mon Myint | Dr. Khin Thuzar Win "Skin Cancer Detection using Digital Image Processing and Implementation using ANN and ABCD Features" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18751.pdf
An evaluation of automated tumor detection techniques of brain magnetic reson...Salam Shah
Image processing is a technique developed by computer and Information technology scientist and being used in all field of research including medical sciences. The focus of this paper is the use of image processing in tumor detection from the brain Magnetic Resonance Imaging (MRI). For the brain tumor detection, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are the prominent imaging techniques, but most of the experts prefer MRI over CT. The traditional method of tumor detection in MRI images is a manual inspection which provides variations in the results when analyzed by different experts, therefore, in view of the limitations of the manual analysis of MRI, there is a need for an automated system that can produce globally acceptable and accurate results. There is enough amount of published literature available to replace the manual inspection process of MRI images with the digital computer system using image processing techniques. In this paper, we have provided a review of digital image processing techniques in the context of brain MRI processing and critically analyzed them for the identification of the gaps and limitations of the techniques so that the gaps can be filled and limitations of various techniques can be improved for precise and better results.
Optical Coherence Tomography: Technology and applications for neuroimagingManish Kumar
Optical coherence tomography (OCT) is an emerging imaging technology with applications in biology, medicine, and materials investigations. Attractive features include high cellular-level resolution, real-time acquisition rates, and spectroscopic feature extraction in a compact noninvasive instrument. OCT can perform ‘‘optical biopsies’’ of tissue, producing images approaching the resolution of histology without having to resect and histologically process tissue specimens for characterization and diagnosis.
Computer Vision for Skin Cancer Diagnosis and Recognition using RBF and SOMCSCJournals
Human skin is the largest organ in our body which provides protection against heat, light, infections and injury. It also stores water, fat, and vitamin. Cancer is the leading cause of death in economically developed countries and the second leading cause of death in developing countries. Skin cancer is the most commonly diagnosed type of cancer among men and women. Exposure to UV rays, modernize diets, smoking, alcohol and nicotine are the main cause. Cancer is increasingly recognized as a critical public health problem in Ethiopia. There are three type of skin cancer and they are recognized based on their own properties. In view of this, a digital image processing technique is proposed to recognize and predict the different types of skin cancers using digital image processing techniques. Sample skin cancer image were taken from American cancer society research center and DERMOFIT which are popular and widely focuses on skin cancer research. The classification system was supervised corresponding to the predefined classes of the type of skin cancer. Combining Self organizing map (SOM) and radial basis function (RBF) for recognition and diagnosis of skin cancer is by far better than KNN, Naïve Bayes and ANN classifier. It was also showed that the discrimination power of morphology and color features was better than texture features but when morphology, texture and color features were used together the classification accuracy was increased. The best classification accuracy (88%, 96.15% and 95.45% for Basal cell carcinoma, Melanoma and Squamous cell carcinoma respectively) were obtained using combining SOM and RBF. The overall classification accuracy was 93.15%.
CALCULATION OF AREA, CENTER AND DISTANCE OF CERVICAL CANCER FROM ORGAN AT RIS...AM Publications
Radiotherapy becomes one of the options in the treatment of cervical cancer. In the process, radiotherapy requires a radiation dose plan for a target volume, including Gross Tumor Volume (GTV) and Organ at Risk (OAR). The planning is based on the acquisition image of CT scan modalities. In this study, the calculation of area, center and distance of cervical cancer from organ at risk on CT image of pelvis for cervical cancer case through digital image processing method. Stages used include image segmentation with histogram, morphological operation, and the determination of the midpoint (centroid) in the cervical, bladder, and cancerous mass. The calculation of the extent of cervical cancer was performed on seven images which were then compared with the calculations performed radiologist manually. The results of the calculation of the method offered has a percentage error of 0.3% and 39.7% of the value indicates that the image processing techniques offered can be implemented to calculate the extent of cervical cancer and organ distance at risk with cancer centers based on the coordinates of the center point.
The document discusses computer assisted screening of microcalcifications in digitized mammograms for early detection of breast cancer. It begins with an introduction to breast cancer and computer aided detection and diagnosis systems. It then provides background on areas of interest including improvement of pictorial information and machine vision. Next, it discusses microcalcifications, mammography, and mammograms. The document reviews literature on various preprocessing, feature extraction, and detection techniques. It identifies challenges in microcalcification detection including their small size and variable clusters. Finally, it outlines the plan of action for the thesis including use of the mini-MIAS mammogram database and a range of techniques to remove pectoral muscle and x-ray labels.
Fibrillation Detection using Accelerometer and Gyroscope of a Smartphoneijtsrd
Using the smartphone as an answer for the identification of Atrial Fibrillation (AFib), which uses the built-in accelerometer and gyroscope sensors (Inertial Measurement Unit, IMU) of the smartphone for detection? Contingent upon the patients circumstance, it is conceivable to utilize the created cell phone application either routinely or at times for making an estimation of the subject with no outer sensors is required. From that point forward, the application decides if the patient experiences AFib or not. Arun Pranav K. R | Elavarasan C"Fibrillation Detection using Accelerometer and Gyroscope of a Smartphone" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11074.pdf http://www.ijtsrd.com/computer-science/other/11074/fibrillation-detection-using-accelerometer-and-gyroscope-of-a-smartphone/arun-pranav-k-r
IRJET- Elderly Care-Taking and Fall Detection SystemIRJET Journal
This document summarizes an elderly care and fall detection system presented in the International Research Journal of Engineering and Technology. The system uses wearable accelerometer sensors and a Raspberry Pi to detect falls in elderly individuals. It also includes a medication reminder system. The system was trained using an artificial neural network algorithm on fall data collected from accelerometers. It achieved 98% accuracy in detecting four types of falls: front, back, left, and right. The system aims to promptly detect falls in elderly to reduce injuries and notify caregivers in emergency situations. It seeks to improve elderly independent living by monitoring medication intake and detecting falls.
Melanoma Skin Cancer Detection using Image Processing and Machine Learningijtsrd
Dermatological Diseases are one of the biggest medical issues in 21st century due to its highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. In cases of fatal diseases like Melanoma diagnosis in early stages play a vital role in determining the probability of getting cured. We believe that the application of automated methods will help in early diagnosis especially with the set of images with variety of diagnosis. Hence, in this article we present a completely automated system of dermatological disease recognition through lesion images, a machine intervention in contrast to conventional medical personnel based detection. Our model is designed into three phases compromising of data collection and augmentation, designing model and finally prediction. We have used multiple AI algorithms like Convolutional Neural Network and Support Vector Machine and amalgamated it with image processing tools to form a better structure, leading to higher accuracy of 85 . Vijayalakshmi M M ""Melanoma Skin Cancer Detection using Image Processing and Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23936.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/23936/melanoma-skin-cancer-detection-using-image-processing-and-machine-learning/vijayalakshmi-m-m
IoT-based air quality monitoring systems for smart cities: A systematic mappi...IJECEIAES
The increased level of air pollution in big cities has become a major concern for several organizations and authorities because of the risk it represents to human health. In this context, the technology has become a very useful tool in the contamination monitoring and the possible mitigation of its impact. Particularly, there are different proposals using the internet of things (IoT) paradigm that use interconnected sensors in order to measure different pollutants. In this paper, we develop a systematic mapping study defined by a five-step methodology to identify and analyze the research status in terms of IoT-based air pollution monitoring systems for smart cities. The study includes 55 proposals, some of which have been implemented in a real environment. We analyze and compare these proposals in terms of different parameters defined in the mapping and highlight some challenges for air quality monitoring systems implementation into the smart city context.
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
IRJET- Skin Cancer Prediction using Image Processing and Deep LearningIRJET Journal
This document discusses using deep learning and image processing to develop a model for skin cancer detection. It begins with an introduction to the rising problem of skin cancer cases and importance of early detection. Next, it describes the process of visual inspection and dermoscopy images currently used by dermatologists. The document then reviews literature on existing methods for skin cancer detection using machine learning approaches like convolutional neural networks (CNNs). Deeper CNN models that can learn from limited data are highlighted. Finally, the document outlines the fundamentals of different types of skin cancer and concludes by acknowledging guidance received to complete the project.
