Elder citizens face sudden fall, which can lead to injuries of both destructive and non-virulent. These sudden falls are later more precarious than diseases like heart attack, blood sugar, blood pressure because these can be untreated for a lengthy time which can lead to death. Elder citizen who experiences a precipitous fall, carry out their communal life narrowed. Therefore, a shrewd and adequate anti-fallen system is required for aiding elderly health care, specifically to those who live individually. So, it can identify and anticipate a precipitous fall through appropriate human activity recognition. In this study, we have suggested an end-edge-cloud based wearable EdgeFall architecture for elderly care. We have performed simulation setups to clarify the query of why we need such a strategy, and its validity. We have achieved maximum 91.87% accuracy with 1.6% false alarm rate (FAR). These empirical results indicate the superiority of using tightly couple multiple information for recognizing human activity. We can accomplish a low FAR with an enhanced accuracy. We can observe that our proposed end-edge-cloud based architecture can reduce the execution time to millisecond range (ms) of 14.16 to 15.74. This work serves as the starting mark for future related research activities.
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.
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.
Internet of things based fall detection and heart rate monitoring system for...IJECEIAES
Falls cause the maximum number of injuries, deaths, and hospitalizations due to injury for senior citizens worldwide. So, fall detection is essential in the health care of senior citizens. Present methods lack either accuracy or comfortability. The design of fall detection and heart rate monitoring system for senior citizens has been presented in this paper. The hardware interface includes wearable monitoring devices based on a tri-axial accelerometer and Bluetooth module that makes a wireless connection by software interface (mobile application) to the caregiver. Global positioning system (GPS) can also track the location of the elder. For detecting falls accurately, an effective fall detection algorithm is developed and used. The performance parameters of the fall detection system are accuracy (97.6%), sensitivity (92.8%), and specificity (100%). A pulse sensor is used for monitoring the heart rate of the elder. The device is put on the hips to increase comfortability. Whenever the elder's fall is detected, the device can send information on fall data and heart rate with location to the respective caregiver successfully. So, this device can minimize the injury and health cost of a fallen person as a victim can get help within a short time.
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.
IRJET- Review on: A Wireless IoT System for Gait Detection in Stroke PatientIRJET Journal
This document summarizes a proposed wireless IoT system for gait detection in stroke patients. The system would use sensors embedded in a smart shoe to discreetly monitor a patient's insole pressure and acceleration during walking. The data collected from the shoe sensors and a smartphone's built-in sensors would be used to detect any abnormal or cautious gait patterns that could predict risk of falling. The system aims to warn patients about risky gaits and potentially prevent injuries. It discusses how IoT and wireless communication could help create a portable system to continuously monitor patients' gaits outside of a clinical setting.
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.
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.
Accelerometer-based elderly fall detection system using edge artificial inte...IJECEIAES
Falls have long been one of the most serious threats to elderly people's health. Detecting falls in real-time can reduce the time the elderly remains on the floor after a fall, hence avoiding fall-related medical conditions. Recently, the fall detection problem has been extensively researched. However, the fall detection systems that use a traditional internet of things (IoT) architecture have some limitations such as latency, high power consumption, and poor performance in areas with unstable internet. This paper intends to show the efficacy of detecting falls in a resourceconstrained microcontroller at the edge of the network using a wearable accelerometer. Since the hardware resources of microcontrollers are limited, a lightweight fall detection deep learning model was developed to be deployed on a microcontroller with only a few kilobytes of memory. The microcontroller was installed in a low-power wide-area network based on long range (LoRa) communication technology. Through comparative testing of different lightweight neural networks and traditional machine learning algorithms, the convolutional neural network (CNN) has been shown to be the most suited, with 95.55% accuracy. The CNN model reached inference times lower than 37.84 ms with 61.084 kilobytes storage requirements, which implies the capability to detect fall event in real-time in low-power microcontrollers.
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.
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.
Internet of things based fall detection and heart rate monitoring system for...IJECEIAES
Falls cause the maximum number of injuries, deaths, and hospitalizations due to injury for senior citizens worldwide. So, fall detection is essential in the health care of senior citizens. Present methods lack either accuracy or comfortability. The design of fall detection and heart rate monitoring system for senior citizens has been presented in this paper. The hardware interface includes wearable monitoring devices based on a tri-axial accelerometer and Bluetooth module that makes a wireless connection by software interface (mobile application) to the caregiver. Global positioning system (GPS) can also track the location of the elder. For detecting falls accurately, an effective fall detection algorithm is developed and used. The performance parameters of the fall detection system are accuracy (97.6%), sensitivity (92.8%), and specificity (100%). A pulse sensor is used for monitoring the heart rate of the elder. The device is put on the hips to increase comfortability. Whenever the elder's fall is detected, the device can send information on fall data and heart rate with location to the respective caregiver successfully. So, this device can minimize the injury and health cost of a fallen person as a victim can get help within a short time.
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.
IRJET- Review on: A Wireless IoT System for Gait Detection in Stroke PatientIRJET Journal
This document summarizes a proposed wireless IoT system for gait detection in stroke patients. The system would use sensors embedded in a smart shoe to discreetly monitor a patient's insole pressure and acceleration during walking. The data collected from the shoe sensors and a smartphone's built-in sensors would be used to detect any abnormal or cautious gait patterns that could predict risk of falling. The system aims to warn patients about risky gaits and potentially prevent injuries. It discusses how IoT and wireless communication could help create a portable system to continuously monitor patients' gaits outside of a clinical setting.
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.
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.
Accelerometer-based elderly fall detection system using edge artificial inte...IJECEIAES
Falls have long been one of the most serious threats to elderly people's health. Detecting falls in real-time can reduce the time the elderly remains on the floor after a fall, hence avoiding fall-related medical conditions. Recently, the fall detection problem has been extensively researched. However, the fall detection systems that use a traditional internet of things (IoT) architecture have some limitations such as latency, high power consumption, and poor performance in areas with unstable internet. This paper intends to show the efficacy of detecting falls in a resourceconstrained microcontroller at the edge of the network using a wearable accelerometer. Since the hardware resources of microcontrollers are limited, a lightweight fall detection deep learning model was developed to be deployed on a microcontroller with only a few kilobytes of memory. The microcontroller was installed in a low-power wide-area network based on long range (LoRa) communication technology. Through comparative testing of different lightweight neural networks and traditional machine learning algorithms, the convolutional neural network (CNN) has been shown to be the most suited, with 95.55% accuracy. The CNN model reached inference times lower than 37.84 ms with 61.084 kilobytes storage requirements, which implies the capability to detect fall event in real-time in low-power microcontrollers.
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.
IRJET- IoT based Smart Sensing Wheelchair to Assist in HealthcareIRJET Journal
The document proposes an IoT-based smart wheelchair that monitors a patient's heartbeat on a regular basis and notifies concerned individuals via message. Sensors would be integrated into the wheelchair seat and backrest to detect cardiovascular abnormalities. An Arduino board would connect to an IoT cloud platform via an edge device to transmit sensor data and receive commands. The system aims to provide independent mobility and remote health monitoring for patients who have difficulty operating a manual wheelchair.
Mini project PowerPoint presentation usefulg8248418302
The document describes a design project for a wearable fall detection device. A group of three students - Devika S, Geetharakchana R, and Janapriya E - presented their mini project on designing a wearable device that can detect falls using an MPU6050 sensor. The device would send an SMS notification to a mobile phone when a fall is detected to help reduce the response time for assistance. The document provides details on the objectives, problem identification, hardware requirements including the NodeMCU ESP8266 microcontroller and MPU6050 sensor, software requirements, and conclusions from exploring the effectiveness of using the MPU6050 sensor to detect falls.
