This document presents a framework for collecting and evaluating fall-related data using mobile devices. The framework includes a test client application for mobile devices that allows clinical mobility tests to be performed and acceleration data to be recorded. A proof-of-concept was developed using an iPhone to record acceleration data during mobility tests. The recorded data was then analyzed to evaluate the potential for fall detection using mobile devices. The framework is intended to support integration of various sensor-enabled devices and allow collected data to be accessed for further analysis.
BackHome: Assisting and Telemonitoring People with DisabilitiesEloisa Vargiu
The document summarizes a presentation on a telemonitoring and home support system called BackHome. The system aims to assist people with disabilities transitioning back home after being discharged from institutional care. BackHome uses ambient intelligence and a sensor-based approach to provide physical autonomy, social connectivity, remote monitoring, and cognitive rehabilitation. It also aims to automatically assess users' quality of life by collecting data from sensors and applying data mining techniques. The presentation outlines the motivation, approach, technologies, and functions of the proposed system.
The DemaWare Service-Oriented AAL Platform for People with DementiaYiannis Kompatsiaris
This work presents DemaWare, an Ambient Intelligence platform that targets Ambient Assisted Living for people with Dementia. DemaWare seamlessly integrates diverse hardware (wearable and ambient sensors), as well as soft- ware components (semantic interpretation, reasoning), involved in such context. It also enables both online and offline processes, including sensor analysis and storage of context semantics in a Knowledge Base. Consequently, it orchestrates semantic interpretation which incorporated defeasible logics for uncertainty handling. Overall, the underlying functionality aids clinicians and carers to timely assess and diagnose patients in the context of lab trials, homes or nursing homes.
From Context-awareness to Human Behavior PatternsVille Antila
Ville Antila discusses using smartphones to detect daily routines and human behavior patterns through continuous context logging. Smartphones can sense context through built-in sensors and log location, device usage, physical activity, and Bluetooth snapshots. This data is interpreted to estimate routines like locations visited and detect changes. Example applications include context-adaptive feedback that considers situation suitability, and context-based user interface migration between devices. Challenges include ensuring quality, user awareness of adaptive behavior, and testing context-aware applications in real-world use.
Wearable Computing - Part III: The Activity Recognition Chain (ARC)Daniel Roggen
This document discusses activity recognition from sensor data. It describes how simple binary sensors can provide some information but full activity detection requires interpreting multiple correlated sensor streams using techniques like signal processing, machine learning and reasoning. Key steps in activity recognition systems are preprocessing, segmentation, feature extraction, and classification of sensor data. Challenges include continuous recognition, dealing with variable executions of activities, and separating activities from non-activities.
MindTrek2011 - ContextCapture: Context-based Awareness Cues in Status UpdatesVille Antila
Presentation of an experimental mobile application, which allows users to add different descriptions of context information to their Facebook status updates. The meaningfulness and the usage of different context descriptions were evaluated in a two-week user trial. The results show that the most frequently used awareness cues in the test setting were location, surroundings, friends and activity. The results also indicate that user-defined semantic abstractions of context items (e.g. “home”, “work”) were often more informative and useful than more accurate indicators (e.g. the address or the name of the place). We also found out that using shared context from friends in vicinity (e.g. identifying the people around) needs careful design to overcome the extended privacy implications.
Perception.JS - A Framework for Context Acquisition Processing and PresentationSupun Dissanayake
Perception.js is a framework I have developed for my final research project for my Masters in Computer Science at University of Moratuwa. My research focused on developing a framework that will enable JavaScript developers to write context-awareness applications by enabling them to integrate various devices, gather data from those devices, specify rules for inferencing, and to respond to contextual changes.
ContextCapture: Exploring the Usage of Context-based Awareness Cues in Inform...Ville Antila
In this paper, we investigate the usage of context-based awareness cues in informal information sharing, especially in social networking services. We present an experimental mobile application, which allows users to add different descriptions of context information to their Facebook status updates. The meaningfulness and the usage of different context descriptions were evaluated in a two-week user trial. The results show that the most frequently used awareness cues in the test setting were location, surroundings, friends and activity. The results also indicate that user-defined semantic abstractions of context items (e.g. “home”, “work”) were often more informative and useful than more accurate indicators (e.g. the address or the name of the place). We also found out that using shared context from friends in vicinity (e.g. identifying the people around) needs careful design to overcome the extended privacy implications.
