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http://dx.doi.org/10.6109/jkiice.2008.12.11.2105

Principal Component analysis based Ambulatory monitoring of elderly  

Sharma, Annapurna (동서대학교 디자인&전문대학원 유비쿼터스IT학과)
Lee, Hoon-Jae (동서대학교 컴퓨터정보공학부)
Chung, Wan-Young (부경대학교 전자컴퓨터정보통신공학부)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.12, no.11, 2008 , pp. 2105-2110 More about this Journal
Abstract
Embedding the compact wearable units to monitor the health status of a person has been analysed as a convenient solution for the home health care. This paper presents a method to detect fall from the other activities of daily living and also to classify those activities. This kind of ambulatory monitoring of the elderly and people with limited mobility can not only provide their general health status but also alarms whenever an emergency such as fall or gait has been occurred and a help is needed. A timely assistance in such a situation can reduce the loss of life. This work shows a detailed analysis of the data received from a chest worn sensor unit embedding a 3-axis accelerometer and depicts which features are important for the classification of human activities. How to arrange and reduce the features to a new feature set so that it can be classified using a simple classifier and also improving the classification resolution. Principal component analysis (PCA) has been used for modifying the feature set and afterwards for reducing the size of the same. Finally a Neural network classifier has been used to analyse the classification accuracies. The accuracy for detection of fall events was found to be 86%. The overall accuracy for the classification of Activities or daily living (ADL) and fall was around 94%.
Keywords
3-axis Accelerometer; Human Activity classification; Principal component analysis; Neural network;
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