IRJET- Development and Monitoring of a Fall Detection System through Wear...IRJET Journal
This document summarizes a research paper that developed and monitored a fall detection system using a wearable sensor belt. The system uses an IoT-based technology with a wearable belt containing an accelerometer to detect falls in elderly patients. When a fall is detected, it sends an alert to caregivers and also senses the location using GPS. The accelerometer data is sent to the cloud for monitoring visually using VPython. The system aims to quickly detect falls and send alerts to caregivers to allow for faster medical treatment and reduce health risks for elderly fall victims.
WIISEL Final Report - 1- Publishable Report FinalElisenda Reixach
The WIISEL project developed a wireless insole system to assess fall risk in elderly individuals in their home and community environments. The system collects gait data from insoles and analyzes parameters related to fall risk. It aims to allow for remote and quantitative assessment of fall risk, measuring activity and mobility under daily living conditions. The project was coordinated by CETEMMSA and funded by the European Commission over 41 months with a budget of 3.9 million euros and 8 partners from 6 countries. Validation studies showed the system can be useful as a research tool for studying fall risk and as a clinical tool for long-term monitoring of fall risk in home and community settings.
This document discusses situation awareness (SA) for high-quality operation and maintenance of smart distribution networks (SDN). It defines SA as having three stages: situation detection to collect data, situation comprehension to analyze and understand the data, and situation projection to predict future situations. The document reviews technologies for SDN SA and proposes a five-layer framework for visualizing the SA process. It aims to provide insights on technical advances, challenges, and future research directions for improving SDN operation through enhanced situation awareness.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Analysis of Fall Detection Systems: A Reviewijtsrd
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
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.
Social Distancing Detection, Monitoring and Management Using OpenCVIRJET Journal
This document proposes a system to detect social distancing violations using computer vision and deep learning algorithms. The system would identify individuals in video frames using a YOLOv3 model, calculate distances between detected individuals, and classify the risk level based on social distancing guidelines. It transforms frames into a bird's eye view to standardize distance measurements. The proposed system aims to help monitor social distancing and slow the spread of COVID-19 by identifying groups that are too close together. It achieved 92.8% precision in social distancing classification during testing.
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
IRJET- A Comprehensive Survey on Smart Healthcare Monitoring of Patients usin...IRJET Journal
This document summarizes a research paper that conducted a comprehensive survey on using the Internet of Things (IoT) for smart healthcare monitoring of patients. The key aspects covered are:
1) IoT enables remote patient monitoring through wearable devices that allow healthcare professionals to monitor patients' conditions without being physically present.
2) The survey reviewed various existing works on IoT-based remote patient monitoring systems that transmit patients' health data like temperature and oxygen levels to doctors via wireless networks and mobile apps.
3) Ensuring patient privacy and security while monitoring and accessing health data remotely is an important challenge addressed in some of the existing research.
IRJET- A Real Time, Automated, Medical Emergency Service for Informing th...IRJET Journal
This document discusses a proposed mobile application for providing automated medical emergency services. The application would monitor patients' vital signs to detect medical emergencies. If an emergency is detected, the application would alert the closest medical rescuer and provide patient details to allow for rapid response. The application would also allow patients to communicate with doctors and receive prescriptions. The proposal aims to reduce deaths from lack of timely medical attention by expediting emergency response.
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.
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
IRJET- Disaster Detection & Early Warning SystemIRJET Journal
This document describes a proposed disaster detection and early warning system that uses sensors and a microcontroller to monitor the environment for hazards. When thresholds are exceeded, the system sends SMS alerts, sounds an alarm, and displays warnings on an LCD screen to notify people. It aims to spread disaster information quickly via GSM to warn those at risk as early as possible. The system initialization, sensor input monitoring, alert activation, and normal/pre-disaster condition displays are outlined in the block diagram and flowchart. The goal is to save lives and reduce damage through early detection and warnings.
Non-contact, camera-based physiological measurements -
such as blood volume pulse and respiration rate - can now
be inferred by neural networks based on facial videos. This
technology has the potential to enable medical professionals
to make more informed telehealth decisions. Currently, this
software only runs on PCs, without a user interface. The neu-
ral network has a significant computational cost, making it
difficult to deploy on low-cost mobile devices. It also performs
poorly in varied environmental, sensor, personal, and contex-
tual conditions - such as darker skin tones. In this project,
we implement this neural network as an Android app that
runs in real-time; develop a more efficient architecture; evalu-
ate these architectures on older smartphones; and provide an
open-source, simple personalization pipeline to enable users to
calibrate the app. This all serves to make the technology more
democratic: making it available to as many users as possible,
while giving them the means to train and develop it further.
Gated recurrent unit decision model for device argumentation in ambient assis...IJECEIAES
The increasing elderly population worldwide is facing a variety of social, phys- ical, and cognitive issues, such as walking problems, falls, and difficulties in performing daily activities. To support elderly people, continuous monitoring and supervision are needed. Due to the busy modern lifestyle of caretakers, taking care of elderly people is difficult. As a result, many elderly people pre- fer to live independently at home without any assistance. To help such people, an ambient assisted living (AAL) environment is provided that monitors and evaluates the daily activities of elderly individuals. An AAL environment has heterogeneous devices that interact, and exchange information of the activities performed by the users. The devices can be involve in an argumentation about the occurrence of an activity thus leading to generate conflicts. To address this issue, the paper proposes a gated recurrent unit (GRU) learning techniques to facilitate decision-making for device argumentation during activity occurrences. The proposed model is used to initially classify user activities and each sensor value status. Then a novel method is used to identify argumentation among de- vices for activity occurrences in the classified user activities. Later, the GRU decision making model is used to resolve the argumentation and to identify the target activity that occurred. The result of the proposed model is compared with other existing techniques. The proposed model outperformed the other existing methods with an accuracy of 85.45%, precision of 72.32%, recall of 65.83%, and F1-Score of 60.22%.
A Survey on Smart Devices for Object and Fall DetectionIRJET Journal
This document summarizes a survey on smart devices for object and fall detection. It discusses how sensors and microcontrollers can be used to create wearable alert devices for the elderly that detect falls and send location information to concerned contacts. It also describes how ultrasonic sensors and smart glasses can detect obstacles to help blind or visually impaired people navigate safely. The document reviews several existing studies on vision-based and sensor-based fall detection systems and identifies challenges in real-world deployment, usability, and user acceptance of emerging technologies.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
More Related Content
Similar to EdgeFall: a promising cloud-edge-end architecture for elderly fall care
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.
IRJET- IoT based Smart Sensing Wheelchair to Assist in HealthcareIRJET Journal
The document proposes an IoT-based smart wheelchair that monitors a patient's heartbeat on a regular basis and notifies concerned individuals via message. Sensors would be integrated into the wheelchair seat and backrest to detect cardiovascular abnormalities. An Arduino board would connect to an IoT cloud platform via an edge device to transmit sensor data and receive commands. The system aims to provide independent mobility and remote health monitoring for patients who have difficulty operating a manual wheelchair.
Mini project PowerPoint presentation usefulg8248418302
The document describes a design project for a wearable fall detection device. A group of three students - Devika S, Geetharakchana R, and Janapriya E - presented their mini project on designing a wearable device that can detect falls using an MPU6050 sensor. The device would send an SMS notification to a mobile phone when a fall is detected to help reduce the response time for assistance. The document provides details on the objectives, problem identification, hardware requirements including the NodeMCU ESP8266 microcontroller and MPU6050 sensor, software requirements, and conclusions from exploring the effectiveness of using the MPU6050 sensor to detect falls.
IRJET- Development and Monitoring of a Fall Detection System through Wear...IRJET Journal
This document summarizes a research paper that developed and monitored a fall detection system using a wearable sensor belt. The system uses an IoT-based technology with a wearable belt containing an accelerometer to detect falls in elderly patients. When a fall is detected, it sends an alert to caregivers and also senses the location using GPS. The accelerometer data is sent to the cloud for monitoring visually using VPython. The system aims to quickly detect falls and send alerts to caregivers to allow for faster medical treatment and reduce health risks for elderly fall victims.