BackHome: Assisting and Telemonitoring People with DisabilitiesEloisa Vargiu
The document summarizes a presentation on a telemonitoring and home support system called BackHome. The system aims to assist people with disabilities transitioning back home after being discharged from institutional care. BackHome uses ambient intelligence and a sensor-based approach to provide physical autonomy, social connectivity, remote monitoring, and cognitive rehabilitation. It also aims to automatically assess users' quality of life by collecting data from sensors and applying data mining techniques. The presentation outlines the motivation, approach, technologies, and functions of the proposed system.
The DemaWare Service-Oriented AAL Platform for People with DementiaYiannis Kompatsiaris
This work presents DemaWare, an Ambient Intelligence platform that targets Ambient Assisted Living for people with Dementia. DemaWare seamlessly integrates diverse hardware (wearable and ambient sensors), as well as soft- ware components (semantic interpretation, reasoning), involved in such context. It also enables both online and offline processes, including sensor analysis and storage of context semantics in a Knowledge Base. Consequently, it orchestrates semantic interpretation which incorporated defeasible logics for uncertainty handling. Overall, the underlying functionality aids clinicians and carers to timely assess and diagnose patients in the context of lab trials, homes or nursing homes.
From Context-awareness to Human Behavior PatternsVille Antila
Ville Antila discusses using smartphones to detect daily routines and human behavior patterns through continuous context logging. Smartphones can sense context through built-in sensors and log location, device usage, physical activity, and Bluetooth snapshots. This data is interpreted to estimate routines like locations visited and detect changes. Example applications include context-adaptive feedback that considers situation suitability, and context-based user interface migration between devices. Challenges include ensuring quality, user awareness of adaptive behavior, and testing context-aware applications in real-world use.
Wearable Computing - Part III: The Activity Recognition Chain (ARC)Daniel Roggen
This document discusses activity recognition from sensor data. It describes how simple binary sensors can provide some information but full activity detection requires interpreting multiple correlated sensor streams using techniques like signal processing, machine learning and reasoning. Key steps in activity recognition systems are preprocessing, segmentation, feature extraction, and classification of sensor data. Challenges include continuous recognition, dealing with variable executions of activities, and separating activities from non-activities.
MindTrek2011 - ContextCapture: Context-based Awareness Cues in Status UpdatesVille Antila
Presentation of an experimental mobile application, which allows users to add different descriptions of context information to their Facebook status updates. The meaningfulness and the usage of different context descriptions were evaluated in a two-week user trial. The results show that the most frequently used awareness cues in the test setting were location, surroundings, friends and activity. The results also indicate that user-defined semantic abstractions of context items (e.g. “home”, “work”) were often more informative and useful than more accurate indicators (e.g. the address or the name of the place). We also found out that using shared context from friends in vicinity (e.g. identifying the people around) needs careful design to overcome the extended privacy implications.
Perception.JS - A Framework for Context Acquisition Processing and PresentationSupun Dissanayake
Perception.js is a framework I have developed for my final research project for my Masters in Computer Science at University of Moratuwa. My research focused on developing a framework that will enable JavaScript developers to write context-awareness applications by enabling them to integrate various devices, gather data from those devices, specify rules for inferencing, and to respond to contextual changes.
ContextCapture: Exploring the Usage of Context-based Awareness Cues in Inform...Ville Antila
In this paper, we investigate the usage of context-based awareness cues in informal information sharing, especially in social networking services. We present an experimental mobile application, which allows users to add different descriptions of context information to their Facebook status updates. The meaningfulness and the usage of different context descriptions were evaluated in a two-week user trial. The results show that the most frequently used awareness cues in the test setting were location, surroundings, friends and activity. The results also indicate that user-defined semantic abstractions of context items (e.g. “home”, “work”) were often more informative and useful than more accurate indicators (e.g. the address or the name of the place). We also found out that using shared context from friends in vicinity (e.g. identifying the people around) needs careful design to overcome the extended privacy implications.
Wearable technologies: what's brewing in the lab?Daniel Roggen
Wearable technologies are being developed for a variety of applications in both research labs and commercial settings. Some key areas of focus include flexible and stretchable electronics; custom wearables for specific sensing needs; activity recognition for tasks like healthcare monitoring and sports analysis; and developing wearables as "smart assistants" that can augment users by constantly sensing their context. Research challenges include miniaturizing components, developing low-power sensing and recognition, and enabling wearables to self-adapt over time through techniques like online user adaptation.
