A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
IEEE Pervasive Computing
Designing a Home of the Future
IEEE Pervasive Computing
Recognizing multitasked activities from video using stochastic context-free grammar
Eighteenth national conference on Artificial intelligence
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
A review of smart homes-Present state and future challenges
Computer Methods and Programs in Biomedicine
Evidential fusion of sensor data for activity recognition in smart homes
Pervasive and Mobile Computing
Gene extraction for cancer diagnosis by support vector machines-An improvement
Artificial Intelligence in Medicine
A new research challenge: persuasive technology to motivate healthy aging
IEEE Transactions on Information Technology in Biomedicine
A model for the measurement of patient activity in a hospital suite
IEEE Transactions on Information Technology in Biomedicine
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Mobile Networks and Applications
A new hybrid and dynamic fusion of multiple experts for intelligent porch system
Expert Systems with Applications: An International Journal
Testing classifiers for embedded health assessment
ICOST'12 Proceedings of the 10th international smart homes and health telematics conference on Impact Ananlysis of Solutions for Chronic Disease Prevention and Management
Elderly activities recognition and classification for applications in assisted living
Expert Systems with Applications: An International Journal
Personal and Ubiquitous Computing
Classifier ensemble optimization for human activity recognition in smart homes
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
A survey on smartphone-based systems for opportunistic user context recognition
ACM Computing Surveys (CSUR)
Multi-metric learning for multi-sensor fusion based classification
Information Fusion
Robust sounds of activities of daily living classification in two-channel audio-based telemonitoring
International Journal of Telemedicine and Applications
A home daily activity simulation model for the evaluation of lifestyle monitoring systems
Computers in Biology and Medicine
Longitudinal residential ambient monitoring: correlating sensor data to functional health status
Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare
Hybrid intelligent methods for arrhythmia detection and geriatric depression diagnosis
Applied Soft Computing
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By 2050, about one third of the French population will be over 65. Our laboratory's current research focuses on the monitoring of elderly people at home, to detect a loss of autonomy as early as possible. Our aim is to quantify criteria such as the international activities of daily living (ADL) or the French Autonomie Gerontologie Groupes Iso-Ressources (AGGIR) scales, by automatically classifying the different ADL performed by the subject during the day. A Health Smart Home is used for this. Our Health Smart Home includes, in a real flat, infrared presence sensors (location), door contacts (to control the use of some facilities), temperature and hygrometry sensor in the bathroom, and microphones (sound classification and speech recognition). A wearable kinematic sensor also informs postural transitions (using pattern recognition) and walk periods (frequency analysis). This data collected from the various sensors are then used to classify each temporal frame into one of the ADL that was previously acquired (seven activities: hygiene, toilet use, eating, resting, sleeping, communication, and dressing/undressing). This is done using support vector machines. We performed a 1-h experimentation with 13 young and healthy subjects to determine the models of the different activities, and then we tested the classification algorithm (cross validation) with real data.