TOP CITED UBICOMPUTING ARTICLES IN 2013 - International Journal of Ubiquitous...ijujournal
International Journal of Ubiquitous Computing (IJU) is a quarterly open access peer-reviewed journal that provides excellent international forum for sharing knowledge and results in theory, methodology and applications of ubiquitous computing. Current information age is witnessing a dramatic use of digital and electronic devices in the workplace and beyond. Ubiquitous Computing presents a rather arduous requirement of robustness, reliability and availability to the end user. Ubiquitous computing has received a significant and sustained research interest in terms of designing and deploying large scale and high performance computational applications in real life. The aim of the journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field
Cybernetics is an interdisciplinary study of regulatory systems, their structures, constraints and possibilities. Cybernetics was defined by Norbert Wiener in 1948 as “the scientific study of control and communication in living organism and the machine”. Cybernetics study includes but not limited to artificial learning, adaptation, cognition, convergence, Social Control, efficacy, efficiency, connectivity and communication [1]. It is known from science fiction; technically modified organism with exceptional skills called as cyborgs -it was originated from the term “cybernetic organism”. As a matter of fact, cyborgs that incorporates technical systems with living organism are already reality. For instance, smart machines that spontaneously operate to changing dynamic conditions, computer supported designs and fabrication based on magnetic tomography datasets or surface modifications for enhanced tissue integration that allowed major development in cybernetics technology [2,3].
Human gait recognition using preprocessing and classification techniques IJECEIAES
Biometric recognition systems have been attracted numerous researchers since they attempt to overcome the problems and factors weakening these systems including problems of obtaining images indeed not appearing the resolution or the object completely. In this work, the object movement reliance was considered to distinguish the human through his/her gait. Some losing features probably weaken the system’s capability in recognizing the people, hence, we propose using all data recorded by the Kinect sensor with no employing the feature extraction methods based on the literature. In these studies, coordinates of 20 points are recorded for each person in various genders and ages, walking with various directions and speeds, creating 8404 constraints. Moreover, pre-processing methods are utilized to measure its influences on the system efficiency through testing on six types of classifiers. Within the proposed approach, a noteworthy recognition rate was obtained reaching 91% without examining the descriptors.
New Research Articles 2019 July Issue International Journal of Artificial Int...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas
IRJET- Detection of Leaf Diseases and Classifying them using Multiclass SVMIRJET Journal
This document presents a method for detecting and classifying leaf diseases using image processing techniques. The proposed method involves image acquisition, preprocessing using median filtering, segmentation using k-means clustering, feature extraction of texture features using GLCM, and classification using multiclass SVM. Median filtering is used for noise removal before segmentation. K-means clustering segments the leaf from the image. GLCM extracts statistical texture features from the segmented leaf images. These features are then classified using multiclass SVM to identify the disease, achieving an accuracy of 97%. The method provides a fast and accurate way to detect leaf diseases using digital image processing and machine learning techniques.
The document discusses the Environmental Information System (ENVIS) in India. It begins by acknowledging the support of an environmental science teacher. It then provides an overview of ENVIS, including its objectives to build an environmental information repository and strengthen information collection, processing and dissemination capabilities.
ENVIS operates as a decentralized network of specialized centers covering different environmental subject areas. The key roles of ENVIS centers and nodes are to establish information sources, create databases on assigned topics, identify information gaps, publish newsletters and bulletins, and serve as an interface for users.
Specific details are provided about the focal point located in the Ministry of Environment and Forests, which works to build the information base and respond to queries. An
An Analysis of The Methods Employed for Breast Cancer Diagnosis IJORCS
This document analyzes various methods used for breast cancer diagnosis. It discusses that artificial neural networks (ANNs) have been widely applied and are shown to increase diagnostic accuracy compared to individual methods. Multiple neural networks together provide better results than single networks. Recent approaches combine ANNs with other techniques like fuzzy logic. Overall, ANNs have become a robust and accurate system for breast cancer diagnosis and extending their use could help other diseases.
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
IRJET- Lung Cancer Detection using Digital Image Processing and Artificia...IRJET Journal
This document discusses a proposed system to detect lung cancer at early stages using digital image processing and artificial neural networks. The system consists of several steps: image acquisition, preprocessing using histogram equalization, segmentation using thresholding, dilation, image filling, feature extraction from CT images, and classification of images using an artificial neural network. The goal is to develop an automated diagnostic system that can maximize the detection of true positive lung cancer cases while minimizing false negatives to improve early detection rates and patient outcomes.
Skin Cancer Detection using Digital Image Processing and Implementation using...ijtsrd
Melanoma is a serious type of skin cancer. It starts in skin cells called melanocytes. There are 3 main types of skin cancer, Melanoma, Basal and Squamous cell carcinoma. Melanoma is more likely to spread to other parts of the body. Early detection of malignant melanoma in dermoscopy images is very important and critical, since its detection in the early stage can be helpful to cure it. Computer Aided Diagnosis systems can be very helpful to facilitate the early detection of cancers for dermatologists. Image processing is a commonly used method for skin cancer detection from the appearance of affected area on the skin. In this work, a computerised method has been developed to make use of Neural Networks in the field of medical image processing. The ultimate aim of this paper is to implement cost-effective emergency support systems to process the medical images. It is more advantageous to patients. The dermoscopy image of suspect area of skin cancer is taken and it goes under various pre-processing technique for noise removal and image enhancement. Then the image is undergone to segmentation using Thresholding method. Some features of image have to be extracted using ABCD rules. In this work, Asymmetry index and Geometric features are extracted from the segmented image. These features are given as the input to classifier. Artificial Neural Network ANN with feed forward architecture is used for classification purpose. It classifies the given image into cancerous or non-cancerous. The proposed algorithm has been tested on the ISIC International Skin Imaging Collaboration 2017 training and test datasets. The ground truth data of each image is available as well, so performance of this work can evaluate quantitatively. Khaing Thazin Oo | Dr. Moe Mon Myint | Dr. Khin Thuzar Win "Skin Cancer Detection using Digital Image Processing and Implementation using ANN and ABCD Features" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18751.pdf
An evaluation of automated tumor detection techniques of brain magnetic reson...Salam Shah
Image processing is a technique developed by computer and Information technology scientist and being used in all field of research including medical sciences. The focus of this paper is the use of image processing in tumor detection from the brain Magnetic Resonance Imaging (MRI). For the brain tumor detection, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are the prominent imaging techniques, but most of the experts prefer MRI over CT. The traditional method of tumor detection in MRI images is a manual inspection which provides variations in the results when analyzed by different experts, therefore, in view of the limitations of the manual analysis of MRI, there is a need for an automated system that can produce globally acceptable and accurate results. There is enough amount of published literature available to replace the manual inspection process of MRI images with the digital computer system using image processing techniques. In this paper, we have provided a review of digital image processing techniques in the context of brain MRI processing and critically analyzed them for the identification of the gaps and limitations of the techniques so that the gaps can be filled and limitations of various techniques can be improved for precise and better results.
Optical Coherence Tomography: Technology and applications for neuroimagingManish Kumar
Optical coherence tomography (OCT) is an emerging imaging technology with applications in biology, medicine, and materials investigations. Attractive features include high cellular-level resolution, real-time acquisition rates, and spectroscopic feature extraction in a compact noninvasive instrument. OCT can perform ‘‘optical biopsies’’ of tissue, producing images approaching the resolution of histology without having to resect and histologically process tissue specimens for characterization and diagnosis.
Computer Vision for Skin Cancer Diagnosis and Recognition using RBF and SOMCSCJournals
Human skin is the largest organ in our body which provides protection against heat, light, infections and injury. It also stores water, fat, and vitamin. Cancer is the leading cause of death in economically developed countries and the second leading cause of death in developing countries. Skin cancer is the most commonly diagnosed type of cancer among men and women. Exposure to UV rays, modernize diets, smoking, alcohol and nicotine are the main cause. Cancer is increasingly recognized as a critical public health problem in Ethiopia. There are three type of skin cancer and they are recognized based on their own properties. In view of this, a digital image processing technique is proposed to recognize and predict the different types of skin cancers using digital image processing techniques. Sample skin cancer image were taken from American cancer society research center and DERMOFIT which are popular and widely focuses on skin cancer research. The classification system was supervised corresponding to the predefined classes of the type of skin cancer. Combining Self organizing map (SOM) and radial basis function (RBF) for recognition and diagnosis of skin cancer is by far better than KNN, Naïve Bayes and ANN classifier. It was also showed that the discrimination power of morphology and color features was better than texture features but when morphology, texture and color features were used together the classification accuracy was increased. The best classification accuracy (88%, 96.15% and 95.45% for Basal cell carcinoma, Melanoma and Squamous cell carcinoma respectively) were obtained using combining SOM and RBF. The overall classification accuracy was 93.15%.
CALCULATION OF AREA, CENTER AND DISTANCE OF CERVICAL CANCER FROM ORGAN AT RIS...AM Publications
Radiotherapy becomes one of the options in the treatment of cervical cancer. In the process, radiotherapy requires a radiation dose plan for a target volume, including Gross Tumor Volume (GTV) and Organ at Risk (OAR). The planning is based on the acquisition image of CT scan modalities. In this study, the calculation of area, center and distance of cervical cancer from organ at risk on CT image of pelvis for cervical cancer case through digital image processing method. Stages used include image segmentation with histogram, morphological operation, and the determination of the midpoint (centroid) in the cervical, bladder, and cancerous mass. The calculation of the extent of cervical cancer was performed on seven images which were then compared with the calculations performed radiologist manually. The results of the calculation of the method offered has a percentage error of 0.3% and 39.7% of the value indicates that the image processing techniques offered can be implemented to calculate the extent of cervical cancer and organ distance at risk with cancer centers based on the coordinates of the center point.
