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, vol. 17, nº 9.
Compte des citations dans Scopus : 25.
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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, révisé par les pairs |
|---|---|
| Informations complémentaires: | Thématique du numéro : Wearable and Ambient Sensors for Healthcare and Wellness Applications |
| Professeur: | Professeur Desrosiers, Christian |
| Affiliation: | Génie logiciel et des technologies de l'information |
| Date de dépôt: | 30 oct. 2017 19:33 |
| Dernière modification: | 16 oct. 2020 18:39 |
| URI: | https://espace2.etsmtl.ca/id/eprint/15796 |
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