This document discusses the development of an Android application for physical activity recognition using the accelerometer sensor. It provides background on the Android operating system and its open development environment. It then summarizes relevant research papers on activity recognition using mobile sensors. The document outlines the process of collecting and labeling accelerometer data from smartphone sensors during different physical activities. Features are extracted from the sensor data and several machine learning classifiers are evaluated for activity recognition. The application will recognize activities and track metrics like calories burned, distance traveled, and implement fall detection and medical reminders.
human activity recognition using smartphones.pptxSURAJSAMANTARAY3
This document discusses human activity recognition using smartphones. The goal is to design a simple, lightweight, and accurate system that can recognize six basic human activities - walking, jogging, moving upstairs, downstairs, running, and sleeping - using a supervised learning model and data from the smartphone's accelerometer sensor. A neural network approach is proposed to classify activities based on patterns in the sensor data. Potential benefits include monitoring health, discovering activity patterns, and detecting activities in real-time.
This document summarizes a student project on human activity recognition using smartphones. A group of 4 students submitted the project to partially fulfill requirements for a Bachelor of Technology degree in computer science and engineering. The project involved developing a system to recognize human activities using the accelerometer and gyroscope sensors in smartphones. Various machine learning algorithms were tested and evaluated on experimental data collected from smartphone sensors. The goal of the project was to create an accurate and lightweight activity recognition system for smartphones, while also exploring active learning methods to reduce the amount of labeled training data needed.
The document discusses gesture recognition technology. It describes how cameras can read human body movements and communicate that data to computers to interpret gestures. Gestures can be used as inputs to control devices or applications. The document outlines different types of gestures, image processing techniques used, input devices like gloves and cameras, challenges, and potential uses like sign language recognition and immersive gaming.
Human Activity Recognition using Smartphone's sensor Pankaj Mishra
Human activity recognition plays significant role in medical field and in security system. In this project we have design a model which recognize a person’s activity based on Smartphone.
A 3- dimensional Smartphone sensor named accelerometer and gyroscope is used to collect time series signal, from which 26 features are generated in time and frequency domain. The activities are classified using 2 different dormant learning method i.e. k-nearest neighbor algorithm, decision tree algorithm.
The Gesture Recognition Technology is rapidly growing technology and this PPT describes about the working of gesture recognition technology,the sub fields in it, its applications and the challenges it faces.
Gestures are an important form of non-verbal communication between humans and can also be used to create interfaces between humans and machines. There are several types of gestures including emblems, sign languages, gesticulation and pantomimes. Gesture recognition allows humans to interact with computers through motions of the body, especially hand movements. Some methods of gesture recognition include device-based techniques using sensors on gloves, vision-based techniques using cameras, and controller-based techniques using motion controllers. Gesture recognition has applications in areas such as virtual controllers, sign language translation, game interaction and robotic assistance.
human activity recognization using machine learning with data analysisVenkat Projects
Human activity recognition, or HAR for short, is a broad field of study concerned with identifying the specific movement or action of a person based on sensor data.
The sensor data may be remotely recorded, such as video, radar, or other wireless methods. It contains data generated from accelerometer, gyroscope and other sensors of Smart phone to train supervised predictive models using machine learning techniques like SVM , Random forest and decision tree to generate a model. Which can be used to predict the kind of movement being carried out by the person, which is divided into six categories walking, walking upstairs, walking down-stairs, sitting, standing and laying?
MLM and SVM achieved accuracy of more than 99.2% in the original data set and 98.1% using new feature selection method. Results show that the proposed feature selection approach is a promising alternative to activity recognition on smart phones.
human activity recognition using smartphones.pptxSURAJSAMANTARAY3
This document discusses human activity recognition using smartphones. The goal is to design a simple, lightweight, and accurate system that can recognize six basic human activities - walking, jogging, moving upstairs, downstairs, running, and sleeping - using a supervised learning model and data from the smartphone's accelerometer sensor. A neural network approach is proposed to classify activities based on patterns in the sensor data. Potential benefits include monitoring health, discovering activity patterns, and detecting activities in real-time.
This document summarizes a student project on human activity recognition using smartphones. A group of 4 students submitted the project to partially fulfill requirements for a Bachelor of Technology degree in computer science and engineering. The project involved developing a system to recognize human activities using the accelerometer and gyroscope sensors in smartphones. Various machine learning algorithms were tested and evaluated on experimental data collected from smartphone sensors. The goal of the project was to create an accurate and lightweight activity recognition system for smartphones, while also exploring active learning methods to reduce the amount of labeled training data needed.
The document discusses gesture recognition technology. It describes how cameras can read human body movements and communicate that data to computers to interpret gestures. Gestures can be used as inputs to control devices or applications. The document outlines different types of gestures, image processing techniques used, input devices like gloves and cameras, challenges, and potential uses like sign language recognition and immersive gaming.
Human Activity Recognition using Smartphone's sensor Pankaj Mishra
Human activity recognition plays significant role in medical field and in security system. In this project we have design a model which recognize a person’s activity based on Smartphone.
A 3- dimensional Smartphone sensor named accelerometer and gyroscope is used to collect time series signal, from which 26 features are generated in time and frequency domain. The activities are classified using 2 different dormant learning method i.e. k-nearest neighbor algorithm, decision tree algorithm.