ContextCapture: Using Context-based Awareness Cues to Create Narrative Events...Ville Antila
In this paper we introduce an experimental application to demonstrate the usage of context-based awareness cues in status updates, especially in SNS’s (Social Networking Services). The presented application allows users to add different descriptions of context information to their Twitter messages and Facebook status updates in a narrative format. We have also developed an adapted version of the system including conference-specific context-types such as the timetable of the presentations and indoor-location detection using Bluetooth beacons. One goal for the demonstrator is to explore the practical use of context abstractions in a conference setup and synthesize interesting insight based on the usage patterns during the event.
This document discusses different types of sensors that can be used for wearable computing applications. It describes sensors for measuring physical context like location, activity, and environment as well as internal states like emotions and cognition. Both software sensors from data on devices and hardware sensors are covered. Specific sensor technologies discussed include accelerometers, gyroscopes, inertial measurement units, GPS, radio fingerprints, capacitive sensing, electrooculography, and skin conductance sensors. Examples are given of how sensor data can be fused and analyzed to infer higher level context and activities. Challenges of using sensors on the body are also addressed.
RoutineMaker: Towards End-User Automation of Daily Routines Using SmartphonesVille Antila
People use smartphones in daily activities for accessing and storing information in various situations. In this paper, we present a work in progress for detecting and automating some of these activities. To explore the possible patterns we developed an experimental application to detect daily tasks used by smartphones and analyzed it to provide suggestions for “routines”. We conducted a two-week user study with 10 users to evaluate the approach. During the study the application logged the usage patterns, sent information to the server where it was analysed and clustered. The participants could also automate their smartphone tasks using the analysed data. The findings suggest that people would be willing to automatize tasks given that the approach gives flexibility and expressiveness without too much information overload. Future work includes refining the algorithms based on the gathered real-life data and modifying the interaction design to approach the challenges found with the initial study.
Understanding the Privacy Implications of Using Context-based Awareness Cues ...Ville Antila
Information from the physical world is increasingly being digitalized and shared in social networks. We share our locations, tag photos and add different kinds of informal awareness cues about the physical world to our online communities. In this paper, we investigate the privacy implications of shared context cues in social networking services. We present an experimental mobile application, which allows users to add different descriptions of context information to their Facebook and Twitter status updates. The application was used by 12 persons during a two-week user trial using their own devices and Facebook accounts. The results indicate that user-defined abstractions of context items were often preferred over more accurate indicators due to privacy concerns or discomfort in sharing. We also found out that using shared context from friends in vicinity needs careful design to overcome the extended privacy implications.
This poster introduces an experimental application to demonstrate the usage of context-based awareness cues in status updates, especially in SNS’s (Social Networking Services). The presented application allows users to add different descriptions of context information to their Twitter messages and Facebook status updates in a narrative format.
This document discusses cognitive informatics, which is the intersection of software engineering and cognitive science. It aims to understand human cognition to improve software design and testing. Three reasons for its importance are improving human-computer interfaces, advancing artificial intelligence by understanding human intelligence, and understanding human memory systems. Challenges include multidisciplinary complexity and domain knowledge requirements. Tools used include brain-computer interfaces, eye tracking, and emotion recognition. Software testing can analyze usability and emotions during use. Software design principles include mimicking real-world problems and accommodating changing users. Examples provided are affective games and tutoring systems that adapt based on inferred user emotions.
EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
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.
Ed Safford III MetroCon 2015 Verification, Validation, and Deployment of Hybr...Edward L S Safford III
The document discusses the challenges of verifying and validating complex hybrid neuromorphic systems as their capabilities continue to exceed our ability to adequately test them. As systems become more autonomous and capable of emergent behaviors through deep learning, new assurance methods are needed. A potential solution proposed is a "trainable testbed" that could be trained as an oracle to help determine if tests expose faults. The complexity of systems now being developed, such as reservoir computing and liquid state machines, is rapidly outpacing our ability to verify, validate and control them through traditional means.
Dr. Cathal Gurrin's research group looks at personal "little-big data" from lifelogs collected using body-worn and external sensors. A one-year lifelog from an individual generated over 2 million images, hundreds of hours of video and audio, 3.9 million location points, and hundreds of millions of sensor readings. The challenge is to extract meaningful information and develop prototypes to evaluate extracting semantics from these extensive lifelogs while maintaining privacy.
SenSec: Mobile Application Security through Passive SensingJiang Zhu
The document proposes a smartphone-based behavioral authentication system called SenSec. It collects sensor data to build user behavior models. Features are extracted from the sensor data and used to build risk analysis trees to detect anomalies. When anomalies are detected, a certainty score is broadcast and can trigger authentication for sensitive applications. The system was tested on a dataset of 25 users, achieving over 98% accuracy in user identification. Extensions and integrations with other systems are discussed to enhance security, privacy, and energy efficiency.
