Context aware adaptable approach for fall detection bases on Smart textile [r-libre/1055]

Mezghani, Neila; Ouakrim, Youssef; Islam, Md Rabaul; Yared, Rami, & Abdulrazak, Bessam (2017). Context aware adaptable approach for fall detection bases on Smart textile. In 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (p. 473 - 476). New Jersey, États-Unis : IEEE Engineering in Medicine and Biology Society (EMBS). ISBN 978-1-5090-4179-4 https://doi.org/10.1109/BHI.2017.7897308

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Item Type:	Papers in Conference Proceedings
Refereed:	Yes
Status:	Published
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).
Official URL:	https://ieeexplore.ieee.org/document/7897308
Depositor:	Mezghani, Neila
Owner / Manager:	Neila Mezghani
Deposited:	17 Jan 2017 16:31
Last Modified:	28 Oct 2019 12:42

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