The Gesture Recognition Technology is rapidly growing technology and this PPT describes about the working of gesture recognition technology,the sub fields in it, its applications and the challenges it faces.
Gestures are an important form of non-verbal communication between humans and can also be used to create interfaces between humans and machines. There are several types of gestures including emblems, sign languages, gesticulation and pantomimes. Gesture recognition allows humans to interact with computers through motions of the body, especially hand movements. Some methods of gesture recognition include device-based techniques using sensors on gloves, vision-based techniques using cameras, and controller-based techniques using motion controllers. Gesture recognition has applications in areas such as virtual controllers, sign language translation, game interaction and robotic assistance.
human activity recognization using machine learning with data analysisVenkat Projects
Human activity recognition, or HAR for short, is a broad field of study concerned with identifying the specific movement or action of a person based on sensor data.
The sensor data may be remotely recorded, such as video, radar, or other wireless methods. It contains data generated from accelerometer, gyroscope and other sensors of Smart phone to train supervised predictive models using machine learning techniques like SVM , Random forest and decision tree to generate a model. Which can be used to predict the kind of movement being carried out by the person, which is divided into six categories walking, walking upstairs, walking down-stairs, sitting, standing and laying?
MLM and SVM achieved accuracy of more than 99.2% in the original data set and 98.1% using new feature selection method. Results show that the proposed feature selection approach is a promising alternative to activity recognition on smart phones.
The document discusses human activity recognition from video data using computer vision techniques. It describes recognizing activities at different levels from object locations to full activities. Basic activities like walking and clapping are the focus. Key steps involve tracking segmented objects across frames and comparing motion patterns to templates to identify activities through model fitting. The DEV8000 development kit and Linux are used to process video and recognize activities in real-time. Applications discussed include surveillance, sports analysis, and unmanned vehicles.
Gesture Recognition Technology-Seminar PPTSuraj Rai
This document provides an overview of gesture recognition technology. It begins with introducing gestures as a form of non-verbal communication and defines gesture recognition as interpreting human gestures through mathematical algorithms. It then discusses the motivation for gesture recognition, including its naturalness and applications in overcoming interaction problems with traditional input devices. The document outlines different types of gestures, input devices like gloves and cameras, challenges like developing standardized gesture languages, and uses like sign language recognition, virtual controllers, and assisting disabled individuals. It concludes with references for further reading.
Gesture recognition is a topic in computer science and language technology which interpret human gestures via mathematical algorithms.
Gestures can originate from any bodily motion or state but commonly originate from the face or hand.
Gesture recognition enables humans to communicate with the machine (HMI) and interact naturally without any mechanical devices.
This document presents an overview of Blue Eyes Technology, which aims to create computational machines that have human-like sensory abilities such as sight and emotion detection. It does this using technologies like an Emotion Mouse that can sense a user's mood based on hand pressure and temperature, as well as eye tracking sensors that allow computers to see where a user is looking. The goal is for computers to be able to understand user emotions and identity to have more natural human-computer interaction. Future applications mentioned include using these sensors in cars, games, and industrial control centers.
The project is about building a human-computer interaction system
using hand gesture by cheap alternative to depth camera. We present
a robust , efficient and real-time technique for depth mapping using
normal 2D -camera and Infrared LED arrays . We use HOG feature
based SVM classifiers to predict hand pose and dynamic hand gestures . The system also tracks hand movements and events like grabbing and
clicking bythe hand.
The document discusses a project to develop a desktop application that converts sign language to speech and text to sign language. It aims to help communicate with deaf people by removing barriers. The team plans to use EmguCV and C# Speech Engine. It has created an application that converts signs to text using image processing. Future work includes completing the software to cover all words in Arabic sign language.
Gesture recognition technology allows for control of devices through hand and body motions. It works by using cameras, sensors and algorithms to interpret gestures and movements. Key applications include controlling smart TVs with hand motions, sign language translation, and assisting disabled individuals. Challenges include variations between individuals, reading motions accurately due to lighting and noise, and lack of standardized gesture languages.
project presentation on mouse simulation using finger tip detection Sumit Varshney
This project presentation describes a virtual mouse interface using finger tip detection. A group of 3 students will design a vision-based mouse that detects hand gestures to control cursor movement and clicks instead of using a physical mouse. The system will use a webcam to capture finger tip motion and apply image processing algorithms like segmentation, denoising, and convex hull analysis to identify gestures and control mouse functions accordingly. The goal is to allow gesture-based computer interaction for applications like presentations to reduce workspace needs.
This project developed a gesture recognition application using machine learning algorithms. The application recognizes gestures without color markers by extracting features from images using Hu moments and training a Hidden Markov Model. Common gestures like "ok" and "peace" were mapped to tasks like switching slides. The system was tested and achieved 60% accuracy. Future work could involve adding more gestures and connecting it to other devices.
The document describes a smart note taker product that allows users to take notes by writing in the air. The notes are sensed and stored digitally. Key features include allowing blind users to write freely, and enabling instructors to write notes during presentations that are broadcast to students. It works using sensors to detect 3D writing motions, which are processed, stored, and can be viewed on a display or sent to other devices. An applet program and database are used to recognize words written in the air and print them. The smart note taker offers advantages over digital pens like ease of use and time savings.