With the introduction of Blue Brain technology, which is a reverse engineering, we can overcome all the brain disorders and diseases. Blue Brain is the name of the world’s first virtual brain which makes a machine, function as a human brain. Even after the death of the person the complete functional attribute of a human brain can be stored in that and can be used for further development.
The weekly report discusses progress on using a photo interrupter sensor to count mosquitoes. This week, the student researched photo interrupter sensors, which use an infrared emitter and photodetector to detect objects passing through a slot. There are different types that vary in slot width and depth for detecting different objects. The sensors are widely used for object detection and automatic counting. Next week, the student will design a high performance circuit to count mosquitoes using the photo interrupter sensor for the project.
Neurohacking is the colloquial term for (usually personal or 'DIY') neuroengineering. It is a form of biohacking (qv) focusing on the brain and CNS. Strictly speaking it is any method of manipulating or interfering with the structure and/or function of neurons for improvement or repair.
This paper describes a realization and research on a neural network for a generalized function with real inputs and a binary output (0 or 1). The neural network has been implemented of three different tools - a neural network simulator NeuroPh, logic programming language Visual Prolog and object - oriented programming language Java. The aim is to explore the neural network realization capabilities of the three tools - a neural network simulator, a logical programming environment, and a language for object-oriented programming. For this purpose is selected function with real inputs and binary output (0 or 1) whose values the neural network is trained to predict. The results obtained allow identifying the strengths and weaknesses of the three realized neural networks as well as the environments through which they are realized and tested.
Context-aware mobile computing allows mobile applications to take advantage of contextual information about the user's environment to provide better services. This literature review explores the evolution of context-aware technologies and their impact on society. Key developments include location sensing technologies like GPS and WiFi that provide context data, as well as augmented reality projects like Google Glass and Project Tango that give mobile devices a 3D understanding of space. Context-aware software like Google Now uses this information to deliver the right data to users at the appropriate time.
(ATS6-APP04) Flexible Data Capture for Improved Laboratory ErgonomicsBIOVIA
The document discusses developing mobile applications to allow scientists to capture experimental observations and data in the laboratory using their mobile devices, with an initial proof of concept application developed for Android and Windows 8 devices; it outlines potential use cases and design priorities for flexible data capture and integration with Accelrys ELN and other systems to improve laboratory ergonomics and workflow.
Wearable technologies: what's brewing in the lab?Daniel Roggen
Wearable technologies are being developed for a variety of applications in both research labs and commercial settings. Some key areas of focus include flexible and stretchable electronics; custom wearables for specific sensing needs; activity recognition for tasks like healthcare monitoring and sports analysis; and developing wearables as "smart assistants" that can augment users by constantly sensing their context. Research challenges include miniaturizing components, developing low-power sensing and recognition, and enabling wearables to self-adapt over time through techniques like online user adaptation.
ContextCapture: Using Context-based Awareness Cues to Create Narrative Events...Ville Antila
In this paper we introduce an experimental application to demonstrate the usage of context-based awareness cues in status updates, especially in SNS’s (Social Networking Services). The presented application allows users to add different descriptions of context information to their Twitter messages and Facebook status updates in a narrative format. We have also developed an adapted version of the system including conference-specific context-types such as the timetable of the presentations and indoor-location detection using Bluetooth beacons. One goal for the demonstrator is to explore the practical use of context abstractions in a conference setup and synthesize interesting insight based on the usage patterns during the event.
This document discusses different types of sensors that can be used for wearable computing applications. It describes sensors for measuring physical context like location, activity, and environment as well as internal states like emotions and cognition. Both software sensors from data on devices and hardware sensors are covered. Specific sensor technologies discussed include accelerometers, gyroscopes, inertial measurement units, GPS, radio fingerprints, capacitive sensing, electrooculography, and skin conductance sensors. Examples are given of how sensor data can be fused and analyzed to infer higher level context and activities. Challenges of using sensors on the body are also addressed.
RoutineMaker: Towards End-User Automation of Daily Routines Using SmartphonesVille Antila
People use smartphones in daily activities for accessing and storing information in various situations. In this paper, we present a work in progress for detecting and automating some of these activities. To explore the possible patterns we developed an experimental application to detect daily tasks used by smartphones and analyzed it to provide suggestions for “routines”. We conducted a two-week user study with 10 users to evaluate the approach. During the study the application logged the usage patterns, sent information to the server where it was analysed and clustered. The participants could also automate their smartphone tasks using the analysed data. The findings suggest that people would be willing to automatize tasks given that the approach gives flexibility and expressiveness without too much information overload. Future work includes refining the algorithms based on the gathered real-life data and modifying the interaction design to approach the challenges found with the initial study.
