An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
A cost minimization approach to human behavior recognition
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
An optimum accelerometer configuration and simple algorithm for accurately detecting falls
BioMed'06 Proceedings of the 24th IASTED international conference on Biomedical engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Extracting a diagnostic gait signature
Pattern Recognition
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Behavior Analysis Based on Coordinates of Body Tags
AmI '09 Proceedings of the European Conference on Ambient Intelligence
Automatic detection of human fall in video
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Semantic ambient media: From ambient advertising to ambient-assisted living
Multimedia Tools and Applications
On designing interactivity awareness for ambient displays
Multimedia Tools and Applications
Multimedia Tools and Applications
Towards mobile language evolution exploitation
Multimedia Tools and Applications
Assisted living solutions for the elderly through interactive TV
Multimedia Tools and Applications
Multimedia Tools and Applications
Home-based health monitoring of the elderly through gait recognition
Journal of Ambient Intelligence and Smart Environments - Home-based Health and Wellness Measurement and Monitoring
Journal of Ambient Intelligence and Smart Environments
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This paper proposes a system for the early automatic recognition of health problems that manifest themselves in distinctive form of gait. Purpose of the system is to prolong the autonomous living of the elderly at home. When the system identifies a health problem, it automatically notifies a physician and provides an explanation of the automatic diagnosis. The gait of the elderly user is captured using a motion-capture system, which consists of body-worn tags and wall-mounted sensors. The positions of the tags are acquired by the sensors and the resulting time series of position coordinates are analyzed with machine-learning algorithms in order to recognize a specific health problem. Novel semantic features based on medical knowledge for training a machine-learning classifier are proposed in this paper. The classifier classifies the user's gait into: 1) normal, 2) with hemiplegia, 3) with Parkinson's disease, 4) with pain in the back and 5) with pain in the leg. The studies of 1) the feasibility of automatic recognition and 2) the impact of tag placement and noise level on the accuracy of the recognition of health problems are presented. The experimental results of the first study (12 tags, no noise) showed that the k-nearest neighbors and neural network algorithms achieved classification accuracies of 100%. The experimental results of the second study showed that classification accuracy of over 99% is achievable using several machine-learning algorithms and 8 or more tags with up to 15 mm standard deviation of noise. The results show that the proposed approach achieves high classification accuracy and can be used as a guide for further studies in the increasingly important area of Ambient Assisted Living. Since the system uses semantic features and an artificial-intelligence approach to interpret the health state, provides a natural explanation of the hypothesis and is embedded in the domestic environment of the elderly person; it is an example of the semantic ambient media for Ambient Assisted Living.