This document discusses hand gesture recognition using an artificial neural network. It aims to classify hand gestures into five categories (pointing one to five fingers) using a supervised feed-forward neural network and backpropagation algorithm. The objective is to facilitate communication for deaf people by automatically translating hand gestures into text. The system requires software like Pandas, Numpy and Matplotlib as well as hardware with a quad core processor and 16GB RAM. It explains key concepts of neural networks like neurons, weights, biases, activation functions and their advantages in handling large datasets and inferring unseen relationships.
This document presents a virtual mouse system that uses computer vision and hand gesture recognition to control the mouse cursor and perform mouse tasks. The system aims to provide a more natural and convenient way to control the computer without requiring physical mouse hardware. It uses a webcam to detect colored fingertips and track hand movements in real-time. Image processing algorithms are employed for tasks like segmentation, denoising, finding the hand center and size, and detecting individual fingertips. Detected gestures are then mapped to mouse functions like cursor movement, left/right clicks, and scrolling. The document outlines the goals, design approach, and implementation details of the system, as well as advantages, limitations, and directions for future work.
Developed Project with 3 more colleagues for Pneumonia Detection from Chest X-ray images using Convolutional Neural Network. Used confusion matrix, Recall, Precision for check the model performance on testing Data
1. The document lists over 100 potential seminar topics in computer science and information technology, ranging from elastic quotas to 3D internet.
2. Some examples include extreme programming, face recognition technology, honeypots, IP spoofing, digital light processing, and cloud computing.
3. The topics cover a wide range of areas including networking, security, hardware, software, interfaces, and applications.
Sensors are devices that measure physical quantities and convert them to signals that can be read by instruments or observers. Modern cellphones contain many sensors like microphones, cameras, GPS, touchscreens, accelerometers, and gyroscopes that have replaced the need for separate devices. The document discusses how common sensors like infrared sensors, accelerometers, gyroscopes, touchscreens, and cameras work, as well as where they are used. It notes that sensors have improved over time by becoming smaller, faster, better, and cheaper due to advances in processing technology and manufacturing.
This document discusses various aspects of human-computer interaction (HCI) and user interface design. It begins by defining HCI and its goals of making systems useful, usable and satisfying to users. It then discusses why good UI design is important, covering both explicit and implicit forms of interaction. The document outlines challenges in areas like ubiquitous access and personalized spaces. It analyzes interfaces for different devices like PCs, mobile phones, games consoles and remote controls. It also covers multimodal interaction, gestures, wearable and implanted devices. Finally, it briefly introduces the human-centered design process.
Gesture recognition technology uses cameras to read human body movements and gestures as a form of input to control devices and applications. A camera captures gestures like hand movements and facial expressions and sends that data to a computer for interpretation. Gesture recognition allows humans to interact with machines naturally without physical devices by using gestures to control cursors, activate menus, or control games and other applications. There are different methods for capturing and interpreting gestures including using wired gloves, depth cameras, stereo cameras, single cameras, or motion controllers.
The document discusses hand gesture recognition. It defines what gestures are and how gesture recognition works by interpreting human gestures through mathematical algorithms. This allows humans to interact with machines naturally without devices. Examples of applications include controlling a smart TV with hand movements and using gestures for gaming. The document outlines the hardware and software needed for gesture recognition, including a webcam, processor, RAM, and operating system. It also provides an overview of the module structure involved in identifying and applying gestures as inputs.
BEHAVIOR-BASED SECURITY FOR MOBILE DEVICES USING MACHINE LEARNING TECHNIQUESijaia
The goal of this research project is to design and implement a mobile application and machine learning techniques to solve problems related to the security of mobile devices. We introduce in this paper a behavior-based approach that can be applied in a mobile environment to capture and learn the behavior of
mobile users. The proposed system was tested using Android OS and the initial experimental results show that the proposed technique is promising, and it can be used effectively to solve the problem of anomaly detection in mobile devices.
This document describes a study that aims to identify user activities from accelerometer data collected on Android smartphones without user consent. The researchers were able to classify activities like walking, running, jumping with up to 93% accuracy using signal processing techniques on the accelerometer data like preprocessing, noise reduction, linearization and smoothing. Feature extraction was then performed on windows of data before classification using algorithms like k-Nearest Neighbor and Naive Bayes. Additional activities like climbing/descending stairs were also detected with reasonable success allowing for customized gesture detection. The study shows sensitive user information can be obtained from smartphone sensors without permission.
The document discusses human activity recognition from video data using computer vision techniques. It describes recognizing activities at different levels from object locations to full activities. Basic activities like walking and clapping are the focus. Key steps involve tracking segmented objects across frames and comparing motion patterns to templates to identify activities through model fitting. The DEV8000 development kit and Linux are used to process video and recognize activities in real-time. Applications discussed include surveillance, sports analysis, and unmanned vehicles.
Gesture Recognition Technology-Seminar PPTSuraj Rai
This document provides an overview of gesture recognition technology. It begins with introducing gestures as a form of non-verbal communication and defines gesture recognition as interpreting human gestures through mathematical algorithms. It then discusses the motivation for gesture recognition, including its naturalness and applications in overcoming interaction problems with traditional input devices. The document outlines different types of gestures, input devices like gloves and cameras, challenges like developing standardized gesture languages, and uses like sign language recognition, virtual controllers, and assisting disabled individuals. It concludes with references for further reading.
Gesture recognition is a topic in computer science and language technology which interpret human gestures via mathematical algorithms.
