Machine Learning Techniques for Motion Analysis of Fatigue from Manual Material Handling Operations Using 3D Motion Capture Data

Abstract

Industrial Revolution 4.0 is defined as the interconnection of Information, Communications Technologies (ICT), and factory floor workers. Workers in the material handling industry are often subject to repetitive motions that cause exhaustion (or fatigue) which leads to work-related musculoskeletal disorders (WMSDs). The most common repetitive motions are lifting, pulling, pushing, carrying and walking with load. In this research data is collected as time-stamped motion data using infrared cameras at a rate of 100Hz while a subject performs one of the repetitive motions (i.e. lifting). The data is a combination of xyz-coordinates of 39 reflective markers. This results in 117 data points for each frame captured. Since these motions occur over time for a duration of time, this data is used as input to a time-series machine learning (ML) model such as Recurrent Neural Network (RNN). Using this model, this paper evaluates machine learning techniques, based on RNN, to evaluate the fatigue factor caused by repetitive motions.