The document discusses computer assisted screening of microcalcifications in digitized mammograms for early detection of breast cancer. It begins with an introduction to breast cancer and computer aided detection and diagnosis systems. It then provides background on areas of interest including improvement of pictorial information and machine vision. Next, it discusses microcalcifications, mammography, and mammograms. The document reviews literature on various preprocessing, feature extraction, and detection techniques. It identifies challenges in microcalcification detection including their small size and variable clusters. Finally, it outlines the plan of action for the thesis including use of the mini-MIAS mammogram database and a range of techniques to remove pectoral muscle and x-ray labels.
Fibrillation Detection using Accelerometer and Gyroscope of a Smartphoneijtsrd
Using the smartphone as an answer for the identification of Atrial Fibrillation (AFib), which uses the built-in accelerometer and gyroscope sensors (Inertial Measurement Unit, IMU) of the smartphone for detection? Contingent upon the patients circumstance, it is conceivable to utilize the created cell phone application either routinely or at times for making an estimation of the subject with no outer sensors is required. From that point forward, the application decides if the patient experiences AFib or not. Arun Pranav K. R | Elavarasan C"Fibrillation Detection using Accelerometer and Gyroscope of a Smartphone" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11074.pdf http://www.ijtsrd.com/computer-science/other/11074/fibrillation-detection-using-accelerometer-and-gyroscope-of-a-smartphone/arun-pranav-k-r
IRJET- Elderly Care-Taking and Fall Detection SystemIRJET Journal
This document summarizes an elderly care and fall detection system presented in the International Research Journal of Engineering and Technology. The system uses wearable accelerometer sensors and a Raspberry Pi to detect falls in elderly individuals. It also includes a medication reminder system. The system was trained using an artificial neural network algorithm on fall data collected from accelerometers. It achieved 98% accuracy in detecting four types of falls: front, back, left, and right. The system aims to promptly detect falls in elderly to reduce injuries and notify caregivers in emergency situations. It seeks to improve elderly independent living by monitoring medication intake and detecting falls.
Fall Detection System for the Elderly based on the Classification of Shimmer ...Moiz Ahmed
The purpose of this research was to use a body sensor network to analyze falls in elderly. Real-time data from Shimmer device could be the analysis for detection of certain activities of daily livings as well as certain cases of falls.
For more information read the publication:
http://pdf.medrang.co.kr/Hir/2017/023/Hir023-03-03.pdf
The document summarizes several papers related to crime detection using computer vision techniques. It discusses approaches for detecting fights in videos using features like STIP and MoSIFT descriptors. It also reviews methods for detecting emotions from body movements and recognizing crowd behaviors in video sequences. Several algorithms are presented, including FSCB for real-time crowd behavior detection and a three-pronged approach using texture, color, and motion history for moving object detection. The document analyzes trajectory-based and pixel-based techniques for unsupervised abnormal event detection.
IRJET- A Survey on Vision based Fall Detection TechniquesIRJET Journal
This document reviews different vision-based fall detection systems that have been developed using computer vision and image processing techniques. It discusses how vision-based systems work by capturing images or videos using cameras and then analyzing the footage using algorithms to classify events as falls or non-falls. The document also examines some of the challenges of vision-based approaches, such as effects of lighting and background objects, and how newer techniques like convolutional neural networks have helped improve accuracy of fall detection.
A Survey on Person Detection for Social Distancing and Safety Violation Alert...IRJET Journal
This document discusses methods for monitoring social distancing using video surveillance and deep learning techniques. It describes how faster R-CNN, single shot detector (SSD) and YOLO v3 deep learning models can be used to detect people in video frames and calculate the distance between individuals to determine if social distancing guidelines are being followed. If distances between people are found to be unsafe, the system can send alerts or cautions. The methodology is intended to help prevent the spread of COVID-19 by monitoring adherence to social distancing and triggering warnings if safety violations are detected.
Real-time Activity Recognition using Smartphone Accelerometerijtsrd
To identify the real time activities, an online algorithm need be considered. In this paper, we will first segment entire one activity as one time interval using Bayesian online detection method instead of fixed and small length time interval. Then, we introduce two layer random forest classification for real time activity recognition on the smartphone by embedded accelerometers. We evaluate the performance of our method based on six activities walking, upstairs, downstairs, sitting, standing, and laying on 30 volunteers. For the data considered, we get 92.4 overall accuracy based on six activities and 100 overall accuracy only based on dynamic activity and static activity. Shuang Na | Kandethody M. Ramachandran | Ming Ji | Yicheng Tu "Real-time Activity Recognition using Smartphone Accelerometer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29550.pdf Paper URL: https://www.ijtsrd.com/mathemetics/other/29550/real-time-activity-recognition-using-smartphone-accelerometer/shuang-na
Protection has become one of the biggest fields of study for several years, however the demand for this is
growing exponentially mostly with rise in sensitive data. The quality of the research can differ slightly from
any workstation to cloud, and though protection must be incredibly important all over. Throughout the past
two decades, sufficient focus has been given to substantiation along with validation in the technology
model. Identifying a legal person is increasingly become the difficult activity with the progression of time.
Some attempts are introduced in that same respect, in particular by utilizing human movements such as
fingerprints, facial recognition, palm scanning, retinal identification, DNA checking
FEATURE EXTRACTION METHODS FOR IRIS RECOGNITION SYSTEM: A SURVEYijcsit
This document summarizes several feature extraction methods for iris recognition systems. It discusses supervised, unsupervised, and semi-supervised learning approaches for iris recognition. It also reviews related literature on iris recognition techniques, including using wavelet transforms, SVM classifiers, and other feature extraction methods. Tables in the document compare different biometric traits and traditional biometric systems, as well as summarize reviewed articles on iris recognition with their main contributions. The methodology section describes the typical four steps of an iris recognition system: image acquisition, preprocessing, feature extraction, and matching/recognition. It also discusses various iris recognition methods and their performance measures.
Real Time Social Distance Detector using Deep learningIRJET Journal
The document describes a real-time social distance detector using deep learning. The researchers created a model called SocialdistancingNet-19 that can identify people in frames from video and label them as safe or dangerous based on whether they are more than a certain distance threshold from others. They used a pre-trained YOLO v3 object detection model to detect people and then calculated the distance between centroids of detected objects to assess social distancing compliance. The model achieved 92.8% accuracy on test data. It is intended to automatically monitor social distancing in public spaces using video surveillance to help reduce the spread of COVID-19.
A FALL DETECTION SMART WATCH USING IOT AND DEEP LEARNINGIRJET Journal
The document describes a proposed fall detection smartwatch system using IoT and deep learning. It aims to enable smartwatches and algorithms to detect falls in smart homes. The proposed system, IMEFD-ODCNN, uses data collection, preprocessing, feature extraction using SqueezeNet, parameter tuning using SSO, and classification using SSOA-VAE. Video frames are preprocessed and features extracted before the SSOA-VAE classifier identifies falls. If a fall is detected, an alert is sent to the patient and caregiver for immediate assistance. The system aims to remotely monitor elderly people and help doctors treat patients by providing health data and history.
IoT Based Human Activity Recognition and Classification Using Machine LearningIRJET Journal
This document discusses a research paper on human activity recognition and classification using machine learning and IoT sensors. It begins with an abstract that outlines several methods for recognizing human activities, including sensors to detect orientation, motion, and position over time. The document then discusses the aim of the project to create an independent device for human activity recognition using IoT sensors to measure acceleration and gyroscopic position, with results predicted using MATLAB. It provides an overview of related work using various sensors and machine learning algorithms for activity recognition. The proposed system architecture is described using an Arduino board, ESP WiFi module, and ADXL334 accelerometer to collect and transmit sensor data for activity classification.
NEW CORONA VIRUS DISEASE 2022: SOCIAL DISTANCING IS AN EFFECTIVE MEASURE (COV...IRJET Journal
The document describes a proposed real-time system to monitor social distancing using computer vision and deep learning techniques. The system would use a camera to detect individuals and calculate distances between them in order to identify instances where social distancing guidelines are breached. When a breach is detected, an audio-visual cue would be emitted to alert individuals without identifying or saving personal data. The system aims to help reduce the spread of COVID-19 while respecting privacy and avoiding overreach. It outlines the technical approach including camera calibration, region of interest definition, object detection using YOLOv3, distance calculation techniques, and system architecture at a high level.