Gestures can originate from any bodily motion or state but commonly originate from the face or hand.
Gesture recognition enables humans to communicate with the machine (HMI) and interact naturally without any mechanical devices.
This document presents an overview of Blue Eyes Technology, which aims to create computational machines that have human-like sensory abilities such as sight and emotion detection. It does this using technologies like an Emotion Mouse that can sense a user's mood based on hand pressure and temperature, as well as eye tracking sensors that allow computers to see where a user is looking. The goal is for computers to be able to understand user emotions and identity to have more natural human-computer interaction. Future applications mentioned include using these sensors in cars, games, and industrial control centers.
The project is about building a human-computer interaction system
using hand gesture by cheap alternative to depth camera. We present
a robust , efficient and real-time technique for depth mapping using
normal 2D -camera and Infrared LED arrays . We use HOG feature
based SVM classifiers to predict hand pose and dynamic hand gestures . The system also tracks hand movements and events like grabbing and
clicking bythe hand.
The document discusses a project to develop a desktop application that converts sign language to speech and text to sign language. It aims to help communicate with deaf people by removing barriers. The team plans to use EmguCV and C# Speech Engine. It has created an application that converts signs to text using image processing. Future work includes completing the software to cover all words in Arabic sign language.
Gesture recognition technology allows for control of devices through hand and body motions. It works by using cameras, sensors and algorithms to interpret gestures and movements. Key applications include controlling smart TVs with hand motions, sign language translation, and assisting disabled individuals. Challenges include variations between individuals, reading motions accurately due to lighting and noise, and lack of standardized gesture languages.
project presentation on mouse simulation using finger tip detection Sumit Varshney
This project presentation describes a virtual mouse interface using finger tip detection. A group of 3 students will design a vision-based mouse that detects hand gestures to control cursor movement and clicks instead of using a physical mouse. The system will use a webcam to capture finger tip motion and apply image processing algorithms like segmentation, denoising, and convex hull analysis to identify gestures and control mouse functions accordingly. The goal is to allow gesture-based computer interaction for applications like presentations to reduce workspace needs.
This project developed a gesture recognition application using machine learning algorithms. The application recognizes gestures without color markers by extracting features from images using Hu moments and training a Hidden Markov Model. Common gestures like "ok" and "peace" were mapped to tasks like switching slides. The system was tested and achieved 60% accuracy. Future work could involve adding more gestures and connecting it to other devices.
The document describes a smart note taker product that allows users to take notes by writing in the air. The notes are sensed and stored digitally. Key features include allowing blind users to write freely, and enabling instructors to write notes during presentations that are broadcast to students. It works using sensors to detect 3D writing motions, which are processed, stored, and can be viewed on a display or sent to other devices. An applet program and database are used to recognize words written in the air and print them. The smart note taker offers advantages over digital pens like ease of use and time savings.
This document discusses hand gesture recognition using an artificial neural network. It aims to classify hand gestures into five categories (pointing one to five fingers) using a supervised feed-forward neural network and backpropagation algorithm. The objective is to facilitate communication for deaf people by automatically translating hand gestures into text. The system requires software like Pandas, Numpy and Matplotlib as well as hardware with a quad core processor and 16GB RAM. It explains key concepts of neural networks like neurons, weights, biases, activation functions and their advantages in handling large datasets and inferring unseen relationships.
This document presents a virtual mouse system that uses computer vision and hand gesture recognition to control the mouse cursor and perform mouse tasks. The system aims to provide a more natural and convenient way to control the computer without requiring physical mouse hardware. It uses a webcam to detect colored fingertips and track hand movements in real-time. Image processing algorithms are employed for tasks like segmentation, denoising, finding the hand center and size, and detecting individual fingertips. Detected gestures are then mapped to mouse functions like cursor movement, left/right clicks, and scrolling. The document outlines the goals, design approach, and implementation details of the system, as well as advantages, limitations, and directions for future work.
Developed Project with 3 more colleagues for Pneumonia Detection from Chest X-ray images using Convolutional Neural Network. Used confusion matrix, Recall, Precision for check the model performance on testing Data
1. The document lists over 100 potential seminar topics in computer science and information technology, ranging from elastic quotas to 3D internet.
2. Some examples include extreme programming, face recognition technology, honeypots, IP spoofing, digital light processing, and cloud computing.
3. The topics cover a wide range of areas including networking, security, hardware, software, interfaces, and applications.
Sensors are devices that measure physical quantities and convert them to signals that can be read by instruments or observers. Modern cellphones contain many sensors like microphones, cameras, GPS, touchscreens, accelerometers, and gyroscopes that have replaced the need for separate devices. The document discusses how common sensors like infrared sensors, accelerometers, gyroscopes, touchscreens, and cameras work, as well as where they are used. It notes that sensors have improved over time by becoming smaller, faster, better, and cheaper due to advances in processing technology and manufacturing.
This document discusses various aspects of human-computer interaction (HCI) and user interface design. It begins by defining HCI and its goals of making systems useful, usable and satisfying to users. It then discusses why good UI design is important, covering both explicit and implicit forms of interaction. The document outlines challenges in areas like ubiquitous access and personalized spaces. It analyzes interfaces for different devices like PCs, mobile phones, games consoles and remote controls. It also covers multimodal interaction, gestures, wearable and implanted devices. Finally, it briefly introduces the human-centered design process.