Understanding the Privacy Implications of Using Context-based Awareness Cues ...Ville Antila
Information from the physical world is increasingly being digitalized and shared in social networks. We share our locations, tag photos and add different kinds of informal awareness cues about the physical world to our online communities. In this paper, we investigate the privacy implications of shared context cues in social networking services. We present an experimental mobile application, which allows users to add different descriptions of context information to their Facebook and Twitter status updates. The application was used by 12 persons during a two-week user trial using their own devices and Facebook accounts. The results indicate that user-defined abstractions of context items were often preferred over more accurate indicators due to privacy concerns or discomfort in sharing. We also found out that using shared context from friends in vicinity needs careful design to overcome the extended privacy implications.
This poster introduces an experimental application to demonstrate the usage of context-based awareness cues in status updates, especially in SNS’s (Social Networking Services). The presented application allows users to add different descriptions of context information to their Twitter messages and Facebook status updates in a narrative format.
This document discusses cognitive informatics, which is the intersection of software engineering and cognitive science. It aims to understand human cognition to improve software design and testing. Three reasons for its importance are improving human-computer interfaces, advancing artificial intelligence by understanding human intelligence, and understanding human memory systems. Challenges include multidisciplinary complexity and domain knowledge requirements. Tools used include brain-computer interfaces, eye tracking, and emotion recognition. Software testing can analyze usability and emotions during use. Software design principles include mimicking real-world problems and accommodating changing users. Examples provided are affective games and tutoring systems that adapt based on inferred user emotions.
EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
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.
Ed Safford III MetroCon 2015 Verification, Validation, and Deployment of Hybr...Edward L S Safford III
The document discusses the challenges of verifying and validating complex hybrid neuromorphic systems as their capabilities continue to exceed our ability to adequately test them. As systems become more autonomous and capable of emergent behaviors through deep learning, new assurance methods are needed. A potential solution proposed is a "trainable testbed" that could be trained as an oracle to help determine if tests expose faults. The complexity of systems now being developed, such as reservoir computing and liquid state machines, is rapidly outpacing our ability to verify, validate and control them through traditional means.
Dr. Cathal Gurrin's research group looks at personal "little-big data" from lifelogs collected using body-worn and external sensors. A one-year lifelog from an individual generated over 2 million images, hundreds of hours of video and audio, 3.9 million location points, and hundreds of millions of sensor readings. The challenge is to extract meaningful information and develop prototypes to evaluate extracting semantics from these extensive lifelogs while maintaining privacy.
SenSec: Mobile Application Security through Passive SensingJiang Zhu
The document proposes a smartphone-based behavioral authentication system called SenSec. It collects sensor data to build user behavior models. Features are extracted from the sensor data and used to build risk analysis trees to detect anomalies. When anomalies are detected, a certainty score is broadcast and can trigger authentication for sensitive applications. The system was tested on a dataset of 25 users, achieving over 98% accuracy in user identification. Extensions and integrations with other systems are discussed to enhance security, privacy, and energy efficiency.
With the introduction of Blue Brain technology, which is a reverse engineering, we can overcome all the brain disorders and diseases. Blue Brain is the name of the world’s first virtual brain which makes a machine, function as a human brain. Even after the death of the person the complete functional attribute of a human brain can be stored in that and can be used for further development.
The weekly report discusses progress on using a photo interrupter sensor to count mosquitoes. This week, the student researched photo interrupter sensors, which use an infrared emitter and photodetector to detect objects passing through a slot. There are different types that vary in slot width and depth for detecting different objects. The sensors are widely used for object detection and automatic counting. Next week, the student will design a high performance circuit to count mosquitoes using the photo interrupter sensor for the project.
Neurohacking is the colloquial term for (usually personal or 'DIY') neuroengineering. It is a form of biohacking (qv) focusing on the brain and CNS. Strictly speaking it is any method of manipulating or interfering with the structure and/or function of neurons for improvement or repair.
This paper describes a realization and research on a neural network for a generalized function with real inputs and a binary output (0 or 1). The neural network has been implemented of three different tools - a neural network simulator NeuroPh, logic programming language Visual Prolog and object - oriented programming language Java. The aim is to explore the neural network realization capabilities of the three tools - a neural network simulator, a logical programming environment, and a language for object-oriented programming. For this purpose is selected function with real inputs and binary output (0 or 1) whose values the neural network is trained to predict. The results obtained allow identifying the strengths and weaknesses of the three realized neural networks as well as the environments through which they are realized and tested.