This document summarizes a research paper that proposes a hierarchical model using conditional random fields (CRF) to recognize alarming states in older patients. The model predicts if a patient has exited their bed or chair using data from a batteryless wireless sensor. It aims to provide timely alarms to caregivers to prevent falls. Specifically:
1. It develops a hierarchical CRF model that can learn relationships between alarm/no-alarm states, sensor data, and predicted activities to recognize alarms in real-time without relying on heuristics or multiple classification stages.
2. It evaluates the model using data from wireless accelerometer sensors collected from 14 healthy older adults and 26 hospitalized older patients.
3. The goal is to
This document discusses crowd density estimation using baseline filtering. It begins with an abstract describing the challenges of detecting and tracking objects in crowded scenes due to occlusions. It then reviews related works on component-based people detection, Bayesian tracking using shape models, and neural network-based people counting. The implementation section describes extracting foreground from background, computing crowd density as a function of foreground pixels, and estimating head counts to determine the total number of people. Screenshots show results of segmentation, preprocessing, tracking, and counting frames. It concludes that the proposed method estimates crowd density using movement, size, and height features with particle filtering and clustering.
BIOMETRIC AUTHORIZATION SYSTEM USING GAIT BIOMETRYIJCSEA Journal
ABSTRACT
Human gait, which is a new biometric aimed to recognize individuals by the way they walk have come to play an increasingly important role in visual surveillance applications. In this paper a novel hybrid holistic approach is proposed to show how behavioural walking characteristics can be used to recognize unauthorized and suspicious persons when they enter a surveillance area. Initially background is modelled from the input video captured from cameras deployed for security and the foreground moving object in the individual frames are segmented using the background subtraction algorithm. Then gait representing spatial, temporal and wavelet components are extracted and fused for training and testing multi class support vector machine models (SVM). The proposed system is evaluated using side view videos of NLPR database. The experimental results demonstrate that the proposed system achieves a pleasing recognition rate and also the results indicate that the classification ability of SVM with Radial Basis Function (RBF) is better than with other kernel functions.
Event Detection Using Background Subtraction For Surveillance SystemsIRJET Journal
The document describes a proposed system for detecting suspicious events using background subtraction for surveillance systems. The system first obtains foreground objects using background subtraction. The foreground objects are then classified as people or suspicious objects and tracked over time using blob matching. By analyzing the temporal and spatial properties of the tracked blobs, activities are classified as normal or suspicious, such as theft of objects. The system aims to more efficiently detect suspicious human behavior and objects for applications such as security and surveillance.
Daily Human Activity Recognition using Adaboost Classifiers on Wisdm Datasetijtsrd
Human activity recognition is an important area of machine learning research as it has much utilization in different areas such as sports training, security, entertainment, ambient assisted living, and health monitoring and management. Studying human activity recognition shows that researchers are interested mostly in the daily activities of the human. Nowadays mobile phone is well equipped with advanced processor, more memory, powerful battery and built in sensors. This provides an opportunity to open up new areas of data mining for activity recognition of human's daily living. In the paper, the benchmark dataset is considered for this work is acquired from the WISDM laboratory, which is available in public domain. We tested experiment using AdaBoost.M1 algorithm with Decision Stump, Hoeffding Tree, Random Tree, J48, Random Forest and REP Tree to classify six activities of daily life by using Weka tool. Then we also see the test output from weka experimenter for these six classifiers. We found the using Adaboost,M1 with Random Forest, J.48 and REP Tree improves overall accuracy. We showed that the difference in accuracy for Random Forest, REP Tree and J48 algorithms compared to Decision Stump, and Hoeffding Tree is statistically significant. We also show that the accuracy of these algorithms compared to Decision Stump, and Hoeffding Tree is high, so we can say that these two algorithms achieved a statistically significantly better result than the Decision Stump, and Hoeffding Tree and Random Tree baseline. Khin Khin Oo "Daily Human Activity Recognition using Adaboost Classifiers on Wisdm Dataset" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28073.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/28073/daily-human-activity-recognition-using-adaboost-classifiers-on-wisdm-dataset/khin-khin-oo
Disabled people can overcome their disabilities in carrying out daily tasks in many facilities [1]. However, they frequently report that they experience difficulty being independently mobile. And even if they can, they are likely to have some serious accidents such as falls. Furthermore, falls constitute the second leading cause of accidental or injury deaths after injuries of road traffic which call for efficient and practical/comfortable means to monitor physically disabled people in order to detect falls and react urgently. Computer vision (CV) is one of the computer sciences fields, and it is actively contributing in building smart applications by providing for image\video content “understanding.” One of the main tasks of CV is detection and recognition. Detection and recognition applications are various and used for different purposes. One of these purposes is to help of the physically disabled people who use a cane as a
mobility aid by detecting the fall. This paper surveys the most popular approaches that have been used in fall detection, the challenges related to developing fall detectors, the techniques that have been used with the Kinect in fall detection, best points of interest (joints) to be tracked and the well-known Kinect-Based Fall Datasets. Finally, recommendations and future works will be summarized.
A DEVICE FOR AUTOMATIC DETECTION OF ELDERLY FALLSIRJET Journal
The document presents a device for automatically detecting falls in elderly individuals. It uses an accelerometer to measure changes in acceleration along three axes and determine body position. When a fall is detected based on acceleration thresholds, the GPS receiver pinpoints the location and a GSM modem sends a text message notification. The system aims to promptly detect falls to reduce injuries and allow for timely medical assistance. It discusses related work on fall detection techniques using sensors like accelerometers and pose estimation. The proposed system design uses an ESP WiFi controller, GPS and GSM modules, accelerometer, and other components to detect falls, track location, and alert caregivers via SMS. It aims to help elderly individuals live independently safely.
Similar to Analysis of Fall Detection Systems: A Review (20)
‘Six Sigma Technique’ A Journey Through its Implementationijtsrd
The manufacturing industries all over the world are facing tough challenges for growth, development and sustainability in today’s competitive environment. They have to achieve apex position by adapting with the global competitive environment by delivering goods and services at low cost, prime quality and better price to increase wealth and consumer satisfaction. Cost Management ensures profit, growth and sustainability of the business with implementation of Continuous Improvement Technique like Six Sigma. This leads to optimize Business performance. The method drives for customer satisfaction, low variation, reduction in waste and cycle time resulting into a competitive advantage over other industries which did not implement it. The main objective of this paper ‘Six Sigma Technique A Journey Through Its Implementation’ is to conceptualize the effectiveness of Six Sigma Technique through the journey of its implementation. Aditi Sunilkumar Ghosalkar "‘Six Sigma Technique’: A Journey Through its Implementation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64546.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64546/‘six-sigma-technique’-a-journey-through-its-implementation/aditi-sunilkumar-ghosalkar
Edge Computing in Space Enhancing Data Processing and Communication for Space...ijtsrd
Edge computing, a paradigm that involves processing data closer to its source, has gained significant attention for its potential to revolutionize data processing and communication in space missions. With the increasing complexity and data volume generated by modern space missions, traditional centralized computing approaches face challenges related to latency, bandwidth, and security. Edge computing in space, involving on board processing and analysis of data, offers promising solutions to these challenges. This paper explores the concept of edge computing in space, its benefits, applications, and future prospects in enhancing space missions. Manish Verma "Edge Computing in Space: Enhancing Data Processing and Communication for Space Missions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64541.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/64541/edge-computing-in-space-enhancing-data-processing-and-communication-for-space-missions/manish-verma
Dynamics of Communal Politics in 21st Century India Challenges and Prospectsijtsrd
Communal politics in India has evolved through centuries, weaving a complex tapestry shaped by historical legacies, colonial influences, and contemporary socio political transformations. This research comprehensively examines the dynamics of communal politics in 21st century India, emphasizing its historical roots, socio political dynamics, economic implications, challenges, and prospects for mitigation. The historical perspective unravels the intricate interplay of religious identities and power dynamics from ancient civilizations to the impact of colonial rule, providing insights into the evolution of communalism. The socio political dynamics section delves into the contemporary manifestations, exploring the roles of identity politics, socio economic disparities, and globalization. The economic implications section highlights how communal politics intersects with economic issues, perpetuating disparities and influencing resource allocation. Challenges posed by communal politics are scrutinized, revealing multifaceted issues ranging from social fragmentation to threats against democratic values. The prospects for mitigation present a multifaceted approach, incorporating policy interventions, community engagement, and educational initiatives. The paper conducts a comparative analysis with international examples, identifying common patterns such as identity politics and economic disparities. It also examines unique challenges, emphasizing Indias diverse religious landscape, historical legacy, and secular framework. Lessons for effective strategies are drawn from international experiences, offering insights into inclusive policies, interfaith dialogue, media regulation, and global cooperation. By scrutinizing historical epochs, contemporary dynamics, economic implications, and international comparisons, this research provides a comprehensive understanding of communal politics in India. The proposed strategies for mitigation underscore the importance of a holistic approach to foster social harmony, inclusivity, and democratic values. Rose Hossain "Dynamics of Communal Politics in 21st Century India: Challenges and Prospects" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64528.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/history/64528/dynamics-of-communal-politics-in-21st-century-india-challenges-and-prospects/rose-hossain
Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in...ijtsrd
Background and Objective Telehealth has become a well known tool for the delivery of health care in Saudi Arabia, and the perspective and knowledge of healthcare providers are influential in the implementation, adoption and advancement of the method. This systematic review was conducted to examine the current literature base regarding telehealth and the related healthcare professional perspective and knowledge in the Kingdom of Saudi Arabia. Materials and Methods This systematic review was conducted by searching 7 databases including, MEDLINE, CINHAL, Web of Science, Scopus, PubMed, PsycINFO, and ProQuest Central. Studies on healthcare practitioners telehealth knowledge and perspectives published in English in Saudi Arabia from 2000 to 2023 were included. Boland directed this comprehensive review. The researchers examined each connected study using the AXIS tool, which evaluates cross sectional systematic reviews. Narrative synthesis was used to summarise and convey the data. Results Out of 1840 search results, 10 studies were included. Positive outlook and limited knowledge among providers were seen across trials. Healthcare professionals like telehealth for its ability to improve quality, access, and delivery, save time and money, and be successful. Age, gender, occupation, and work experience also affect health workers knowledge. In Saudi Arabia, healthcare professionals face inadequate expert assistance, patient privacy, internet connection concerns, lack of training courses, lack of telehealth understanding, and high costs while performing telemedicine. Conclusions Healthcare practitioners telehealth perceptions and knowledge were examined in this systematic study. Its collection of concerned experts different personal attitudes and expertise would help enhance telehealths implementation in Saudi Arabia, develop its healthcare delivery alternative, and eliminate frequent problems. Badriah Mousa I Mulayhi | Dr. Jomin George | Judy Jenkins "Assess Perspective and Knowledge of Healthcare Providers Towards Elehealth in Saudi Arabia: A Systematic Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64535.pdf Paper Url: https://www.ijtsrd.com/medicine/other/64535/assess-perspective-and-knowledge-of-healthcare-providers-towards-elehealth-in-saudi-arabia-a-systematic-review/badriah-mousa-i-mulayhi
The Impact of Digital Media on the Decentralization of Power and the Erosion ...ijtsrd
The impact of digital media on the distribution of power and the weakening of traditional gatekeepers has gained considerable attention in recent years. The adoption of digital technologies and the internet has resulted in declining influence and power for traditional gatekeepers such as publishing houses and news organizations. Simultaneously, digital media has facilitated the emergence of new voices and players in the media industry. Digital medias impact on power decentralization and gatekeeper erosion is visible in several ways. One significant aspect is the democratization of information, which enables anyone with an internet connection to publish and share content globally, leading to citizen journalism and bypassing traditional gatekeepers. Another aspect is the disruption of conventional media industry business models, as traditional organizations struggle to adjust to the decrease in advertising revenue and the rise of digital platforms. Alternative business models, such as subscription models and crowdfunding, have become more prevalent, leading to the emergence of new players. Overall, the impact of digital media on the distribution of power and the weakening of traditional gatekeepers has brought about significant changes in the media landscape and the way information is shared. Further research is required to fully comprehend the implications of these changes and their impact on society. Dr. Kusum Lata "The Impact of Digital Media on the Decentralization of Power and the Erosion of Traditional Gatekeepers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64544.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64544/the-impact-of-digital-media-on-the-decentralization-of-power-and-the-erosion-of-traditional-gatekeepers/dr-kusum-lata
Online Voices, Offline Impact Ambedkars Ideals and Socio Political Inclusion ...ijtsrd
This research investigates the nexus between online discussions on Dr. B.R. Ambedkars ideals and their impact on social inclusion among college students in Gurugram, Haryana. Surveying 240 students from 12 government colleges, findings indicate that 65 actively engage in online discussions, with 80 demonstrating moderate to high awareness of Ambedkars ideals. Statistically significant correlations reveal that higher online engagement correlates with increased awareness p 0.05 and perceived social inclusion. Variations across colleges and a notable effect of college type on perceived social inclusion highlight the influence of contextual factors. Furthermore, the intersectional analysis underscores nuanced differences based on gender, caste, and socio economic status. Dr. Kusum Lata "Online Voices, Offline Impact: Ambedkar's Ideals and Socio-Political Inclusion - A Study of Gurugram District" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64543.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/political-science/64543/online-voices-offline-impact-ambedkars-ideals-and-sociopolitical-inclusion--a-study-of-gurugram-district/dr-kusum-lata
Problems and Challenges of Agro Entreprenurship A Studyijtsrd
Noting calls for contextualizing Agro entrepreneurs problems and challenges of the agro entrepreneurs and for greater attention to the Role of entrepreneurs in agro entrepreneurship research, we conduct a systematic literature review of extent research in agriculture entrepreneurship to overcome the study objectives of complications of agro entrepreneurs through various factors, Development of agriculture products is a key factor for the overall economic growth of agro entrepreneurs Agro Entrepreneurs produces firsthand large scale employment, utilizes the labor and natural resources, This research outlines the problems of Weather and Soil Erosions, Market price fluctuation, stimulates labor cost problems, reduces concentration of Price volatility, Dependency on Intermediaries, induces Limited Bargaining Power, and Storage and Transportation Costs. This paper mainly devoted to highlight Problems and challenges faced for the sustainable of Agro Entrepreneurs in India. Vinay Prasad B "Problems and Challenges of Agro Entreprenurship - A Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64540.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64540/problems-and-challenges-of-agro-entreprenurship--a-study/vinay-prasad-b
Comparative Analysis of Total Corporate Disclosure of Selected IT Companies o...ijtsrd
Disclosure is a process through which a business enterprise communicates with external parties. A corporate disclosure is communication of financial and non financial information of the activities of a business enterprise to the interested entities. Corporate disclosure is done through publishing annual reports. So corporate disclosure through annual reports plays a vital role in the life of all the companies and provides valuable information to investors. The basic objectives of corporate disclosure is to give a true and fair view of companies to the parties related either directly or indirectly like owner, government, creditors, shareholders etc. in the companies act, provisions have been made about mandatory and voluntary disclosure. The IT sector in India is rapidly growing, the trend to invest in the IT sector is rising and employment opportunities in IT sectors are also increasing. Therefore the IT sector is expected to have fair, full and adequate disclosure of all information. Unfair and incomplete disclosure may adversely affect the entire economy. A research study on disclosure practices of IT companies could play an important role in this regard. Hence, the present research study has been done to study and review comparative analysis of total corporate disclosure of selected IT companies of India and to put forward overall findings and suggestions with a view to increase disclosure score of these companies. The researcher hopes that the present research study will be helpful to all selected Companies for improving level of corporate disclosure through annual reports as well as the government, creditors, investors, all business organizations and upcoming researcher for comparative analyses of level of corporate disclosure with special reference to selected IT companies. Dr. Vaibhavi D. Thaker "Comparative Analysis of Total Corporate Disclosure of Selected IT Companies of India" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64539.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/64539/comparative-analysis-of-total-corporate-disclosure-of-selected-it-companies-of-india/dr-vaibhavi-d-thaker
The Impact of Educational Background and Professional Training on Human Right...ijtsrd
This study investigated the impact of educational background and professional training on human rights awareness among secondary school teachers in the Marathwada region of Maharashtra, India. The key findings reveal that higher levels of education, particularly a master’s degree, and fields of study related to education, humanities, or social sciences are associated with greater human rights awareness among teachers. Additionally, both pre service teacher training and in service professional development programs focused on human rights education significantly enhance teacher’s knowledge, skills, and competencies in promoting human rights principles in their classrooms. Baig Ameer Bee Mirza Abdul Aziz | Dr. Syed Azaz Ali Amjad Ali "The Impact of Educational Background and Professional Training on Human Rights Awareness among Secondary School Teachers" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64529.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64529/the-impact-of-educational-background-and-professional-training-on-human-rights-awareness-among-secondary-school-teachers/baig-ameer-bee-mirza-abdul-aziz
A Study on the Effective Teaching Learning Process in English Curriculum at t...ijtsrd