Gesture recognition technology uses cameras to read human body movements and gestures as a form of input to control devices and applications. A camera captures gestures like hand movements and facial expressions and sends that data to a computer for interpretation. Gesture recognition allows humans to interact with machines naturally without physical devices by using gestures to control cursors, activate menus, or control games and other applications. There are different methods for capturing and interpreting gestures including using wired gloves, depth cameras, stereo cameras, single cameras, or motion controllers.
The document discusses hand gesture recognition. It defines what gestures are and how gesture recognition works by interpreting human gestures through mathematical algorithms. This allows humans to interact with machines naturally without devices. Examples of applications include controlling a smart TV with hand movements and using gestures for gaming. The document outlines the hardware and software needed for gesture recognition, including a webcam, processor, RAM, and operating system. It also provides an overview of the module structure involved in identifying and applying gestures as inputs.
BEHAVIOR-BASED SECURITY FOR MOBILE DEVICES USING MACHINE LEARNING TECHNIQUESijaia
The goal of this research project is to design and implement a mobile application and machine learning techniques to solve problems related to the security of mobile devices. We introduce in this paper a behavior-based approach that can be applied in a mobile environment to capture and learn the behavior of
mobile users. The proposed system was tested using Android OS and the initial experimental results show that the proposed technique is promising, and it can be used effectively to solve the problem of anomaly detection in mobile devices.
This document describes a study that aims to identify user activities from accelerometer data collected on Android smartphones without user consent. The researchers were able to classify activities like walking, running, jumping with up to 93% accuracy using signal processing techniques on the accelerometer data like preprocessing, noise reduction, linearization and smoothing. Feature extraction was then performed on windows of data before classification using algorithms like k-Nearest Neighbor and Naive Bayes. Additional activities like climbing/descending stairs were also detected with reasonable success allowing for customized gesture detection. The study shows sensitive user information can be obtained from smartphone sensors without permission.
Draft activity recognition from accelerometer dataRaghu Palakodety
This document describes a framework for classifying human activities like standing, walking, and running using data from an accelerometer sensor on a smartphone. It discusses collecting raw sensor data, preprocessing the data through smoothing and feature extraction, training classifiers on extracted features, and classifying new data in real-time. Random forest classification achieved 83.49% accuracy on this activity recognition task using accelerometer data from an Android application.
Human Activity Recognition Using SmartphoneIRJET Journal
The document discusses human activity recognition using smartphone sensors. It proposes using a CNN-LSTM model to classify activities like walking, running, and sitting based on accelerometer and gyroscope sensor data from a smartphone. The CNN extracts features from the sensor data, while the LSTM recognizes sequences of activities over time. The model is implemented in an Android application that recognizes activities in real-time and also counts steps, distance, and calories burned. The application uses built-in smartphone sensors like accelerometer, gyroscope, and pedometer to recognize activities affordably and with high availability without external devices. The CNN-LSTM model achieves accurate activity recognition compared to other machine learning techniques.
The document describes a study that investigates using gestures as a form of authentication on smartwatches. The researchers collected accelerometer data from smartwatches as users performed different gestures. They extracted time and frequency domain features from the data and used k-nearest neighbors and random forest classifiers to distinguish between gestures and identify individual users performing the same gesture. Through 5-fold cross validation experiments, they found it was possible to accurately classify gestures and identify users with error rates comparable or better than previous gait-based authentication studies. This suggests gesture-based authentication on smartwatches is a viable solution.
Mobile devices are becoming more and more powerful. They come with all sorts of wonderful hardware like cpu/gpus, tons of ram and blazing fast download times. Smart phones have become commoditized in a sense. What's the next evolution of mobile? Now these devices are coming with a really solid set of sensors and apis that allow developers to determine a user's context. How does that work? Developers fuse the sensor output to infer context and infer events from the data. This talk will discuss ways to do it, challenges and drawbacks.
This document describes a gait-based authentication system project. The project aims to authenticate individuals based on their unique walking gait using wearable sensors. It discusses implementing gait authentication using machine vision, floor sensors, or wearable sensors. The implementation phases include data gathering, feature extraction, modeling, training, and testing classifiers like neural networks and random forests to identify users based on their gait data. A web portal was created for data collection and evaluation of the gait authentication system.
Behaviometrics: Behavior Modeling from Heterogeneous Sensory Time-SeriesJiang Zhu
Over the decades, we have seen tremendous success in biometrics technologies being used in all types of applications based on the physical attributes of the individual such as face, fingerprints, voice and iris. Inspired by this, we introduce a new concept Mobile Behaviometrics, which uses algorithms and models to measure and quantify unique human behavioral patterns in place of human bio-attributes. Behaviometrics algorithms take multiple data from various sensors as input and fuse them to build behavioral models which are capable of producing application specific quantitative analysis on the unique individuals that were the originators of the data.
[EUC2014] cODA: An Open-Source Framework to Easily Design Context-Aware Andro...Matteo Ferroni
Mobile devices take an important part in everyday life. They are now cheaper and widespread, but still a lot of time is spent by the users to configure them: users adapt to their own device, not vice versa. Can our smartphones do something smarter? In this work, we propose a framework to support the development of context-aware applications for Android devices: the goal of such applications is to reduce as much as possible the interaction with the user, making use of automatic and intelligent components. Moreover, these components should consume as less power and computational resources as possible, being them part of a mobile ecosystem whose battery and hardware are highly constrained. The work implies the study of a methodology that fits the Android framework and the design of a highly extensible software architecture. An open-source framework based on the proposed methodology is then described. Some use cases are finally presented, analyzing the performances and the limitations of the proposed methodology.