Context-aware mobile computing allows mobile applications to take advantage of contextual information about the user's environment to provide better services. This literature review explores the evolution of context-aware technologies and their impact on society. Key developments include location sensing technologies like GPS and WiFi that provide context data, as well as augmented reality projects like Google Glass and Project Tango that give mobile devices a 3D understanding of space. Context-aware software like Google Now uses this information to deliver the right data to users at the appropriate time.
(ATS6-APP04) Flexible Data Capture for Improved Laboratory ErgonomicsBIOVIA
The document discusses developing mobile applications to allow scientists to capture experimental observations and data in the laboratory using their mobile devices, with an initial proof of concept application developed for Android and Windows 8 devices; it outlines potential use cases and design priorities for flexible data capture and integration with Accelrys ELN and other systems to improve laboratory ergonomics and workflow.
Sensor Observation Service Client for Android Mobile PhonesCybera Inc.
Presentation by Alain Tamayo during the Sensor Web System and Visualization paper session of the Sensor Web Enablement workshop (held during the 2011 Cybera Summit).
Join technology experts from Perfecto Mobile for a discussion about how to prepare for wearables and the implications of including them in your mobile development projects.
Learn more: http://www.perfectomobile.com
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 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.
Zhipeng Zhao is seeking a research-oriented position utilizing his 6 years of experience and 6 publications in computer vision, machine learning, and data mining. He holds a Ph.D. in Computer Science from Rutgers University where he focused on object recognition, motion analysis, and statistical modeling. He also has 2 years of industrial experience applying skills in Java, C/C++, and data mining technologies at IBM.
Overview of usability testing methods for mobile devices and apps. Includes information on usability, explanation of challenges introduced by the mobile context, and practical tools and techniques.
Future of testing – impact of mobile devices somenath nag- calsoft labsSomenath Nag
Over last couple of years, mobile devices have shown a phenomenal growth, at the same time PC industry is on a declining path. Due to this, we are experiencing a paradigm shift on how applications are built, tested, and used by the end users, and has a potential to create a disruption in the traditional way of software development and QA. Though it started with consumers, enterprises are also embracing mobility more and more, especially after the stupendous success of iPad. At the same time enterprises are also facing challenges in the area of provisioning, data management, device management, and security. Mobile devices are also used differently compared to traditional computing platforms. Due to this mobility devices and platforms throw up a new kind of challenges to testing fraternity. Calsoft Labs, with its unique competence and experience of working with leading Software and Hardware companies, has been in the forefront of mobility. Calsoft Labs’ Mobility & Testing practice have been working together for some time to build methodologies, processes, and frameworks to address the challenges arising because of the above mentioned challenges shift and to create a new paradigm in application and product testing.
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 apps can be developed for various platforms including Android, iOS, Blackberry, Windows, and more. Testing mobile apps presents unique challenges due to the diversity of devices, operating systems, and connectivity issues. Key types of mobile app testing include functional testing, port testing across devices, laboratory testing to simulate networks, performance testing for speed and reliability, memory leakage testing, interrupt handling, usability testing, installation testing, security testing, stress testing, localization for different languages and regions, and certification testing required by each platform. Thorough testing across all these areas is needed to ensure mobile apps work as intended on the wide variety of mobile environments.
Rajakumari Thota has over 9 years of experience in software testing of web, mobile, and desktop applications. She has expertise in test automation using Selenium WebDriver, Appium, JMeter, and other tools. Her experience includes test automation framework development, test case writing, defect tracking, test execution and reporting. She is proficient in testing methodologies like test planning, requirement analysis, test design, test execution and defect management.
Rajakumari Thota has over 9 years of experience in software testing. She has expertise in test automation using tools like Selenium, Appium, and JMeter. She has extensive experience testing web and mobile applications, including writing test cases and executing automation test suites. She has worked on projects across various domains and platforms, including testing Android and iOS applications. She is proficient in functional and non-functional testing methodologies.
Activity Monitoring Using Wearable Sensors and Smart PhoneDrAhmedZoha
The document discusses two problems related to real-time activity recognition using data from wearable sensors and mobile phones. For problem 1 of developing an algorithm to recognize exercises from a raw sensor data stream, the solution involves a two-phase learning and recognition process using techniques like filtering, time-windowing, feature extraction and selection, and classification models. For problem 2 of enabling real-time recognition on mobile phones, the document recommends using Android and Java APIs to receive Bluetooth sensor data, train models on servers, and locally recognize activities on phones for efficiency. Key challenges discussed include energy usage, response time, and developing flexible models for different users.
