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

Mezghani, Neila; Ouakrim, Youssef; Md Rabaul, Islam; Rami, Yard et Abdulrazak, Bessam (sous presse). Context aware adaptable approach for fall detection bases on Smart textile. Dans International Conference on Biomedical and Health Informatics (BHI). IEEE Engineering in Medicine and Biology Society (EMBS).

Fichier(s) associé(s) à ce document :
  PDF - 2017_BHI_14-01-2016.pdf
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Catégorie de document : Communications dans des actes de congrès/colloques
Évaluation par un comité de lecture : Oui
Étape de publication : Accepté (sous presse)
Résumé : 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).
Déposant: Mezghani, Neila
Responsable : Neila Mezghani
Dépôt : 17 janv. 2017 16:31
Dernière modification : 23 nov. 2017 21:12

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