“One Language sets you in a corridor for life. Two languages open every door along the way” Frank Smith English as a foreign language or as a second language has been ruling in India since the period of Lord Macaulay. But the question is how much we teach or learn English properly in our culture. Is there any scope to use English as a language rather than a subject How much we learn or teach English without any interference of mother language specially in the classroom teaching learning scenario in West Bengal By considering all these issues the researcher has attempted in this article to focus on the effective teaching learning process comparing to other traditional strategies in the field of English curriculum at the secondary level to investigate whether they fulfill the present teaching learning requirements or not by examining the validity of the present curriculum of English. The purpose of this study is to focus on the effectiveness of the systematic, scientific, sequential and logical transaction of the course between the teachers and the learners in the perspective of the 5Es programme that is engage, explore, explain, extend and evaluate. Sanchali Mondal | Santinath Sarkar "A Study on the Effective Teaching Learning Process in English Curriculum at the Secondary Level of West Bengal" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd62412.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/62412/a-study-on-the-effective-teaching-learning-process-in-english-curriculum-at-the-secondary-level-of-west-bengal/sanchali-mondal
The Role of Mentoring and Its Influence on the Effectiveness of the Teaching ...ijtsrd
This paper reports on a study which was conducted to investigate the role of mentoring and its influence on the effectiveness of the teaching of Physics in secondary schools in the South West Region of Cameroon. The study adopted the convergent parallel mixed methods design, focusing on respondents in secondary schools in the South West Region of Cameroon. Both quantitative and qualitative data were collected, analysed separately, and the results were compared to see if the findings confirm or disconfirm each other. The quantitative analysis found that majority of the respondents 72 of Physics teachers affirmed that they had more experienced colleagues as mentors to help build their confidence, improve their teaching, and help them improve their effectiveness and efficiency in guiding learners’ achievements. Only 28 of the respondents disagreed with these statements. With majority respondents 72 agreeing with the statements, it implies that in most secondary schools, experienced Physics teachers act as mentors to build teachers’ confidence in teaching and improving students’ learning. The interview qualitative data analysis summarized how secondary school Principals use meetings with mentors and mentees to promote mentorship in the school milieu. This has helped strengthen teachers’ classroom practices in secondary schools in the South West Region of Cameroon. With the results confirming each other, the study recommends that mentoring should focus on helping teachers employ social interactions and instructional practices feedback and clarity in teaching that have direct measurable impact on students’ learning achievements. Andrew Ngeim Sumba | Frederick Ebot Ashu | Peter Agborbechem Tambi "The Role of Mentoring and Its Influence on the Effectiveness of the Teaching of Physics in Secondary Schools in the South West Region of Cameroon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64524.pdf Paper Url: https://www.ijtsrd.com/management/management-development/64524/the-role-of-mentoring-and-its-influence-on-the-effectiveness-of-the-teaching-of-physics-in-secondary-schools-in-the-south-west-region-of-cameroon/andrew-ngeim-sumba
Design Simulation and Hardware Construction of an Arduino Microcontroller Bas...ijtsrd
This study primarily focuses on the design of a high side buck converter using an Arduino microcontroller. The converter is specifically intended for use in DC DC applications, particularly in standalone solar PV systems where the PV output voltage exceeds the load or battery voltage. To evaluate the performance of the converter, simulation experiments are conducted using Proteus Software. These simulations provide insights into the input and output voltages, currents, powers, and efficiency under different state of charge SoC conditions of a 12V,70Ah rechargeable lead acid battery. Additionally, the hardware design of the converter is implemented, and practical data is collected through operation, monitoring, and recording. By comparing the simulation results with the practical results, the efficiency and performance of the designed converter are assessed. The findings indicate that while the buck converter is suitable for practical use in standalone PV systems, its efficiency is compromised due to a lower output current. Chan Myae Aung | Dr. Ei Mon "Design Simulation and Hardware Construction of an Arduino-Microcontroller Based DC-DC High-Side Buck Converter for Standalone PV System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64518.pdf Paper Url: https://www.ijtsrd.com/engineering/mechanical-engineering/64518/design-simulation-and-hardware-construction-of-an-arduinomicrocontroller-based-dcdc-highside-buck-converter-for-standalone-pv-system/chan-myae-aung
Sustainable Energy by Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadikuijtsrd
Energy becomes sustainable if it meets the needs of the present without compromising the ability of future generations to meet their own needs. Some of the definitions of sustainable energy include the considerations of environmental aspects such as greenhouse gas emissions, social, and economic aspects such as energy poverty. Generally far more sustainable than fossil fuel are renewable energy sources such as wind, hydroelectric power, solar, and geothermal energy sources. Worthy of note is that some renewable energy projects, like the clearing of forests to produce biofuels, can cause severe environmental damage. The sustainability of nuclear power which is a low carbon source is highly debated because of concerns about radioactive waste, nuclear proliferation, and accidents. The switching from coal to natural gas has environmental benefits, including a lower climate impact, but could lead to delay in switching to more sustainable options. “Carbon capture and storage” can be built into power plants to remove the carbon dioxide CO2 emissions, but this technology is expensive and has rarely been implemented. Leading non renewable energy sources around the world is fossil fuels, coal, petroleum, and natural gas. Nuclear energy is usually considered another non renewable energy source, although nuclear energy itself is a renewable energy source, but the material used in nuclear power plants is not. The paper addresses the issue of sustainable energy, its attendant benefits to the future generation, and humanity in general. Paul A. Adekunte | Matthew N. O. Sadiku | Janet O. Sadiku "Sustainable Energy" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64534.pdf Paper Url: https://www.ijtsrd.com/engineering/electrical-engineering/64534/sustainable-energy/paul-a-adekunte
Concepts for Sudan Survey Act Implementations Executive Regulations and Stand...ijtsrd
This paper aims to outline the executive regulations, survey standards, and specifications required for the implementation of the Sudan Survey Act, and for regulating and organizing all surveying work activities in Sudan. The act has been discussed for more than 5 years. The Land Survey Act was initiated by the Sudan Survey Authority and all official legislations were headed by the Sudan Ministry of Justice till it was issued in 2022. The paper presents conceptual guidelines to be used for the Survey Act implementation and to regulate the survey work practice, standardizing the field surveys, processing, quality control, procedures, and the processes related to survey work carried out by the stakeholders and relevant authorities in Sudan. The conceptual guidelines are meant to improve the quality and harmonization of geospatial data and to aid decision making processes as well as geospatial information systems. The established comprehensive executive regulations will govern and regulate the implementation of the Sudan Survey Geomatics Act in all surveying and mapping practices undertaken by the Sudan Survey Authority SSA and state local survey departments for public or private sector organizations. The targeted standards and specifications include the reference frame, projection, coordinate systems, and the guidelines and specifications that must be followed in the field of survey work, processes, and mapping products. In the last few decades, there has been a growing awareness of the importance of geomatics activities and measurements on the Earths surface in space and time, together with observing and mapping the changes. In such cases, data must be captured promptly, standardized, and obtained with more accuracy and specified in much detail. The paper will also highlight the current situation in Sudan, the degree to which survey standards are used, the problems encountered, and the errors that arise from not using the standards and survey specifications. Kamal A. A. Sami "Concepts for Sudan Survey Act Implementations - Executive Regulations and Standards" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63484.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63484/concepts-for-sudan-survey-act-implementations--executive-regulations-and-standards/kamal-a-a-sami
Towards the Implementation of the Sudan Interpolated Geoid Model Khartoum Sta...ijtsrd
The discussions between ellipsoid and geoid have invoked many researchers during the recent decades, especially during the GNSS technology era, which had witnessed a great deal of development but still geoid undulation requires more investigations. To figure out a solution for Sudans local geoid, this research has tried to intake the possibility of determining the geoid model by following two approaches, gravimetric and geometrical geoid model determination, by making use of GNSS leveling benchmarks at Khartoum state. The Benchmarks are well distributed in the study area, in which, the horizontal coordinates and the height above the ellipsoid have been observed by GNSS while orthometric heights were carried out using precise leveling. The Global Geopotential Model GGM represented in EGM2008 has been exploited to figure out the geoid undulation at the benchmarks in the study area. This is followed by a fitting process, that has been done to suit the geoid undulation data which has been computed using GNSS leveling data and geoid undulation inspired by the EGM2008. Two geoid surfaces were created after the fitting process to ensure that they are identical and both of them could be counted for getting the same geoid undulation with an acceptable accuracy. In this respect, statistical operation played an important role in ensuring the consistency and integrity of the model by applying cross validation techniques splitting the data into training and testing datasets for building the geoid model and testing its eligibility. The geometrical solution for geoid undulation computation has been utilized by applying straightforward equations that facilitate the calculation of the geoid undulation directly through applying statistical techniques for the GNSS leveling data of the study area to get the common equation parameters values that could be utilized to calculate geoid undulation of any position in the study area within the claimed accuracy. Both systems were checked and proved eligible to be used within the study area with acceptable accuracy which may contribute to solving the geoid undulation problem in the Khartoum area, and be further generalized to determine the geoid model over the entire country, and this could be considered in the future, for regional and continental geoid model. Ahmed M. A. Mohammed. | Kamal A. A. Sami "Towards the Implementation of the Sudan Interpolated Geoid Model (Khartoum State Case Study)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63483.pdf Paper Url: https://www.ijtsrd.com/engineering/civil-engineering/63483/towards-the-implementation-of-the-sudan-interpolated-geoid-model-khartoum-state-case-study/ahmed-m-a-mohammed