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
Microlearning in crowdsourcing and crowdtasking applicaitonsDenis Havlik
A presentation given by Denis Havlik (AIT) on "Microlearning 7.0" conference (26-27 09 2013, Krems)
It presents the challenges of the crowdsourcing/crowdtasking applications and proposes the way to improve them by integrating the microlearning approaches in the applications.
- The document discusses speeding up mobile development through continuous integration on real devices. It introduces Intuit's Virtual Device Lab (VDL), which allows running automated tests on real mobile devices through a browser.
- VDL addresses challenges developers face from the large number of device and OS combinations by running tests in parallel across many real devices. This reduces the development iteration cycle from days to minutes.
- The document highlights how VDL integrated with Intuit's test automation framework and tools like Cucumber and Calabash allows running tests remotely over WiFi. This provided significant time and cost savings compared to alternatives.
Monitoring energy consumption of smartphonesphonecom
The document summarizes a system called SEMO (Smart Energy Monitoring System) that monitors and analyzes energy consumption of applications on smartphones. SEMO includes an inspector that checks battery information, a recorder that records battery and application information, and an analyzer that analyzes the recorded data. The prototype was implemented as a Java program for Android smartphones and can view energy consumption histories and rankings of applications based on real-time usage. Testing showed SEMO works well to monitor and analyze relative energy consumption of different applications on smartphones.
Ist africa2012 alert system in case of excess drawing of ground water_1Karel Charvat
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Assessment Test Framework for Collecting and Evaluating Fall - Related Data using Mobile Devices
1. Assessment Test Framework for
Collecting and Evaluating
Fall-Related Data
Using Mobile Devices
DI Stefan Almer
July 11th, 2012
Graz University of Technology Central European Institute of Technology
Institute for Information Systems and Computer Media Institute for Rehabilitation and Ambient Assisted Living Technologies
Univ.-Doz. Dipl.-Ing. Dr.techn. Martin Ebner Dipl.-Ing. Dr.techn. Johannes Oberzaucher
Dipl.-Ing. Dr.techn. Josef Kolbitsch
2. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 2
Agenda
• Introduction
• Mobile Devices for Fall Detection
• Assessment Test Framework
• Mobile Device Client
• Evaluation
• Summary
Stefan Almer July 11th, 2012
3. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 3
Introduction
• Motivated by the demographic trend
[van den Broek et al., 2009]
• average age will increase
• impact on healthcare systems, retirement plans
• more people will need assistance or support
• Falls and fall-related injuries
• Mobile Devices
• device of the future: “the steady companion”
Stefan Almer July 11th, 2012
4. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 4
Fall Prevention
[Todd and Skelton, 2004; WHO, 2007; Tremblay Jr. and Barber, 2005; LeMier et al., 2002; BRAID, 2010]
• Common methods
• assessment tests
• adjustment of environment and walking aids
• gait analysis
• education
• exercise/training
• Differ in usage based on context
Stefan Almer July 11th, 2012
5. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 5
Fall Detection
• Five phases of a fall
1) Activity 2) Hard- 3) Free-fall 4) Impact 5) Optional
of daily living predictable event recovery
Fig. 1: Fall Phases [Abbate et al., 2010]
• Classification of fall detection methods [Yu, 2008]
• wearable device / camera-based / ambience device
• Important to differentiate between a fall and
activities of daily living
Stefan Almer July 11th, 2012
6. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 6
Mobile Devices for Fall Detection
[Columbus, 2011; Noury et al., 2007; Kangas et al., 2007]
• Classic approach
• “Individual” devices and sensors
• New approach: Mobile Devices
• equipped with required hardware: accelerometer
• software capabilities to read acceleration data
• Method: measure body acceleration
• fall has higher acceleration
• acceleration threshold to determine fall
• problem: position of sensor
Stefan Almer July 11th, 2012
7. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 7
Test Framework
• Fall detection is complex
• many parameters
• no general fall detection algorithm
• Aim of the framework
• collecting fall-related data
• easily set up of tests settings
• integration with different systems and devices
Stefan Almer July 11th, 2012
8. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 8
Test Framework (cont.)