Full paper: http://ieeexplore.ieee.org/abstract/document/6962264
Expert System Lecture Notes Chapter 1,2,3,4,5 - Dr.J.VijiPriyaVijiPriya Jeyamani
Chapter 1 Introduction to AI
Chapter 2 Introduction to Expert Systems
Chapter 3 Knowledge Representation
Chapter 4 Inference Methods and Reasoning
Chapter 5 Expert System Design and Pattern Matching
IRJET- Design an Approach for Prediction of Human Activity Recognition us...IRJET Journal
The document proposes a framework for human activity recognition using smartphones. It involves collecting data from a smartphone's accelerometer and gyroscope sensors worn on the waist during various activities of daily living. The data is preprocessed and classified using machine learning algorithms like Naive Bayes, logistic regression, and SVM. The proposed framework first loads and preprocesses the sensor data, then generates features before splitting the data into training and test sets. Various classifiers are applied and evaluated to select the best performing one for activity recognition. The authors conclude that implementing tri-axial acceleration from sensors provides different accuracy for different algorithms, with SVM achieving maximum accuracy in previous work.
Embedded Sensing and Computational Behaviour ScienceDaniel Roggen
Overview of the activities in the Sensor Technology Research Centre, University of Sussex, UK, in wearable technologies and computational behaviour science.
Analysis of Inertial Sensor Data Using Trajectory Recognition Algorithmijcisjournal
This paper describes a digital pen based on IMU sensor for gesture and handwritten digit gesture
trajectory recognition applications. This project allows human and Pc interaction. Handwriting
Recognition is mainly used for applications in the field of security and authentication. By using embedded
pen the user can make hand gesture or write a digit and also an alphabetical character. The embedded pen
contains an inertial sensor, microcontroller and a module having Zigbee wireless transmitter for creating
handwriting and trajectories using gestures. The propound trajectory recognition algorithm constitute the
sensing signal attainment, pre-processing techniques, feature origination, feature extraction, classification
technique. The user hand motion is measured using the sensor and the sensing information is wirelessly
imparted to PC for recognition. In this process initially excerpt the time domain and frequency domain
features from pre-processed signal, later it performs linear discriminant analysis in order to represent
features with reduced dimension. The dimensionally reduced features are processed with two classifiers –
State Vector Machine (SVM) and k-Nearest Neighbour (kNN). Through this algorithm with SVM classifier
provides recognition rate is 98.5% and with kNN classifier recognition rate is 95.5% .
Smart systems aimed at detecting the fall of a person have increased significantly due to recent technological
advances and availability of modular electronics. This work presents the use of em-bedded accelerometer and gyroscope in mobile
phones to accurately detect and classify the type of fall a person is experiencing before suffering an impact. Early classification of
fall type helps in optimizing the algorithm of the fall detection. User acceptance, feasibility and the limitations in the accuracy of
the existing devices have also been considered in this study. High efficiency and low power approaches were emphasized with
wireless capability that enhanced the system per-formance for variety of applications. There is a need of reducing the time for
analyzing the smart algorithms designed. It is also emphasized that this application will be a good platform that can be used to test
various algorithms and multiple sensors at a time with ease and obtain data analysis in a short period
Real Time Vision Hand Gesture Recognition Based Media Control via LAN & Wirel...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
IRJET- Analysing Wound Area Measurement using Android AppIRJET Journal
This document describes an Android app that uses image processing techniques to measure wound areas from digital images. The app first pre-processes images to remove noise and enhance edges. It then uses Sobel edge detection, kernel algorithms, and fuzzy c-means clustering to segment the wound from the image. Pixels within the wound boundary are counted and scaled to calculate the actual wound area. The app was found to accurately measure wound areas in clinical tests to within 90% compared to traditional measurement methods. Future work could expand the technique to other medical imaging applications like fractures or retinal diseases.
Sherlock: Monitoring sensor broadcasted data to optimize mobile environmentijsrd.com
Sherlock is a framework that uses sensors in smartphones to optimize the micro-environment around the phone. It runs as a daemon process and provides finer-grained environmental information to applications through APIs. The goal is to save battery by adapting the phone's behavior based on accurate context, such as dimming the screen when in a pocket or bag. It covers major usage scenarios and can detect if the phone is in the hand, on a desk, etc. using sensors like proximity, accelerometer, gyroscope. This allows applications to provide customized services based on the user's situation.
The document discusses decision trees and the ID3 algorithm. It provides an overview of data mining techniques, including decision trees. It then describes the ID3 algorithm in detail, including how it uses information gain to build decision trees top-down and recursively to classify data. An example of applying the ID3 algorithm to a sample dataset is also provided to illustrate the step-by-step process.
Gait Recognition using MDA, LDA, BPNN and SVMIJEEE
Recognition of any individual is a task to identify the human beings. Human identification using Gait is method to identify an individual by the way he walk or manner of moving on foot of humans. Gait recognition is a type of biometric recognition and related to the behavioral characteristics of biometric recognition. Gait offers ability of distance recognition or at low resolution. In this paper it will present the review of gait recognition system where different approaches and classification categories of Gait recognition like model free and model based approach, MDA, BPNN, LDA, and SVM.