WIISEL Final Report - 1- Publishable Report FinalElisenda Reixach
The WIISEL project developed a wireless insole system to assess fall risk in elderly individuals in their home and community environments. The system collects gait data from insoles and analyzes parameters related to fall risk. It aims to allow for remote and quantitative assessment of fall risk, measuring activity and mobility under daily living conditions. The project was coordinated by CETEMMSA and funded by the European Commission over 41 months with a budget of 3.9 million euros and 8 partners from 6 countries. Validation studies showed the system can be useful as a research tool for studying fall risk and as a clinical tool for long-term monitoring of fall risk in home and community settings.
This document discusses situation awareness (SA) for high-quality operation and maintenance of smart distribution networks (SDN). It defines SA as having three stages: situation detection to collect data, situation comprehension to analyze and understand the data, and situation projection to predict future situations. The document reviews technologies for SDN SA and proposes a five-layer framework for visualizing the SA process. It aims to provide insights on technical advances, challenges, and future research directions for improving SDN operation through enhanced situation awareness.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Analysis of Fall Detection Systems: A Reviewijtsrd
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
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.
Social Distancing Detection, Monitoring and Management Using OpenCVIRJET Journal
This document proposes a system to detect social distancing violations using computer vision and deep learning algorithms. The system would identify individuals in video frames using a YOLOv3 model, calculate distances between detected individuals, and classify the risk level based on social distancing guidelines. It transforms frames into a bird's eye view to standardize distance measurements. The proposed system aims to help monitor social distancing and slow the spread of COVID-19 by identifying groups that are too close together. It achieved 92.8% precision in social distancing classification during testing.
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
IRJET- A Comprehensive Survey on Smart Healthcare Monitoring of Patients usin...IRJET Journal
This document summarizes a research paper that conducted a comprehensive survey on using the Internet of Things (IoT) for smart healthcare monitoring of patients. The key aspects covered are:
1) IoT enables remote patient monitoring through wearable devices that allow healthcare professionals to monitor patients' conditions without being physically present.
2) The survey reviewed various existing works on IoT-based remote patient monitoring systems that transmit patients' health data like temperature and oxygen levels to doctors via wireless networks and mobile apps.
3) Ensuring patient privacy and security while monitoring and accessing health data remotely is an important challenge addressed in some of the existing research.
IRJET- A Real Time, Automated, Medical Emergency Service for Informing th...IRJET Journal
This document discusses a proposed mobile application for providing automated medical emergency services. The application would monitor patients' vital signs to detect medical emergencies. If an emergency is detected, the application would alert the closest medical rescuer and provide patient details to allow for rapid response. The application would also allow patients to communicate with doctors and receive prescriptions. The proposal aims to reduce deaths from lack of timely medical attention by expediting emergency response.
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.
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
IRJET- Disaster Detection & Early Warning SystemIRJET Journal
This document describes a proposed disaster detection and early warning system that uses sensors and a microcontroller to monitor the environment for hazards. When thresholds are exceeded, the system sends SMS alerts, sounds an alarm, and displays warnings on an LCD screen to notify people. It aims to spread disaster information quickly via GSM to warn those at risk as early as possible. The system initialization, sensor input monitoring, alert activation, and normal/pre-disaster condition displays are outlined in the block diagram and flowchart. The goal is to save lives and reduce damage through early detection and warnings.
Non-contact, camera-based physiological measurements -
such as blood volume pulse and respiration rate - can now
be inferred by neural networks based on facial videos. This
technology has the potential to enable medical professionals
to make more informed telehealth decisions. Currently, this
software only runs on PCs, without a user interface. The neu-
ral network has a significant computational cost, making it
difficult to deploy on low-cost mobile devices. It also performs
poorly in varied environmental, sensor, personal, and contex-
tual conditions - such as darker skin tones. In this project,
we implement this neural network as an Android app that
runs in real-time; develop a more efficient architecture; evalu-
ate these architectures on older smartphones; and provide an
open-source, simple personalization pipeline to enable users to
calibrate the app. This all serves to make the technology more
democratic: making it available to as many users as possible,
while giving them the means to train and develop it further.
Gated recurrent unit decision model for device argumentation in ambient assis...IJECEIAES
The increasing elderly population worldwide is facing a variety of social, phys- ical, and cognitive issues, such as walking problems, falls, and difficulties in performing daily activities. To support elderly people, continuous monitoring and supervision are needed. Due to the busy modern lifestyle of caretakers, taking care of elderly people is difficult. As a result, many elderly people pre- fer to live independently at home without any assistance. To help such people, an ambient assisted living (AAL) environment is provided that monitors and evaluates the daily activities of elderly individuals. An AAL environment has heterogeneous devices that interact, and exchange information of the activities performed by the users. The devices can be involve in an argumentation about the occurrence of an activity thus leading to generate conflicts. To address this issue, the paper proposes a gated recurrent unit (GRU) learning techniques to facilitate decision-making for device argumentation during activity occurrences. The proposed model is used to initially classify user activities and each sensor value status. Then a novel method is used to identify argumentation among de- vices for activity occurrences in the classified user activities. Later, the GRU decision making model is used to resolve the argumentation and to identify the target activity that occurred. The result of the proposed model is compared with other existing techniques. The proposed model outperformed the other existing methods with an accuracy of 85.45%, precision of 72.32%, recall of 65.83%, and F1-Score of 60.22%.
A Survey on Smart Devices for Object and Fall DetectionIRJET Journal
This document summarizes a survey on smart devices for object and fall detection. It discusses how sensors and microcontrollers can be used to create wearable alert devices for the elderly that detect falls and send location information to concerned contacts. It also describes how ultrasonic sensors and smart glasses can detect obstacles to help blind or visually impaired people navigate safely. The document reviews several existing studies on vision-based and sensor-based fall detection systems and identifies challenges in real-world deployment, usability, and user acceptance of emerging technologies.
Similar to EdgeFall: a promising cloud-edge-end architecture for elderly fall care (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
EdgeFall: a promising cloud-edge-end architecture for elderly fall care
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 4, August 2023, pp. 4721∼4733
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i4.pp4721-4733 ❒ 4721
EdgeFall: a promising cloud-edge-end architecture for
elderly fall care
Kazi Md Shahiduzzaman, Md. Salah Uddin Yusuf
Department of Electrical and Electrical Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh
Article Info
Article history:
Received Jul 4, 2022
Revised Oct 26, 2022
Accepted Nov 6, 2022
Keywords:
Accelerometer
Elderly care
End-edge-cloud architecture
Gyroscope
Human activity
Long term short memory
Wearable camera
ABSTRACT
Elder citizens face sudden fall, which can lead to injuries of both destructive
and non-virulent. These sudden falls are later more precarious than diseases like
heart attack, blood sugar, blood pressure because these can be untreated for a
lengthy time which can lead to death. Elder citizen who experiences a precipi-
tous fall, carry out their communal life narrowed. Therefore, a shrewd and ad-
equate anti-fallen system is required for aiding elderly health care, specifically
to those who live individually. So, it can identify and anticipate a precipitous
fall through appropriate human activity recognition. In this study, we have sug-
gested an end-edge-cloud based wearable EdgeFall architecture for elderly care.
We have performed simulation setups to clarify the query of why we need such
a strategy, and its validity. We have achieved maximum 91.87% accuracy with
1.6% false alarm rate (FAR). These empirical results indicate the superiority of
using tightly couple multiple information for recognizing human activity. We
can accomplish a low FAR with an enhanced accuracy. We can observe that our
proposed end-edge-cloud based architecture can reduce the execution time to
millisecond range (ms) of 14.16 to 15.74. This work serves as the starting mark
for future related research activities.
This is an open access article under the CC BY-SA license.
