Proactive prevention of work-related musculoskeletal disorders using a motion capture system and time series machine learning
Luís Miguel Matos, Paula Dias, Arthur Matta, Dário Machado, Rosane Sampaio, André Pilastri, Paulo Cortez
Abstract
In this paper, we propose a proactive method to prevent Work-related MusculoSkeletal Disorders (WMSDs) in manufacturing industries. The integrated method includes a Motion Capture System (MCS) for data collection, a Time Series Forecasting (TSF) module using Machine Learning (ML) algorithms, a WMSD risk assessment module based on ergonomic standards, and a safety mechanism (e.g., alarm sound). We evaluated the method by analyzing shoulder abduction, rotation, and flexion movements of 12 participants working with textile machines. The computational experiments included a comparison of four ML algorithms and a baseline Naive method using a 12-fold participant cross-validation approach. Overall, the best Ahead-of-Time (AoT) TSF and WMSD risk detection empirical results were obtained by a Support Vector Machine (SVM), which required a reasonable training computational effort and provides an interesting performance for AoT TSF and high risk WMSD detection. • A proactive method is proposed to prevent Work-related MusculoSkeletal Disorders (WMSDs). • Machine Learning (ML) was used to forecast Ahead-of-Time (AoT) angular movements. • Standard ergonomics were adopted to detect upper limb high risk WMSD of 12 textile workers. • Best empirical results provided by a Support Vector Machine (SVM).