Activating Geospatial Information for Sudans Sustainable Investment Mapijtsrd
Sudan is witnessing an acceleration in the processes of development and transformation in the performance of government institutions to raise the productivity and investment efficiency of the government sector. The development plans and investment opportunities have focused on achieving national goals in various sectors. This paper aims to illuminate the path to the future and provide geospatial data and information to develop the investment climate and environment for all sized businesses, and to bridge the development gap between the Sudan states. The Sudan Survey Authority SSA is the main advisor to the Sudan Government in conducting surveying, mappings, designing, and developing systems related to geospatial data and information. In recent years, SSA made a strategic partnership with the Ministry of Investment to activate Geospatial Information for Sudans Sustainable Investment and in particular, for the preparation and implementation of the Sudan investment map, based on the directives and objectives of the Ministry of Investment MI in Sudan. This paper comes within the framework of activating the efforts of the Ministry of Investment to develop technical investment services by applying techniques adopted by the Ministry and its strategic partners for advancing investment processes in the country. Kamal A. A. Sami "Activating Geospatial Information for Sudan's Sustainable Investment Map" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63482.pdf Paper Url: https://www.ijtsrd.com/engineering/information-technology/63482/activating-geospatial-information-for-sudans-sustainable-investment-map/kamal-a-a-sami
Educational Unity Embracing Diversity for a Stronger Societyijtsrd
In a rapidly changing global landscape, the importance of education as a unifying force cannot be overstated. This paper explores the crucial role of educational unity in fostering a stronger and more inclusive society through the embrace of diversity. By examining the benefits of diverse learning environments, the paper aims to highlight the positive impact on societal strength. The discussion encompasses various dimensions, from curriculum design to classroom dynamics, and emphasizes the need for educational institutions to become catalysts for unity in diversity. It highlights the need for a paradigm shift in educational policies, curricula, and pedagogical approaches to ensure that they are reflective of the diverse fabric of society. This paper also addresses the challenges associated with implementing inclusive educational practices and offers practical strategies for overcoming barriers. It advocates for collaborative efforts between educational institutions, policymakers, and communities to create a supportive ecosystem that promotes diversity and unity. Mr. Amit Adhikari | Madhumita Teli | Gopal Adhikari "Educational Unity: Embracing Diversity for a Stronger Society" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64525.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/64525/educational-unity-embracing-diversity-for-a-stronger-society/mr-amit-adhikari
Integration of Indian Indigenous Knowledge System in Management Prospects and...ijtsrd
The diversity of indigenous knowledge systems in India is vast and can vary significantly between different communities and regions. Preserving and respecting these knowledge systems is crucial for maintaining cultural heritage, promoting sustainable practices, and fostering cross cultural understanding. In this paper, an overview of the prospects and challenges associated with incorporating Indian indigenous knowledge into management is explored. It is found that IIKS helps in management in many areas like sustainable development, tourism, food security, natural resource management, cultural preservation and innovation, etc. However, IIKS integration with management faces some challenges in the form of a lack of documentation, cultural sensitivity, language barriers legal framework, etc. Savita Lathwal "Integration of Indian Indigenous Knowledge System in Management: Prospects and Challenges" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63500.pdf Paper Url: https://www.ijtsrd.com/management/accounting-and-finance/63500/integration-of-indian-indigenous-knowledge-system-in-management-prospects-and-challenges/savita-lathwal
DeepMask Transforming Face Mask Identification for Better Pandemic Control in...ijtsrd
The COVID 19 pandemic has highlighted the crucial need of preventive measures, with widespread use of face masks being a key method for slowing the viruss spread. This research investigates face mask identification using deep learning as a technological solution to be reducing the risk of coronavirus transmission. The proposed method uses state of the art convolutional neural networks CNNs and transfer learning to automatically recognize persons who are not wearing masks in a variety of circumstances. We discuss how this strategy improves public health and safety by providing an efficient manner of enforcing mask wearing standards. The report also discusses the obstacles, ethical concerns, and prospective applications of face mask detection systems in the ongoing fight against the pandemic. Dilip Kumar Sharma | Aaditya Yadav "DeepMask: Transforming Face Mask Identification for Better Pandemic Control in the COVID-19 Era" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd64522.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/64522/deepmask-transforming-face-mask-identification-for-better-pandemic-control-in-the-covid19-era/dilip-kumar-sharma
Streamlining Data Collection eCRF Design and Machine Learningijtsrd
Efficient and accurate data collection is paramount in clinical trials, and the design of Electronic Case Report Forms eCRFs plays a pivotal role in streamlining this process. This paper explores the integration of machine learning techniques in the design and implementation of eCRFs to enhance data collection efficiency. We delve into the synergies between eCRF design principles and machine learning algorithms, aiming to optimize data quality, reduce errors, and expedite the overall data collection process. The application of machine learning in eCRF design brings forth innovative approaches to data validation, anomaly detection, and real time adaptability. This paper discusses the benefits, challenges, and future prospects of leveraging machine learning in eCRF design for streamlined and advanced data collection in clinical trials. Dhanalakshmi D | Vijaya Lakshmi Kannareddy "Streamlining Data Collection: eCRF Design and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd63515.pdf Paper Url: https://www.ijtsrd.com/biological-science/biotechnology/63515/streamlining-data-collection-ecrf-design-and-machine-learning/dhanalakshmi-d
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
2. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD29467 | Volume – 4 | Issue – 1 | November-December 2019 Page 104
Ambient-based fall detection systems are based on using
proximity and floor sensor to collect the data oftheactivities
of daily living. This data is used for the fall detection. The
research work presented in [24] uses numerous sensors
installed to collect human data when a person gets close to
them. What's more, ambience based devices endeavor to
combine sound and visual information and identify the
occasion through vibration information [3]. Ambient based
approaches are the simplest techniques for detecting a fall
event as it does not use any wearable device and just use
motion, light and vibration sensors. Figure.1 depicts how
ambient based solution is used for fall detection.
Figure 2: An example of the usage of ambient sensors
in monitoring activity patterns
A. Audio and Visual
Ambient based models tend to be based on combined
determination associated with audio visual signals along
with some other specific information including floor
vibrational data or even microphone signals through the
channel of environmental sensors. Toreyin et al.[6] utilized
audio and video data in order to detect fall of a person and
attempt to separate falling from walking and sitting down
using waveletprocessingandHiddenMarkovmodels(HMM).
In another research work, Toreyin et al. [7] used a HMM
model to detect a fall using audio data and passive infrared
(PIR) sensors Event sensing using vibrational data.
B. Vibration Sensing
The detection associated with activities as well as utilizing
vibrational data can be essential in any way, for instance,
monitoring, tracking and localization etc. [3]. Alwan and
Majd et al. [4] focus on a floor vibration based fall detection
system. The daily activities of peoples can produce the floor
vibration. The system uses the vibration patternsofthefloor
and matches vibration pattern technique to detect fall
events. Yazar et al. [5] used PIR and vibration sensors and
deployed winner-takes-all (WTA) decision algorithm to
distinguish fall from the normal activities of daily living..
Alwan and Majid further revealed that these ambient based
solution leads to a high rate of false alarm, limited accuracy
and the high cost of installation [4].
III. VISION-BASED APPROACHES
The vision-based approach uses single or multiple cameras
in an indoor environment to track a person’s movements
and the body shape during the whole falling period [8] [9]
[10]. Anh Nguyen et al. [10] proposed a single camera based
fall detection system, and the system works on the tracking
of the motion characteristics and the body shape during the
whole falling period, not at a certain point in time. Zhen-
Peng Bian et al. [9] proposed single depth camera based fall
detection system. This systemisindependentofillumination
of lights, and the system can also work in the dark room. Yu
et al. [11] proposed a fall detection system using vision-
based technique by applying backgroundsubtractiontotake
out the frontal area human body; and the data is imported
into a directed a cyclic graph supporting vector machine
(SVM) for classifying different human poses. Different
methodologies as to image examining have been proposed
including spatiotemporal features and 3D head position
analysis [3].
Figure 3: Camera based fall detection system example
A. Spatio-temporal Shape modellingusingspatio-temporal
features gives human activities important data which are
used to detect different events. Foroughi et al.[12]proposed
a method to fall detection by merging the eigen space
approach and integrated time motionimages(ITMI).Timeof
motion event and Motion information that are contained in
spatiotemporal database can be described as ITMI. Feature
reduction is applied using the Eigenspacetechnique,and the
neural network classifier which isusedforclassifyingthe fall
events.