• Assessment test-based approach
Motion Data
Sensors
User
Test Device Position
Test Type
Sample Rate
Fig. 2: Test Properties and Device Relation
• Provides Interface (API)
• well defined interface
• integrate various devices with different sensors
• stored data can be accessed later
Stefan Almer July 11th, 2012
9. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 9
Framework Architecture [Helic, 2008]
• Based on 3-tier architecture
Data Tier Application Tier Client Tier
Interface
HTTP
Database JDBC
Java (Web service)
Client
(Browser/
iOS)
Static Content HTTP
(Backend)
Fig. 3: Framework Architecture
Stefan Almer July 11th, 2012
10. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 10
Proof-of-Concept
• Integration of different devices
• Mobile Device Client
• demonstrates functionality of the
framework
• shows capabilities and sensor accuracy
• Developed on iOS Platform
• uses the possibility to receive high-
rate continuous motion data
Fig. 4: Apple iOS Client
Stefan Almer July 11th, 2012
11. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 11
Evaluation
• 3 clinical mobility tests were
performed
[Podsiadlo and Richardson, 1991; Whitney et al., 2005; Lewis and Shaw, 2005]
• “Sit-to-Stand 5”, “Timed Up and
Go”, “2-Minute-Walk”
• iPhone 4, worn on hip height,
50Hz sample rate
• Tests analyzed afterwards
Fig. 5: User wears Device while performing test
Stefan Almer July 11th, 2012
12. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 12
Evaluation (cont.)
• Movement analysis of recorded gait data
• sensor accurate enough to perform fall detection
• Data analysis and future extraction (peak detection,
knowledge methods, statistical analysis)
1.4
3 m straight walk turn phase
1.2 3 m straight walk
sit phase
sit down phase sit phase
1
0.8
SVM [g]
0.6
0.4
0.2
0
0 100 200 300 400 500 600 700 800 900 1000
datapoints [n] - movement data recorded with 50 Hz
Fig.6: Timed Up and Go Test (Individual Phases)
Stefan Almer July 11th, 2012
13. Assessment Test Framework for Collecting and Evaluating Fall-Related Data Using Mobile Devices 13
Summary
• One framework for various devices
• support for different sensors
• data collected or later analysis
• Web-Service API
• Backend for administrative tasks
• Proof-of-Concept
• Mobile Device Client (iOS platform)
• hardware well-suited for fall detection
Stefan Almer July 11th, 2012
14. Assessment Test Framework for
Collecting and Evaluating Fall-Related Data
Using Mobile Devices
Stefan Almer
stefan@almer.cc
@stefalmer
Slides available at: http://elearningblog.tugraz.at
Graz University of Technology Central European Institute of Technology
Institute for Information Systems and Computer Media Institute for Rehabilitation and Ambient Assisted Living Technologies
http://www.iicm.tugraz.at http://www.ceit.at
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[Noury et al., 2007] Noury, N., Fleury, A., Rumeau, P., Bourke, A.K., Ó Laighin, G., Rialle V., Lundy, J.E., 2007. Fall Detection - Principles and Methods. In Proc. of the 29th
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Society, 39(2), pages 142–148. ISSN 00028614. http://www.ncbi.nlm.nih.gov/pubmed/1991946. Last accessed October 1, 2011.
[Todd and Skelton, 2004] Todd, C., Skelton, D., 2004. What are the main risk factors for falls among older people and what are the most effective interventions to prevent
these falls? Technical Report, WHO Regional Office for Europe. http://www.euro.who.int/document/E82552.pdf. Last accessed November 2, 2011.
[Tremblay Jr. and Barber, 2005] Tremblay Jr., K.R., Barber, C.E., 2005. Preventing Falls in the Elderly. http://www.ext.colostate.edu/pubs/consumer/10242.pdf. Last accessed
November 2, 2011.
[van den Broek et al., 2009] van den Broek, G., Cavallo, F., Odetti, L., Wehrmann, C., 2009. Ambient Assisted Living Roadmap. http://www.aaliance.eu/public/documents/
aaliance-roadmap/aaliance-aal-roadmap.pdf. Last accessed October 20, 2011.
[WHO, 2007] WHO, 2007. WHO Global Report on Falls Prevention in Older Age. http://www.who.int/ageing/publications/Falls_prevention7March.pdf. Last accessed
November 2, 2011.
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[Yu, 2008] Yu, X., 2008. Approaches and Principles of Fall Detection for Elderly and Patient. In Proc. of the 10th International Conference on e-Health Networking,
Applications and Services, pages 42–47. IEEE. doi:10.1109/HEALTH.2008.4600107.