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Open AccessArticle

Lifting Activity Assessment Using Kinematic Features and Neural Networks

1
Department of Engineering, University Roma Tre, Via Vito Volterra 62, 00146 Rome, Italy
2
Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Via Fontana Candida 1, 00078 Monte Porzio Catone, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(6), 1989; https://doi.org/10.3390/app10061989
Received: 12 February 2020 / Revised: 9 March 2020 / Accepted: 10 March 2020 / Published: 14 March 2020
(This article belongs to the Special Issue Applied Biomechanics in Sport, Rehabilitation and Ergonomy)
Work-related low-back disorders (WLBDs) can be caused by manual lifting tasks. Wearable devices used to monitor these tasks can be one possible way to assess the main risk factors for WLBDs. This study aims at analyzing the sensitivity of kinematic data to the risk level changes, and to define an instrument-based tool for risk classification by using kinematic data and artificial neural networks (ANNs). Twenty workers performed lifting tasks, designed by following the rules of the revised NIOSH lifting equation, with an increasing lifting index (LI). From the acquired kinematic data, we computed smoothness parameters together with kinetic, potential and mechanical energy. We used ANNs for mapping different set of features on LI levels to obtain an automatic risk estimation during these tasks. The results show that most of the calculated kinematic indexes are significantly affected by changes in LI and that all the lifting condition pairs can be correctly distinguished. Furthermore, using specific set of features, different topologies of ANNs can lead to a reliable classification of the biomechanical risk related to lifting tasks. In particular, the training sets and numbers of neurons in each hidden layer influence the ANNs performance, which is instead independent from the numbers of hidden layers. Reliable biomechanical risk estimation can be obtained by using training sets combining body and load kinematic features. View Full-Text
Keywords: work-related low-back disorders; biomechanical risk; kinematic; artificial neural networks work-related low-back disorders; biomechanical risk; kinematic; artificial neural networks
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MDPI and ACS Style

Varrecchia, T.; De Marchis, C.; Draicchio, F.; Schmid, M.; Conforto, S.; Ranavolo, A. Lifting Activity Assessment Using Kinematic Features and Neural Networks. Appl. Sci. 2020, 10, 1989.

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