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Wearable devices for classification of inadequate posture at work using neural networks

Barkallah Eya, Freulard Johan, Otis Martin J.-D., Ngomo Suzy, Ayena Johannes C. et Desrosiers Christian. (2017). Wearable devices for classification of inadequate posture at work using neural networks. Sensors, 17, (9), p. 1-24.

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URL officielle: http://dx.doi.org/doi:10.3390/s17092003

Résumé

Inadequate postures adopted by an operator at work are among the most important risk factors in Work-related Musculoskeletal Disorders (WMSDs). Although several studies have focused on inadequate posture, there is limited information on its identification in a work context. The aim of this study is to automatically differentiate between adequate and inadequate postures using two wearable devices (helmet and instrumented insole) with an inertial measurement unit (IMU) and force sensors. From the force sensors located inside the insole, the center of pressure (COP) is computed since it is considered an important parameter in the analysis of posture. In a first step, a set of 60 features is computed with a direct approach, and later reduced to eight via a hybrid feature selection. A neural network is then employed to classify the current posture of a worker, yielding a recognition rate of 90%. In a second step, an innovative graphic approach is proposed to extract three additional features for the classification. This approach represents the main contribution of this study. Combining both approaches improves the recognition rate to 95%. Our results suggest that neural network could be applied successfully for the classification of adequate and inadequate posture.

Type de document:Article publié dans une revue avec comité d'évaluation
Volume:17
Numéro:9
Pages:p. 1-24
Version évaluée par les pairs:Oui
Date:2017
Sujets:Sciences naturelles et génie > Génie > Génie informatique et génie logiciel
Département, module, service et unité de recherche:Départements et modules > Département des sciences appliquées > Module d'ingénierie
Mots-clés:posture, center of pressure, instrumented insole, IMU, supervised classification, feature selection, neural networks, posture, centre de pression, semelle instrumentée, classification contrôlée, fonctionnalité, sélection, les réseaux de neurones
Déposé le:05 sept. 2017 20:52
Dernière modification:05 sept. 2017 20:52
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Creative Commons LicenseSauf indication contraire, les documents archivés dans Constellation sont rendus disponibles selon les termes de la licence Creative Commons "Paternité, pas d'utilisation commerciale, pas de modification" 2.5 Canada.

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