Abstract
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.
Highlights
Manual lifting tasks are very common in a variety of workplaces [1] and have been demonstrated to influence the occurrence of musculoskeletal problems such as work-related low-back disorders (WLBDs) [2,3,4]
The aims of this study were to verify the sensitivity of kinematic data to the risk level and to test the ability of machine-learning techniques (ANNs) to map kinematic features on lifting index (LI) levels so leading to a reliable biomechanical risk estimation
The training sets, numbers of hidden layers, and numbers of neurons in each hidden layer influence the artificial neural networks (ANNs) performance: the best performances were obtained by using energy consumption data derived from upper body and load, energy and smoothness data derived from the upper body-load complex, and, as expected, by considering all the energy and smoothness features
Summary
Manual lifting tasks are very common in a variety of workplaces [1] and have been demonstrated to influence the occurrence of musculoskeletal problems such as work-related low-back disorders (WLBDs) [2,3,4]. Over the past three decades, a growing effort has been made to evaluate the effectiveness of ergonomic interventions in preventing and reducing the risk of developing WLBDs. Among the proposed quantitative methods, the revised National Institute for Occupational Safety and Health (NIOSH) lifting equation (RNLE) [3,5,6,7,8,9] is an established means to assess risk of low back pain (LBP). In order to overcome equation and parameter restrictions [10,11,12,13,14,15,16,17], to increase the accuracy and minimize job misidentification [10,18] and to improve the identification of the relationship between WLBDs and risk factors [19], wearable monitoring devices have been proposed for biomechanical risk assessment [20,21].
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