Corresponding Author:
Md. Salah Uddin Yusuf
Department of Electrical and Electronic Engineering, Khulna University of Engineering and Technology
Khulna, Bangladesh
Email: suyusuf@eee.kuet.ac.bd
1. INTRODUCTION
Any abnormality in everyday human activity brings an unusual fall. An adequate routine human
activity recognition can identify, anticipate and inhibit many diseases. Walking, running (jogging), jumping,
walking stairs, sitting, standing, and laying are closely related to elderly fall-related service [1]–[3]. Besides,
human activity recognition is an elemental part of most explorations concerned to fall detection and prediction
[3]. That is why we choose to lead our investigation with human activity recognition from the context of fall
detection and prediction.
We can express the interpretation of fall as the sudden change of state from a higher post ion like
standing to a lower position like laying or sitting [4]. People over 65 years of age are at significant risk
of falling, corresponding to many published articles. A survey from the World Health Organization (WHO)
shows that there are around 650 thousand injuries occur that are linked to falls every year [1], [5]–[7]. These
falls induce curable or incurable injuries from flesh cramping, fractured extremities, posterior damage, and
brain injury to life expiry. Every inmate has to put in an abundance of payment in medicine, therapy, and
treatment [4], [8]–[10]. The after-effects of falls are likewise severe as they lead to falls, depression, restraint
Journal homepage: http://ijece.iaescore.com
2. 4722 ❒ ISSN: 2088-8708
of regular living movements, fewer personal tasks, and poorer conditions of life, specifically for indepen-
dent elderly citizens [8]. A surprisingly striking factor is the aged populations will explode in the upcoming
20 years [11]. A survey was done on people equal to and over 65 years to answer the requirement of a fall-
related service in [12]. The number of participants was 125. The analysis shows that falling on the floor for
more than an hour leads to death within a six months period [12]. Over eight hundred thousand old individu-
als, 65 years or above, are using medical alert systems like MobileHelp, Bay alarm medical, LifeFone, Philips
Lifeline, and QMedic, with the lowest cost of USD 19.99 [13]. These services offer wearable or mobile devices
to bring mobility to the users. Besides the subscription fees, these devices still require user aid for pressing the
SOS button during an accident. This is the communal demand to promote a dynamic solution that can predict
and detect a precipitous fall. We crave to build up such a structure that can be self-employed in a necessary and
affordable.
Now, we recommend the interpretation of a wearable smart anti-fall from the point of its performance
and the opportunity of fitting into the smart environment. We can suggest the definition as a wearable device
capable of predicting and detecting an abrupt fall. For example, the system designed and developed in [14]
which can detect and predict a sudden fall while Mauldin et al. [15], Naihan et al. [16], [17] illustrate the
opportunity of enforcing end-edge-cloud approach for fall detection. So, we should develop the architecture
with various sensors like wearable cameras, accelerometers, gyroscopes, global positioning system (GPS), and
alarms. The functions of such architecture should not restrict to predicting and detecting falls but continu-
ing the auxiliary functions like position detection and sending alarms and messages to the involved parties.
Comfortable to carry, small battery utilization and significant reliability are the elementary properties of such
a system. Any wearable platform like spectacles on the eyes, helmet on the head, and gear around the waist
can build up an anti-fall system. This system should be transparent to fuse with a smart environment like smart
health care and smart home system [18], [19]. This intelligent system can be an integral part of information
and communications technology (ICT)-based healthcare solutions for elderly people in the smart home concept
similar to [20]. It is significant to define two more terms which are:
− Fall prediction: A process that forecasts a sudden fall and tries to restrain it. It provides a clue to the user
about an abrupt fall in progress so that the user can become alert and escape the fall for further emotional
and health consequences.
− Fall detection: A process that identifies a fall and gives alarms to the corresponding third parties. The third
parties include the relative of the sufferers, caretakers and near medical amenities so that the effect of falls
can be diminished and the medication can be set up as promptly as possible.
So, a robust prediction process is required before falling down, and a detection process is after a
fall occurs. In [7], the authors reviewed sensors, algorithms, and performance on the 20 most authoritative
and often cited papers among 6,830 papers with keywords linked to falling detection. The report reveals that
an accelerometer is usually employed for data collection, machine learning-based algorithms have become
mainstream in this area, and accuracy is the key performance evaluation factor. There is a positive sugges-
tion of applying camera and accelerometer for data collection i,e, data fusion. This study shows nothing
about the impact of false alarm rate (FAR) on fall detection or prediction. While the review on fall detection
in [21], we see that wearable-based development is more suitable for real-world implementation. Researches
like Saadeh et al. [14], Mualdin et al. [15], Rachakonda et al. [17], Ozcan et al. [22], Micucci et al. [23],
George et al. [24], Davide et al. [25], Reyes-Ortiz et al. [26], Li et al. [27], Howcroft [28], Engel and Ding [29],
Wang et al. [30], and others have developed fall detection systems with a wearable camera, kinit camera sen-
sors, accelerometer, gyroscope, internal measurement unit (IMU), Wi-Fi system with either machine-learning
interfaced or simply threshold-based techniques. Almost all instances apply experimental setup to classify a
binary type grouping, i,e, defining fall or not fall. Classical machine learning techniques like support vector
machine (SVM), k- neighbor nearest (k-NN), and artificial neural network (ANN), are not up to the level for
defining different human activities of daily living (ADL) [23]. We have noticed that threshold-based algorithms
can lead to a false alarm in recognizing multiclass human activity through our own empirical results [31]. In
the case of fall prediction, a few researches like Li et al. [27], Engel and Ding [29], Otanasap [32], Tong et al.
[33] have developed methods using wearable and ambient devices with both dynamic threshold and machine
learning techniques. These algorithms can maintain adequate lead time to the users before falling down so they
can be aware. Therefore the algorithms developed strategies for such kind of employment should recognize
human activities to separate them from falls.
Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4721-4733
3. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 4723
One of the dominant concerns regarding these developments is the on-chip or on-board system, which
have defined power sources and computational capacity. It is worthwhile to investigate end-edge-cloud-based
architecture for prediction. Because this architecture can hold larger and perpetual power sources and compu-
tational capacity. Therefore, our technical interest in this work is to reinforce the accuracy, lower the FAR, and
raise the performance of the classification network so it can realize different human activities, lower latency in
classifying tasks, less power utilization, and adequate lead time prediction. In this paper, we are mainly focus-
ing on the classification process. We crave to estimate the usefulness of end-edge-cloud architecture using data
fusion and modern machine learning approaches in ADLs recognition. Because this recognition is the initial
stride toward fall detection and prediction. Thus, we propose EdgeFall, an end-edge-cloud-based architecture
where different data sources are fused, and a recurrent neural network (RNN) approach will analyze human
activity. We have set up our simulation through four datasets which are UniMIB SHAR [23], MobiAct [24],
UCI HAR [25] and HAPT [26] for demonstrating the potency of data fusion in human activity recognition
(summary is given in Table 1). We can reproduce various daily activities of a group of volunteers with these
datasets. We have further studied the classic machine learning-based activity recognition model with recurrent
neural network (RNN) based model on these datasets. The performance evaluating metrics will help us design
human activity relevant to the real world.