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Dr. Sean Tan, Head of Data Science, Changi Airport Group
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Test Automation with generative AI and Open AI.
UiPath integration with generative AI
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3. The Android Framework
The Android platform is an open platform for mobile devices consisting of an operating system,
applications and middleware
Android gives users the opportunity to build and publish their own applications by providing an open
development environment. Android treats all applications (native and third-party) as equals.
Therefore, having such an open development environment requires security measures to be taken in
order to protect the integrity of the Android platform and the privacy of its users.
4. • Android is an open source mobile operating system with Linux
kernel.
• The Android SDK is installed in to Eclipse.
• Android treats both native and third party applications as the same.
So we can build and develop our own applications easily here.
• The android software development kit includes a set of
development tool such as a debugger, libraries, handset emulator,
documentation, sample code and tutorials.
• Android SDK has a java frame work and a powerful API for the
hardware embedded on smartphones.
Why Android ?
6. Various Android Sensors
The Android has several sensors available:
• Accelerometer
• Orientation
• Ambient Light
• Proximity
• Magnetic Force
Use of these sensors does not require direct user
permission!
9. Our Objective
To explore the Accelerometer as a
measure of context- aware based
applications for physical activity
recognition on the Android
framework.
10. Literature Survey
List Of Paper Studied:
• Paper 1 : Applications of Mobile Activity Recognition
Authors : Jeffrey W. Lockhart, Tony Pulickal AND Gary M. Weis
• Paper 2 : User, Device and Orientation Independent Human Activity
Recognition on
Mobile Phones: Challenges and a Proposal
Authors : Yunus Emre Ustey, Ozlem Durmaz Incel AND Cem Ersoy
• Paper 3 : Simple and Complex Activity Recognition Through
SmartPhones
Authors : Das, B., Krishnan, Narayanan C., Thomas, B.L. AND Cook, D.J
• Paper 4 : Fall Detection by Built-In Tri-Accelerometer of Smartphone
Authors : Yi He, Ye Li AND Shu-Oi Bao
• Paper 5 : Feature Selection Based On Mutual Information For Human
Activity Recognition
Authors : Khan, A., Chehade, N.H., Chieh Chien AND Pottie. G
11. • Paper 6 : Smartphone-based Monitoring System for Activities of Daily
Living for Elderly People and Their Relatives Etc.
Authors : Kazushige Ouchi AND Miwako Doi
• Paper 7 : Environment Feature Extraction and Classification for
Context Aware Physical Activity Monitoring
Authors : Troped, P.J., Evans,J.J. AND Pour,G.M
• Paper 8 : Fall Detection based on movement in Smartphone
Technology
Authors : Gueesang Lee AND Deokjai Cho
• Paper 9 : Activity logging using lightweight classification techniques in
mobile devices
Authors : Henar Martı´n ,Ana M. Bernardos ,Josue´ Iglesias • Jose´ R.Casar
• Paper 10 : Privacy control in smart phones using semantically rich
reasoning and context modeling
Authors : Dibyajyoti Ghosh, Joshi, A., Finin, T. AND Jagtap, P
contd..
12. • Paper 11 : Towards Successful Design of Context-Aware Application
Frameworks to Develop Mobile Patient Monitoring Systems Using
Wireless Sensors
Authors : Al-Bashayreh, M.G. Hashim, N.L. AND Khorma, O.T
• Paper 12 : ActivityMonitor: Assisted Life Using Mobile Phones
Authors : Matti Lyra AND Hamed Ketabdar
13. Comparison among the papers
Paper Parameters Used Algorithm Used/Proposed
Paper 1 Nil Neural networks and J48 decision trees
Autocorrelation, K-nearest neighbors (KNN),
Paper 2 Mean, Variance, Std. Dev, fast Fourier transform (FFT) coefficients
Zero Crossing Rate, Period
Mean, min, max, Std. Dev, Multi-layer Perceptron,
Paper 3 Zero Crossing Rate, correlation Naïve Bayes, Bayesian network, Decision
Table, Best-First
Tree, and K-star
Acceleration due to Signal Magnitude Vector, Signal Magnitude
Paper 4 body movement; 2) Area (SMA), Tilt Angle (T A)
gravitational acceleration,
median filter
Standard deviation, Mean, Tree-based, feature selection algorithm based
Paper 5 Absolute mean, Energy ratio, on mutual information, binary
Ratio of DC to sidelobe, First decision-tree with a naıve Bayes classifier
sidelobe location, Max value,
Short time energy, Correlation
Paper 6 Average, minimum, maximum Stochastic model, Neural Networks, SVM every
and variance, MFCC (Mel- 1 sec.
Frequency
Cepstral Coefficient), RMS
(Root Mean Square) and ZCR
(Zero-Crossing Rate)
14. Paper 7 Mean and sigma K-nearest neighbor
of the Gaussian function
Paper 8 Lower
NIL Threshold (LT) and Upper Threshold (UT).
Paper 9 Mean, Variance, Zero crossing Naıve Bayes,
rate ,75 percentile Decision Table and Decision Tree
Paper 10 NIL NIL
Paper 11 NIL NIL
Paper 12 Average magnitude value, Multi-Layer Perceptron (MLP)
average rate of change,
weighted sum
15. Current Problems
• First and Foremost is the use of Body Worn sensors. Today, In most of
the apps we have found that external sensors are used to detect the
physical movements of a person. Practically it is not possible to carry an
external device with you Sometimes people forget to wear the device.
