Context aware adaptable approach for fall detection bases on Smart textile
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Author |
Neila Mezghani, Youssef Ouakrim, Md R Islam, Rami Yared, Bessam Abdulrazak |
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Published in |
2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2017. |
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Abstract |
Fall detection is very important to provide adequate interventions for aging people in risk situations. Existing techniques focus on detecting falls using wearable or ambient sensors. However, they do not consider fall orientations. In this paper, we present our novel fall detection system based on smart textiles and machine learning techniques. Using a non-linear support vector machine, we determine the fall orientation which will be helpful to study the impact of a fall according to its orientation. Additionally, we classify falls based on their orientations among 11 classes (moving upstairs, moving downstairs, walking, running, standing, fall forward, fall backward, fall right, fall left, lying, sitting). Results show the reliability of the proposed approach for falls detection (98% of accuracy, 97.5% of sensitivity and 98.5% specificity) and also for fall orientation (98.5% of accuracy). |
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Link to full paper |