Table 1. Characteristics of considered public datasets
Dataset UniMIB SHAR [23] MobiAct [24] UCI HAR [25] HAPT [26]
Size of original data 255 MB 1.24 GB 241 MB 7.54 KB
Representation Both ADL and fall Both ADL and fall Daily Human Daily Human
activities activities
No. of sensors 1 3 2 2
Sensors used Accelerometer Accelerometer, Accelerometer and Accelerometer and
Gyroscope and Gyroscope Gyroscope
Orientation sensor
Device used to Samsung Galaxy Nexus Samsung Galaxy S3 Samsung Galaxy S II Samsung Galaxy S
collect data I9250 with Bosh with LSM330DLC II with HARApp
BMA220 module
Position of the Trouser front pocket (half Not Specified Waist-mounted Different body parts
device of the time in the left one (trunk, upper and
and the remaining time lower extremities)
in the right one)
No of Subjects 30 (24 Females, 6 males) 57 (15 Females, 30 (the number of 17 (the number of
42 males) female and male is) female and male is)
not specified) not specified)
Physical Age: 18-60, Age: 20-47, Age: 19-48 Not specified
information about Height: 160-190, Height: 160-189,
the subjects Weight: 50-82 Weight: 50-120
No. of activities 9 types of ADL and 9 types of ADL and 6 type of daily 12 type of daily
8 type of fall 4 type of fall activities activities
Nature of data Raw accelerometer Raw accelerometer, Extracted feature Feature from
data gyroscope and accelerometer and accelerometer and
orientation data gyroscope data gyroscope data
Sampling frequency 50 Hz 20 Hz 50 Hz 50 Hz
in original dataset
Samples in modified 4,632 67,348 10,299 10,411
dataset
Depicted activities walk, walk up-stair, walk, walk down-stair, walk, walk up-stair, walk, walk up-stair,
walk down-stair, sitting, sitting, standing, walk down-stair, sitting, walk down-stair, sitting,
standing, laying down jogging standing, laying down standing,laying down
Signal window 3 second 1 second 1.5 second 1.5 second
These results likewise indicate how adequately EdgeFall architecture can analyze different human
activities. We observe from the assessment that the model developed by ActDec-SysOpt outperforms classical-
based recognition models. So, our significant contributions are:
− Designing and resembling the end-edge-cloud-based EdgeFall architecture to figure out the usefulness in
human activity detection.
EdgeFall: a promising cloud-edge-end architecture for elderly fall care (Kazi Md Shahiduzzaman)
4. 4724 ❒ ISSN: 2088-8708
− Spot the challenges like FAR, accuracy, precision, and recall in loosely coupled data fusion for recognizing
human activities.
− Performance improvement in classifying and realizing human daily activity through tightly coupled multiple
information sources and ActDec-SysOpt algorithm.
The remaining paper dwells on four more sections. The next section will present the proposed Edge-
Fall paradigm for activity recognition. Algorithms used for this application are depicted in section 4 with
respective flowcharts. The EdgeFall is simulated using the ActDec-SysOpt algorithm for human activity detec-
tion and described there. Then, the empirical setups for evaluating EdgeFall architecture with data fusion are
analyzed in section 5. The performance evaluating metrics are also enlisted in this section. Research findings
and highlights on prospective research directions are mentioned in the conclusion.
2. EdgeFall: THE PROPOSED ARCHITECTURE
The proposed EdgeFall system is a three-tiered system as depicted in Figure 1 which are the artificial
intelligence (AI)-aware medical cloud for model development, the mobile edge for human activity recognition,
user identification, fall detection, prediction, and the body area network (BAN) for smart sensing and command
execution. The proposed architecture makes the fall detection and prediction system into three distributed sub-
systems connected to each other by the wireless network. We expect this architecture to be efficient regarding
computational capacity and energy management. BAN is indeed a platform consists sensors like wearable
cameras, gyroscopes, accelerometers, GPS sensors, and other related sensors and is pictured as a wearable
glass, but we can accept any platform like a watch, wearable around the waist, helmet on the head, smart-
phone on the pocket or similar. This layer performs data collection, feature extraction, communication to the
edge node, and decision execution. The mobile edge layer consists of a rational edge node and contrasting
processing units. User identification, activity recognition, lead time calculation for fall prediction, and fall
detection are major responsibilities of this tier. It will adapt protocols, classification networks, and users’ daily
human activity patterns from the cloud. The last tier is the cloud, the intellect of this technique. Deep learning,
special machine learning approach, and human intervention (HI) are used to implement this tier. It develops a
user identification model and activity classification model. It should update the mobile edge node with updated
reformed system parameters in a frequent cycle. This architecture can again refer to diverse areas related to
health care.
Figure 1. Proposed architecture of EdgeFall architecture
In this script, we will present the essence of this recommended architecture by breaking up the com-
putational capacity among mobile edge nodes and the cloud. Here, we have simply focused on various human
activity recognition. Because it will contribute to strong fall detection and prediction.
Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4721-4733
5. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 4725
3. PROPOPSED ALGORITHM
This section will discuss the methodologies named FuseFall and ActDec-SysOpt algorithm. These two
approaches will demonstrate the validity of data fusion, cloud-edge-end type architecture, and the classification
algorithm based on recurrent neural networks. This section will help the readers to visualize the approaches
used in this paper.
3.1. FuseFall algorithm
Fall detection takes advantage of two sources (a wearable camera and a tri-axial accelerometer) of
information. Two different sources detect human activity and fall separately. The block diagram in Figure 2(a)
explains the FuseFall algorithm. In this algorithm, the information sources are loosely coupled and analyzed
separately. We can observe that both sources use a different suitable algorithm to define the same activity
separately and compare it to the final decision from the block diagram, as shown in Figure 2(a). This algorithm
is explained in [31]. Two separate processes are used for two data types to reduce the complexity. Both
processes run simultaneously but on two different platforms. We assume that the resource required to analyze
inertia sensors data is lesser than image processing. So, the inertia sensor’s data processing process will run in
the edge node, while image processing will occur in the cloud. An optimization algorithm will be necessary to
enhance the efficiency of the system. In the detection phase, inertia data will be analyzed to detect a fall using
SVM machine learning technique. The reason behind using SVM technique is to perform binary classification
between AD and fall. A signal window of 3 seconds is considered to determine an event. In the case of
detection, image processing techniques are used to determine the feature (i,e, dissimilarity distance between
odd frames) from the captured images between 1.5 seconds backward and 1.5 seconds forward from the instant
of fall occurrence. This dissimilarity distance will verify the fall event determined in the detection phase. The
overall fall detection algorithm is shown in algorithm 1. Finally, another process will be used to optimize
the system presented in algorithm 2. This process automatically selects the optimal trained model and system
parameters to overwrite the old network.
3.2. ActDec-SysOpt algorithm
Figure 2(b) displays the block diagram of ActDec-SysOpt. We train the classification model with long
short term memory network (LSTM). This algorithm includes two parts. We adopt ActDec to recognize the
human activity, whereas SysOpt is for training classification networks. These two parts also depict mobile edge
nodes and medical cloud appropriately. We explain the details in [34].
(a) (b)
Figure 2. Block diagram of proposed algorithms (a) block diagram of FuseFall algorithm for fall detection and
(b) simple block diagram of ActDec-SysOpt algorithm
EdgeFall: a promising cloud-edge-end architecture for elderly fall care (Kazi Md Shahiduzzaman)
6. 4726 ❒ ISSN: 2088-8708
Now the information sources are tightly coupled. This algorithm represents a centralized trained
model of human activity recognition for all users. SysOpt generates the trained classification model (ActDec)
on the cloud server and pushes it to the edge server. A multivariate time series signal is fed to the bi-directional
LSTM. A signal window of 1.5 seconds is used here. A softmax layer matches the best probability fit for a
certain human activity. With a fully connected network, each human activity model is trained in the cloud layer.
The point should be mentioned that the SysOpt algorithm optimizes the parameters like learning rate, epoch,
and number of layers. According to the incoming data and the patient’s nature of performing the activities. This
trained network is then passed to the edge layer, where the incoming pre-processed raw data from the edge layer
will be analyzed to determine the particular activities and types of falls. The overall activity detection algorithm
is shown in algorithm 3. Finally, another algorithm will be used to initialize and optimize the system presented
in algorithm 4. The test data will be labelled and added to the training data to train the even detection network.
If the result of the newly trained system is better than the old one, the parameter e,g, threshold value used in the
verification phase will be changed. Otherwise, the old parameters are kept. The fall detection and verification
algorithm runs in an intelligent access point, whereas the optimization algorithm and process data will be stored
in the cloud.
