An automatic approach to assess biomechanical risk using machine learning algorithms and inertial sensors.

Abstract

Work-related musculoskeletal disorders represent a significant occupational health issue. These disorders encompass a range of conditions resulting from specific risk factors associate to manual material handling such as: intensity, repetition, and duration. Over the years, several observational methodologies have been developed to assess biomechanical risk, but their limits depend mainly on clinicians' subjective assessment. For this reason, wearable sensors coupled with artificial intelligence have recently been integrated in the occupational ergonomic field. This study aimed to develop a new technological methodology-based on machine learning algorithms and inertial wearable sensors-able to automatically discriminate biomechanical risk associated with lifting loads. Ten healthy volunteers were enrolled in this study performing specific weight-lifting tasks wearing two inertial measurement units on the sternum and lumbar region. The acquired inertial signals were appropriately processed to extract several features in the time-domain and frequency-domain which have been used as input to several machine learning algorithms. Excellent results in discriminating biomechanical risk classes were obtained reaching accuracies and areas under the receiver operating characteristic curve above 86% and 95%, respectively. In addition, the sternum emerged as the most informative body landmark, while the mean absolute value was identified as the most informative feature. Future investigations on a larger study population could confirm the potential of the proposed automatic procedure to be used in the workplace in combination with well-established methodologies.