• In most of the apps Positioning of the device is the concerned for the
success of application i.e. Most of the apps build are position specific of
device. If the device is kept in hands then values will be different from the
values generated when the device is kept in pocket.
• Use of multiple Sensors to achieve the same goal which makes the
application bulky leading to slower processing of the data and also affects
its cost.
16. Restating the Problem
We primarily focused on the Activity Recognition project
Inputs:
-X acceleration
-Y acceleration
-Z acceleration
Desired Outputs:
- Physical Activities (e.g., Running,
Walking)
- Approximate time spans
- Quick detection of change
18. Data Collection Process
The first step to the project was to collect raw accelerometer data and transform it
into features that WEKA, the machine-learning tool that we implemented, used to
train a classifier. To accomplish this, we first took in sensor samples made up of
acceleration readings in the x, y, and z directions and computed their magnitudes.
Labeled all the data manually in terms of running, walking, standing, sitting. To
make the data more accurate data of more than 20 minutes have been taken.
Data gathering was done by performing experiments on four subjects. Each of the
four subjects were asked to collect the data activity one by one by placing
smartphone at the positions mentioned above. Each subject performed the set of 6
activities one by one for the duration of two minutes and the respective data was
recorded in a .csv file in the external storage of the smartphone.
Contd...
20. Feature Extraction
Feature Extraction is the process of extracting key “features” from a
signal. Features will be extracted from every sample window of 512
samples. The following features we Will be using in our project:
1. The Fundamental Frequencies: The average of the three
dominant frequencies of the signal over the sample window.
This was found via a Discrete Fourier Transformation.
2. Average Acceleration: The arithmetic mean of the
acceleration magnitudes over the sample window.
3. Max Amplitude: The maximum acceleration value of the
signal in the sample window.
4. Min Amplitude: The minimum acceleration value of the signal
in the sample window.
21. Classification is the process of labeling unknown patterns based on the knowledge of
known patterns of data. Four different classifiers were used:
K-Nearest Neighbor: Based on the shortest euclidean distance between the
unknown and known data’s feature vector
Naïve Bayes: Assumes the absence of one feature does not disqualify a candidate
(e.g., an object which is red and round is an apple, even if is not known to be a fruit)
J48(Decision Tree): J48 builds decision trees from a set of labeled training data
using the concept of information entropy. It uses the fact that each attribute of the
data can be used to make a decision by splitting the data into smaller subsets.
Random Forest: An ensemble classifier using many decision tree models. It can be
used for classification or regression.
Classification
23. The application will be divided in several modules which will be
implemented time to time.
Module-1 Activity Recognition
Physical Activites like cycling, running,walking, standing etc performed
by the user will be recognized in this module. User will click on the app
icon or start button in the app which make him able to run sensors and
thus his motion wil be detected.
Module-2 Location Based Activity Recognition
In addition with Activity Recognition GPS sensor will be used to find the
location of user also what activity he is performing at that location. This
will help in Fall Detection Module discussed later.
Overall Description
Contd…
24. Module-3 Fall Recognition
In addition with physical Movement Recognition, Fall Recognition will be
there.. Whenever Fall detection will have positive result then alarm will be
raised instatntly and then app will monitor physical activities and if motion
is not detected it will send an emergency message to the guardian
informing about this accident.
Module-4 Physical Activity Chart
The User will be able to see his/her daily physical activity chart i.e. how
much he has done workouts today and what type of physical activity
he/she performed during the day.
Module-5 Calorie Burnt
The user will have the ability to see the calories burnt by him within a day
and how much calories he/she should be burnt to be physically fit.
Contd…
25. Module-6 Medical Reminder
The user can set the medical reminder if he wants. For this he/she have
to feed the prescription in the phone with the timings and the made
reminder active.
Module-7 Distance Travelled
The user have the ability to see the distance he travelled by running or
by cycling or by walking
30. Risk and Mitigation
Risk Description Risk Area Prob Impa RE (P* Risk Mitigati
Id of Risk ( Identify abilit c I) Select on Plan
Risk y t (I) ed if 8 is ‘Y’
Areas for (P) for
your Mitiga
project) ti
on
(Y/N)
Position of mobile Sensor
1. i.e. Whether the readings High High High Taking data by
phone is taken in variation Y considering all the
hand or kept in possible locations.
chest pocket or in
pant’s pocket
2. Battery drainage Hardware High Medi Medium N NIL
um
3. Sending each sms Security Medi Low Medium N NIL
to a hidden 3rd um
Party address
Device
4. Computational limitations Hardware Medi Low Medium Y On cloud storage service.
um
31. Sources
International Journal of Distributed Sensor Networks provided by
Hindawi.com
IEEE Sensors Journal provided by http://www.ieee-
sensors.org/journals.
IJCA Proceedings on International Conference on Recent Trends in
Information Technology and Computer Science 2012
Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE
International Conference
Sensors Applications Symposium (SAS), 2013 IEEE
2012 IEEE RIVF International Conference
IEEE Symposium on Security and Privacy Workshops@2012
ACM Transactions on Knowledge Discovery from Data (TKDD)
ACM Journal of Data and Information Quality