Algorithm 1: Detection process of FuseFall algorithm
Detection Phase
Data: Inertia Sensor’s Data
Result: Detection of an activity
Load SVM Model
initialization
while Unless detecting a Fall do
Extract feature F(accl) from signal window
%3 sec data
if F(accl) ∼ ADL then
go to initialization
else
if F(accl) ∼ Fall then
Verification Phase
end
end
end
Verification Phase
Data: Image data
Result: Verification of Fall
while Fall Detected do
Load images between t-1.5 to t+1.5
%Images correspond to 3 sec
Extract feature I(dissimilarity)
if I(dissimilarity) ∼ Fall then
Fall verified
else
Consider as ADL
end
end
Algorithm 2: FuseFall optimization process
Data: Test Data
Result: Optimization of the System
begin
Test data −→ Training Data
Evaluate Both trained model
if ResultNew > ResultOld then
Pass Paranew to Detection process
Modify Both Phase
else
Keep previous State
Remove Test data
end
end
Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4721-4733
7. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 4727
Algorithm 3: ActDect algorithm
Data: Inertia sensor’s data
Result: Event Detection
Load Trained LSTM Classification Model
initialization
while Unless detecting an event do
Extract feature F(accl & gyro) from signal window
%1.5 sec data
if F(accl & gyro) ∼ Daily Activity then
go to initialization
else
if F(accl & gyro) ∼ Fall then
Alarm, Protection
end
end
end
Algorithm 4: SysOpt algorithm
Data: Test Data
Result: Optimization of detection system
begin
Test data −→ Training Data
Run LSTM with TrainingDataNew
if V alidationNew > V alidationOld then
Modify Classification Network
Pass Paranew to Event Detection Algorithm
else
Keep previous State
Remove Test data
end
end
4. EVALUATION
This section presents our experiments and results using FuseFall and ActDec-SysFall architecture.
The findings will justify our claims outlined in 1. First, the performance evaluating metrics will be deliberated,
and results and findings from studied algorithms will be later.
4.1. Metrics
Accuracy, recall, precision, F1-score, FAR, training time, and execution time are the performance
evaluation metrics. The definitions are given below:
− Accuracy is stated precisely the percentage of correctly detected activities from the tested one.
− Recall is interpreted as the percentage of proper activities that are exactly verified. It provides information
about the accuracy row-wise of the confusion matrix.
− Precision is elucidating as the segment of equitable classified activities i,e, accuracy along column wise of
the confusion matrix.
− F1 score is the harmonic mean of precision and recall, which measures the accuracy of the test.
− False alarm rate (FAR) acquaints with how the system gets confused between any activity and other con-
sidered activities.
− Training-time is the time required by the cloud to generate a global classification network with the proposed
algorithm.
− Execution time is the time the global classifier recognizes human activities at the mobile edge node.
4.2. Experiments with FuseFall
We have simulated and tested loosely coupled two different data in this experiment. The results are
generated by the wearable camera y1 and the raw accelerometer data y2 separately at this stage, as shown
in Figure 3(a). This experiment demonstrates the real-life daily human activities and falls related to elderly
people.
EdgeFall: a promising cloud-edge-end architecture for elderly fall care (Kazi Md Shahiduzzaman)
8. 4728 ❒ ISSN: 2088-8708
4.2.1. Experimental setup and results
Figure 3(a) presents the simulation scheme. A forward fall will take place from the walking position.
We present the details about how to evaluate it in [31]. We calculate the accuracy, precision, recall, F1-score,
and execution time of y2 from 4.1. We consider the k-NN method for comparison. The results are presented
in Table 2. We have utilized the frontal camera of Xiaomi S2 around the head height to perform the same
activities by taking video, similarly to [34]. As illustrated in Figure 3(b), the dissimilarity distance for the
video frames during the fall is considerably larger than other frames, marked by two red circles. From the
binary classification between falls and daily human activity, we can determine the effectiveness of FuseFall.
Nevertheless, the challenge persists in performing this FuseFall with multi-class classification to recognize
the daily human activity. Please point out to [34] what we mean by multi-class activity. Table 3 gives the
performance of the FuseFall algorithm for accelerometer data with k-NN. During video processing, we have
observed high picks like Figure 3(b), which can cause fall alarms. Table 3 and Figure 3(b) show that a loosely
coupled information source with the FuseFall algorithm is unsuitable for multi-class activity detection. That
is why we hold our experiments with y1 and y2. Besides, the time involved in generating results with video
processing is large in our experiments. The challenges ascribed to the multi-class activity classification are
managed more accurately by the ActDec-SysOpt algorithm [34], which will be demonstrated in the next part.
(a)
(b)
Figure 3. Experimental outcome of FuseFall (a) experimental setup to evaluate FuseFall algorithm and
(b) FAR during multiclass activity detection with FuseFall (video processing part)
Table 2. Partial evaluation performance of FuseFall to differentiate fall from daily activities
Metrics FuseFall for y2 k-NN
Accuracy 0.9667 0.900
Recall 0.9667 0.900
Precision 0.9839 0.9545
F1-score 0.9752 0.9265
FAR 0.033 0.10
Execution time (sec) 2.282 1.043
Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4721-4733
9. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 4729
Table 3. Performance of FuseFall for multi-class type human activity recognition
Metrics FuseFall for y2 k-NN
Accuracy 0.8468 0.8022
Recall 0.8198 0.8222
Precision 0.8518 0.8595
F1-score 0.8355 0.8405
False alarm rate (FAR) 0.0396 0.0547
4.3. Experiments with ActDec-SysOpt
As we have previously noticed, that single source of information and classical machine learning-based
models are not up to the adequate standard for realizing different human activities. Here we will adopt ActDec-
SysOpt [34] algorithm and data fusion technique to justify some remarkable objectives, i) demand of proposed
EdgeFall as shown in Figure 1, ii) the performance of multiple sources, use of features and LSTM, and iii) the
prerequisite of tight coupling.
4.3.1. Experimental setup
For the experimental setup we have utilized four original data sources, named, UniMIB SHAR [23],
MobiAct [24], UCI HAR [25] and HAPT [26] to simulate BAN of the EdgeFall. These sources consist of
diverse human activities for several volunteers. We have transformed the original datasets in such a manner
that it mirrors the BAN of the proposed architecture which relays data to the edge node regularly in time
windows. The preliminary results will confirm the potency of tightly coupled data fusion. ActDec represents
the mobile edge node while SysOpt is for the medical cloud. Here, we want to assess how dramatically this
algorithm can recognize different human activities i,e, the classification process.
4.3.2. Results
First, we will present the operation of EdgeFall through ActDec-SysOpt algorithm. The supporting
chart (Table 4 accumulates the simulation result which outperforms classical machine learning type algorithm.
We have considered a traditional data split (70-30) between training and test data. Results indicate that ActDec-
SysOpt outperforms the other two machines learning-based classification models (where one of them is Fuse-
Fall). Accordingly, we will weigh the performance of ActDec-SysOpt on different datasets. These different
datasets represent BAN of the recommended EdgeFall architecture (i,e, several users with unfamiliar weights,
heights, ages, and gender).
Table 4. Performance of ActDec-SysOpt over FuseFall and k-NN algorithm
Methods FuseFALL k-NN (k=2) ActDec-SysOpt
Accuracy 0.8468 0.8022 0.9187
Recall 0.8198 0.8222 0.8303
Precision 0.8518 0.8595 0.8511
F1-score 0.8355 0.8405 0.8406
FAR 0.0396 0.0547 0.025
Training-time (minute) 2.282 1.043 112
To conduct the simulation further sensibly, we have considered a 50-50 approach for training and test
data to appraise the validity of data fusion. Figure 4 displays the simulation results and reveals that data fu-
sion from numerous sources with proposed ActDec-SysOpt have stronger achievement than information from
a single source and loosely coupled multiple information sources. We can observe that feature extraction can
develop a far better result than raw data fusion. The accuracy of raw data fusion is remarkably unsatisfac-
tory around 59.65% with a considerable FAR 10.66%. Whereas the feature extraction (tightly coupled) from
multiple information sources forms a sharp accuracy of 92.01% with FAR of 1.69%.
