Wearable Devices for Classification of Inadequate Posture at Work Using Neural Networks
by
Eya Barkallah 1, Johan Freulard 1, Martin J. -D. Otis 1
, Suzy Ngomo 2, Johannes C. Ayena 1,* and Christian Desrosiers 3
Eya Barkallah 1, Johan Freulard 1, Martin J. -D. Otis 1, Suzy Ngomo 2, Johannes C. Ayena 1,* and Christian Desrosiers 3
1
Laboratory of Automation and 3D Multimodal Intelligent Interaction (LAIMI), Department of Applied Sciences, University of Quebec at Chicoutimi (UQAC), 555 Boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada
2
Laboratory of Automation and 3D Multimodal Intelligent Interaction (LAIMI), Department of Health Sciences, University of Quebec at Chicoutimi (UQAC), 555 Boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada
3
Department of Software and IT Engineering, École de Technologie Supérieure (ÉTS), 1100 Rue Notre-Dame Ouest, Montreal, QC H3C 1K3, Canada
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(9), 2003; https://doi.org/10.3390/s17092003
Received: 31 July 2017 / Revised: 20 August 2017 / Accepted: 30 August 2017 / Published: 1 September 2017
(This article belongs to the Special Issue Wearable and Ambient Sensors for Healthcare and Wellness Applications)
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.
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Keywords:
posture; center of pressure; instrumented insole; IMU; supervised classification; feature selection; neural networks
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MDPI and ACS Style
Barkallah, E.; Freulard, J.; Otis, M.J.-D.; Ngomo, S.; Ayena, J.C.; Desrosiers, C. Wearable Devices for Classification of Inadequate Posture at Work Using Neural Networks. Sensors 2017, 17, 2003.
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