B. 3D head position analysis Head position analysis relies
on the head monitoring that controls the event of large
motion inside the video sequence. Differentstatemodelsare
utilized to monitor the head based on the magnitude of the
movement information [3]. Auvinet et al. [8] mentioned a
several approach to the technique in [13] by means of
Occlusion-resistant algorithm and Vertical volume
distribution ratio (VVDR). Hence, they proposed fall
detection system on the basis of multiple cameras fuses
reconstructed 3D shape ofthepersonand,theyhavereached
sensitivity of 99.7% and specificity or in other way four or
more cameras works better. The drawback of the multi-
camera system is must be adjusted,andvideosequencefrom
different camera must also be synchronized. This procedure
makes the implementation of a system to be more
complicated and costly.
The main advantage of the vision-based approach is that the
person does not suppose to wear any extra device for the
falling detection. Nonetheless,theoperationofthisapproach
is restricted to those spots where the sensors have been
beforehand deployed [14].
IV. WEARABLE DEVICE BASED APPROACHES
The wearable device based methods requires thesubjectsto
be dress in some devices or garments with embedded
sensors such as magnetometer, gyroscopes, and
accelerometers to track theuser’smotionofbodyorposture,
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the data collected by the inertial sensors are used as motion
signals to analyze the state of movement.
Accelerometer
An accelerometer is a device that used to measure the
acceleration, changes in position and velocity. It is the most
widely utilized techniques applied for determining physical
activities in order to observe activity patterns.
Figure 4: Three axis accelerometer (a) and gyroscope
(b) in a smartphone device (source: Apple Inc.)
Gyroscope
A gyroscope is a device that used to measure change in
orientation and rotational velocity.
Magnetometer
A magnetometer is a device which measures a magnetic
field. Either the sensor attached to the body or embedded in
smart phone generates the inertial sensor data.
A. Wearable Device attached to the body
The sensor attached to different part oftheindividuals’body
to collect the data during the fall. Huynh et al. [21] [35] used
wireless sensor system (WSS) based on accelerometer and
gyroscope, and the senor is attached to the human body at
the center of the chest to collect real-time fall data. Lai et al.
[15] integrated different sensor devices such as, tri-axial
acceleration for joint sensing of injured body parts when an
accidental fall take place. The model transmits the data
encouraged by the sensors which are dispersed over
different body parts.
B. Wearable Device Built-in Smartphone
According to [16], today’s Smart phones accompany a rich
set of embedded sensors, namely an accelerometer, digital
compass, gyroscope, GPS, microphone, and camera. Todays,
many researchers are using the advantage of this fact to
develop Smartphone-basedfall detectors.Forinstance,Baiet
al. [17] illustrated Smartphone with GPS function which is
based on 3-axis accelerometer sensor to detect falls.Andòet
al. [18] developed a Smartphone based ADL andfall detector
system by using accelerometer sensor. Rakhman et al. [19]
used accelerometer and gyroscope sensors integrated into
an Android-based smartphone and evaluated some
threshold based algorithms and sensor data to determine a
fall.
V. CLASSIFICATION ALGORITHM BASED ON
WEARABLE APPROACH
The classification algorithm is applied to classifyactivitiesof
daily living (ADL) and several fall events. According to our
literature review, a wearable based fall detection algorithm
can be categorized into two approaches namely Threshold
based [19]-[22] and machine learning based [23]-[27]. A.
Threshold Based Threshold-based approaches use single or
multiple threshold values to classify events. The system
compares real time sensor data with the given threshold
values, and if it exceeds, the system notifies the occurrence
of a fall. For instance, Bourke et al. [22]proposeda threshold
based fall detection algorithm using a bi-axial gyroscope
sensor and, they have identified three threshold values.
Rakhman et al. [19] used accelerometer and gyroscope
sensors integrated into an Android-based smartphone and
evaluated some threshold based algorithms and sensordata
to determine a fall. Guo et al. [20] and Huynh et al. [21] [35]
used a wearable device with built-in tri-axial accelerometer
and gyroscope for fall detection, and they have utilized a
threshold based algorithm. This algorithm has three
threshold values: lower acceleration, upper acceleration,
lower angular velocity in order to check whether the person
is fallen or not. B. Machine Learning Based In Machine
learning based approach, different types of falls and ADL
patterns are trained by a learning algorithm and then
classified the event by evaluation algorithm[23]-[27]. The
machine learning algorithm includes Hidden Markov Model
(HMM) [23,24], Support vector machine (SVM) [25],
Decision Tree [26]. Tong et al. [23] proposed a low-cost fall
detection and preventionsystem byusingHMMandtri-axial,
and the results of experiment indicated that falls could be
expected 200–400 ms earlier the accident, and could alsobe
accurately identified from other regular activities. Cao et al.
[24] proposed fall detection system by using acceleration
data and Hidden Markov model (HMM), and the data
collected by tri-axial accelerometerintegratedona wearable
device. Aguiar et al. [26] proposed a Smartphone based
detection system by using accelerometer sensor embedded
on the device, and they have checkedthree machinelearning
algorithms such as Decision tree, k-nearest neighbour (K-
NN), and Naive Bayes, but among those algorithms,Decision
Tree has appeared good performance. Pierleoni et al. [25]
proposed support vector machines (SVM) based fall
detection system by using accelerometerandMagnetometer
sensors. In recent years, numerous papers were published
that discuss different aspects of the fall detectiontechniques
based on the combination of threshold-based and machine
learning based algorithms. Lim et al. [28] applied the
combination of simple threshold and HMM algorithm and
using 3-axis acceleration, the combination of simple
threshold and HMM have decreased the complexity of
hardware. Yodpijit et al. [27] used accelerometer and
gyroscope motion sensors to detect the fall, and focused on
threshold-based and ANN algorithm to distinguish between
ADL and falls in order to minimize the number of false
positive outcomes.
VI. DATA FUSION APPROACH
Andò et al. [29] presented multi-sensor data fusion
approach, which fuses data from a gyroscope and an
accelerometer; and they have worked on smart algorithms
for the activities of daily living (ADL) and fall classification,
which utilize the data provided by inertial sensors
embedded in a mobile phone, and installed on the user
device. This algorithm uses a threshold based method
applied to the features extracted from the average of the
magnitude of the three acceleration and angular velocities
components.Thesystemautomaticallysendsthenotification
to caregivers as soon as the fall event detected. Wang et al.
[30] presented multi-sensor data fusion approach for fall
prediction of the older peoples by using the walking
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assistant robot, which uses acceleration, gyroscope and
tactile-slip sensors to acquire the elder’s falling data and
extracts its features, and they used BP neural network
algorithm for fall prediction. In the following tables I and II,
we present a summary of different works with
corresponding researchers. This summary depicts thetypes
of sensors used, the methodology applied and final outcome
of each work.
VII. OPEN CHALLENGES
In the previous sections, the paper provides a detailed
survey on various fall detection techniques. Each technique
tends to offer certain benefit for fall detection. There are
following ongoing challenges with regard to various fall
detection techniques covered in this survey: A. Challenges
with vision-based fall-detectionVision-basedfall detectionis
using rich set of features extracted from the sequence of
frames captured from the video data. To be able to exploit
the full potential of vision based solution for early fall
detection, we believe the real challenge still lies in terms of
cost of GPU based processing at the local node and use of
improved ANN algorithm for fall predication and
anticipation on real-time data. B. Challenges with ambient
based approaches Though ambient based solutions are the
simplest compared to other techniques, as it makes use of
vibration sensor to detect the fall. However, as reported by
previous research work, ambient-based solutions lead to
increased false alarms thus limited in accuracy. But,
ambient-based approaches are quite appropriate for
Ambient-Assisted Living application wherein data can be
continuously collected and stored on cloud for further
analytics. Real challenge is to preserve the privacydata ofan
old age person who may be reluctant to share the data of
daily activity living. C. Wearable based ongoing research
challenges. The recent advances in the wearable space is
attracting many researchers to address range of problems.
Most of these problems are related to our activities. There
activity recognition using wearable is drawing lot of
attention. Wearable devices use embedded sensors to
recognize the activities It is used to measure changes in
orientation, position and velocity in order to detect the
physical activity so that can identify the fall event. Previous
research work suggeststhatanaccelerometerandgyroscope
data can be collected and processed for fall detection. It’s
worth that gyroscope has a practical problem of drift
therefore further research work should be focused on
minimizing the drift issues while using gyroscope data
independently or in fusion with accelerometer.
Table I: Comparison of Different Fall Detection Systems
Table II: Various Categories Of Fall Detection Techniques And Comparison
VIII. CONCLUSION
To sum up, fall detection is an interestingproblemwhich has
been discussed widely but still requires further attention.
The fall detection system intended to anticipate a fall event
by analyzing the data of daily activity living. This paper
reviews various research studies being conductedonthefall
detection systems for elderly people.Mostlystudiesfocus on
the identification of elderly falls from their normal activities.
There are three majors fall detection approaches such as
wearable, vision and ambient based to classify fall events.
Among the major approaches of fall detection system
wearable based system is rapidly increasing. Recently a
number of studies prefer Smart phone based method which
uses built in sensors for detecting falls.
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