We present the expected training time and classification execution time in Table 5. The time of training
classification network and execution time to realize an activity is higher for tightly coupled multiple information
sources. As we have divided the classification model development and execution process into cloud and edge,
the execution time may not be a perturbing factor. This time can be boosted by adopting a hardware/software
co-design concept.
EdgeFall: a promising cloud-edge-end architecture for elderly fall care (Kazi Md Shahiduzzaman)
10. 4730 ❒ ISSN: 2088-8708
Figure 4. Advantages of data fusion from multiple information sources
From Tables 4 and 5, Figure 4 we come up with the following decisions:
− Tightly coupled information sources with an LSTM-based classification model are more adequate. The
accuracy is around 92% with FAR being 1.6%. The high value of recall, precision, and F1-score confirm
that the classification model is competent in recognizing human activity precisely.
− The execution time compelled to recognize activity from one window of information is noticeably less,
around 15 ms with data fusion from multiple sources (check execution time of Table 4.
− RNN type machine learning-based algorithms are better applicable to recognizing human daily activities.
− Features are better reasonable as they can decrease the FAR. 1.69% with HAPT and 1.87% with UCI HAR.
− However, we can learn that training the classification model requires a lot of time, 1,094 minutes for HAPT
and 691 minutes for UCI HAR. Medical cloud is good enough to deal with the training process because
there are no issues with the computational capability and cache.
− The train network later can be passed to the edge node which can execute activity recognition.
− Data collection and feature extraction can be managed conveniently in BAN.
Table 5. Effectiveness of EdgeFall architecture in computation
Methods UniMIB SHAR Mobi Act HAPT UCI HAR
Training-time (minute) 84 161 1094 691
Execution time (ms) ∼ 4.52 - 7.065 ∼ 0.6 - 0.73 ∼ 14.16 - 15.74 ∼ 15.20 - 16.71
So far, ActDec-SysOpt is a definite stride toward fall prediction and detection using EdgeFall. Nev-
ertheless, there are some challenges that need to figure out. For example, accuracy, FAR, a simple algorithm
for fall prediction and detection, and user identification. Though, we are a little behind in accomplishing the
coveted accuracy of over 95%.
5. CONCLUSION
In this study, we have recommended a cloud-edge-end architecture, EdgeFall, which can distribute
the full function of a single board structure to three tiers. A particular contribution of this script is that we
can uphold our claims through proper simulation results. We have sought to figure out challenges related to
establishing this system through appropriate experimental results. The ActDec-SysOpt algorithm represents
not only the EdgeFall architecture but also validates the potency of such architecture. The ActDec-SysOpt
Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4721-4733
11. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 4731
algorithm also opens up the opportunity for exploiting data fusion for this system for further enhancements in a
term of accuracy and FAR for human activity recognition. We are able to obtain 92% accuracy while lowering
the FAR to 1.69%. Therefore, this EdgeFall architecture can be regarded as being beneficial for reducing
computational capacity and power utilization on BAN, reinforcing the classification performance by specific
recognition of an activity. This architecture can also be useful in the management of mobile edge nodes, user-
profile management (patient-specific), and quality of service (QoS) which are our future research directions.
A suitable single algorithm requires to promote which can predict and detect falls. Visual inertial odometry-
based algorithms may have the capacity because they can produce trajectory. User identification is another issue
that opens a possible research trend. We can develop a modern machine learning-based algorithm to solve it
with privacy protection measures. Another prospective future task for the smart anti-fall system is to build
up a secure, reliable, low latency-based network. Software-defined network (SDN) can be the most practical
solution for not only meeting the QoS requirements but also providing security to the network. Speeding
up the execution time is another demand in implementing this EdgeFall architecture into a prototype device.
Hardware-software co-design approach can overcome this challenge. This paper not only reviews but also
evaluates different approaches toward developing smart anti-fall systems. Concepts, algorithms, experimental
setups, results and decisions, provide comprehensive guidance for deeper investigation, particularly in this
emerging area.
ACKNOWLEDGEMENT
This project is partially funded by Jatiya Kabi Kazi Nazrul Islam University’s Annual (2021-22) Re-
search Grunt.
REFERENCES
[1] Z. Wang, V. Ramamoorthy, U. Gal, and A. Guez, “Possible life saver: a review on human fall detection technology,”
Robotics, vol. 9, no. 3, Jul. 2020, doi: 10.3390/robotics9030055.
[2] E. C. Dinarevic, J. B. Husic, and S. Barakovic, “Issues of human activity recognition in healthcare,”
in 2019 18th
International Symposium INFOTEH-JAHORINA (INFOTEH), Mar. 2019, pp. 1–6, doi: 10.1109/IN-
FOTEH.2019.8717749.
[3] J. A. Álvarez-Garcı́a, L. M. Soria Morillo, M. Á. Á. de La Concepción, A. Fernández-Montes, and J. A. O. Ramı́rez,
“Evaluating wearable activity recognition and fall detection systems,” in IFMBE Proceedings of the European
Conference of the International Federation for Medical and Biological Engineering, vol. 45, 2015, pp. 653–656.
[4] K. Chaccour, R. Darazi, A. H. El Hassani, and E. Andres, “From fall detection to fall prevention: a
generic classification of fall-related systems,” IEEE Sensors Journal, vol. 17, no. 3, pp. 812–822, Feb. 2017,
doi: 10.1109/JSEN.2016.2628099.
[5] WHO, “Fall,” World Health Organization, 2018. https://www.who.int/news-room/fact-sheets/detail/falls (accessed
Aug. 20, 2018).
[6] S. Liang, Y. Liu, G. Li, and G. Zhao, “Elderly fall risk prediction with plantar center of force using ConvL-
STM algorithm,” in 2019 IEEE International Conference on Cyborg and Bionic Systems (CBS), 2019, pp. 36–41,
doi: 10.1109/CBS46900.2019.9114487.
[7] T. Xu, Y. Zhou, and J. Zhu, “New advances and challenges of fall detection systems: a survey,” Applied Sciences,
vol. 8, no. 3, Mar. 2018, doi: 10.3390/app8030418.
[8] R. Rajagopalan, I. Litvan, and T.-P. Jung, “Fall prediction and prevention systems: recent trends, challenges, and
future research directions,” Sensors, vol. 17, no. 11, Nov. 2017, doi: 10.3390/s17112509.
[9] T.R. Frieden, D. Houry, G. Baldwin, A. Dellinger, R. Lee, “Preventing falls: a guide to implementing effective
community-based fall prevention programs,” National Center for Injury Prevention and Control of the Centers Disease
Control and Prevention, 2015.
[10] J. Hamm, A. G. Money, A. Atwal, and I. Paraskevopoulos, “Fall prevention intervention technologies: A conceptual
framework and survey of the state of the art,” Journal of Biomedical Informatics, vol. 59, pp. 319–345, Feb. 2016,
doi: 10.1016/j.jbi.2015.12.013.
[11] “Global Ageing,” ageinternational, 2018. https://www.ageinternational.org.uk/policy-research/statistics/global-
ageing/ (accessed Nov. 22, 2018).
[12] L. Montanini, A. Del Campo, D. Perla, S. Spinsante, and E. Gambi, “A footwear-based methodology for fall detec-
tion,” IEEE Sensors Journal, vol. 18, no. 3, pp. 1233–1242, Feb. 2018, doi: 10.1109/JSEN.2017.2778742.
[13] A. Clark, “The best medical alert systems 2019,” TheSeniorList, Sep. 2019. https://www.theseniorlist.com/medical-
alert-systems/best/ (accessed Sep. 21, 2018).
EdgeFall: a promising cloud-edge-end architecture for elderly fall care (Kazi Md Shahiduzzaman)
12. 4732 ❒ ISSN: 2088-8708
[14] W. Saadeh, S. A. Butt, and M. A. Bin Altaf, “A patient-specific single sensor IoT-based wearable fall predic-
tion and detection system,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 5,
pp. 995–1003, May 2019, doi: 10.1109/TNSRE.2019.2911602.
[15] T. R. Mauldin, M. E. Canby, V. Metsis, A. H. H. Ngu, and C. C. Rivera, “SmartFall: a smartwatch-based fall detection
system using deep learning,” Sensors, vol. 18, no. 3363, Oct. 2018, doi: 10.3390/s18103363.
[16] M. J. Al Nahian et al., “Towards an accelerometer-based elderly fall detection system using cross-disciplinary time
series features,” IEEE Access, vol. 9, pp. 39413–39431, 2021, doi: 10.1109/ACCESS.2021.3056441.
[17] L. Rachakonda, S. P. Mohanty, and E. Kougianos, “Good-eye: a device for automatic prediction and detection of
elderly falls in smart homes,” in 2020 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly
iNiS), Dec. 2020, pp. 202–203, doi: 10.1109/iSES50453.2020.00051.
[18] Y. Zhang, M. Qiu, C.-W. Tsai, M. M. Hassan, and A. Alamri, “Health-CPS: healthcare cyber-physical
system assisted by cloud and big data,” IEEE Systems Journal, vol. 11, no. 1, pp. 88–95, Mar. 2017,
doi: 10.1109/JSYST.2015.2460747.
[19] A. Paneerselvam, R. Yaakob, T. Perumal, and E. Marlisah, “Fall detection framework for smart home,”
in 2018 IEEE 7th
Global Conference on Consumer Electronics (GCCE), Oct. 2018, pp. 351–352,
doi: 10.1109/GCCE.2018.8574617.
[20] N. Fares, R. S. Sherratt, and I. H. Elhajj, “Directing and orienting ICT healthcare solutions to address the needs of
the aging population,” Healthcare, vol. 9, no. 2, Feb. 2021, doi: 10.3390/healthcare9020147.
[21] S. Chaudhuri, H. Thompson, and G. Demiris, “Fall detection devices and their use with older adults:
a systematic review,” Journal of Geriatric Physical Therapy, vol. 37, no. 4, pp. 178–196, Oct. 2014,
doi: 10.1519/JPT.0b013e3182abe77.
[22] K. Ozcan, S. Velipasalar, and P. K. Varshney, “Autonomous fall detection with wearable cameras by using relative
entropy distance measure,” IEEE Transactions on Human-Machine Systems, vol. 47, no. 1, pp. 31–39, Feb. 2017,
doi: 10.1109/THMS.2016.2620904.
[23] D. Micucci, M. Mobilio, and P. Napoletano, “UniMiB SHAR: a dataset for human activity recognition using acceler-
ation data from smartphones,” Applied Sciences, vol. 7, no. 10, Oct. 2017, doi: 10.3390/app7101101.
[24] V. George, C. Chariklei, M. Thodoris, P. Matthew, and T. Manolis, “The MobiAct dataset: recognition of activities of
daily living using smartphones,” in International Conference on Information and Communication Technologies for
Ageing Well and e-Health (ICT4AWE), Apr. 2016, pp. 143–151.
[25] A. Davide, G. Alessandro, O. Luca, P. Xavier, and L. R.-O. Jorge, “A public domain dataset for human activity
recognition using smartphones,” in European Symposium on Artificial Neural Networks, Computational Intelligence
and Machine Learning (ESANN), Apr. 2013.
[26] J.-L. Reyes-Ortiz, L. Oneto, A. Samà, X. Parra, and D. Anguita, “Transition-aware human activity recognition using
smartphones,” Neurocomputing, vol. 171, pp. 754–767, Jan. 2016, doi: 10.1016/j.neucom.2015.07.085.
[27] M. Li, G. Xu, B. He, X. Ma, and J. Xie, “Pre-impact fall detection based on a modified zero moment point
criterion using data from kinect sensors,” IEEE Sensors Journal, vol. 18, no. 13, pp. 5522–5531, Jul. 2018,
doi: 10.1109/JSEN.2018.2833451.
[28] J. Howcroft, J. Kofman, and E. D. Lemaire, “Prospective fall-risk prediction models for older adults based on wear-
able sensors,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 10, pp. 1812–1820,
Oct. 2017, doi: 10.1109/TNSRE.2017.2687100.
[29] W. Engel and W. Ding, “Reliable and practical fall prediction using artificial neural network,” in 2017 13th
Inter-
national Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Jul. 2017,
pp. 1867–1871, doi: 10.1109/FSKD.2017.8393052.
[30] Y. Wang, K. Wu, and L. M. Ni, “WiFall: device-free fall detection by wireless networks,” IEEE Transactions on
Mobile Computing, vol. 16, no. 2, pp. 581–594, Feb. 2017, doi: 10.1109/TMC.2016.2557792.
[31] K. M. Shahiduzzaman, X. Hei, C. Guo, and W. Cheng, “Enhancing fall detection for elderly with smart helmet in a
cloud-network-edge architecture,” in IEEE International Conference on Consumer Electronics- Taiwan (ICCE-TW),
May 2019.
[32] N. Otanasap, “Pre-impact fall detection based on wearable device using dynamic threshold model,” in Parallel
and Distributed Computing, Applications and Technologies, Dec. 2016, vol. 0, pp. 362–365, doi: 10.1109/PD-
CAT.2016.083.
[33] L. Tong, Q. Song, Y. Ge, and Ming Liu, “HMM-based human fall detection and prediction method using tri-axial
accelerometer,” IEEE Sensors Journal, vol. 13, no. 5, pp. 1849–1856, May 2013, doi: 10.1109/JSEN.2013.2245231.
[34] K. M. Shahiduzzaman, J. Peng, Y. Gao, X. Hei, and W. Cheng, “Towards accurate and robust fall detection for the
elderly in a hybrid cloud-edge architecture,” in IEEE Smart World Congress (SmartWorld), Aug. 2019.
Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4721-4733
13. Int J Elec & Comp Eng ISSN: 2088-8708 ❒ 4733
BIOGRAPHIES OF AUTHORS
Kazi Md Shahiduzzaman received the B.Sc.Eng. degree in Electrical and Electronic
Engineering (EEE) from the Khulna University of Engineering and Technology (KUET), Bangladesh,
in 2010 and the M.Sc. from Hochschule Bremen (University of Applied Sciences Bremen), Germany,
in 2013. Now, He is pursuing a Ph.D. degree in EEE from KUET. He is an Associate Professor at the
Department of EEE, Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. His research interests
include smart health care, elderly daily activity recognition, machine learning techniques, free space
optical communication, cloud-edge-end-based distributed platforms, artificial intelligent load, and
power production forecasting. His research mainly focuses on application-oriented issues. He can be
contacted at email: shahiduzzaman2103751@stud.kuet.ac.bd and shahiduzzaman@jkkniu.edu.bd.
Md. Salah Uddin Yusuf received the B.Sc., M.Sc., and Ph.D. degree in Electri-
cal and Electronic Engineering from Khulna University of Engineering and Technology (KUET),
Bangladesh, in 2001, 2005, and 2016 respectively. Since 2001, he has been with the same depart-
ment of the same University and currently serving as Professor. His research interests mainly focus
on deep learning-based signal, image, and video processing with quality assessment, Face liveness
detection and authentication, and GAN-based Human activity analysis for the modern healthcare
systems. He can be contacted at email: suyusuf@eee.kuet.ac.bd.
EdgeFall: a promising cloud-edge-end architecture for elderly fall care (Kazi Md Shahiduzzaman)