Analysis of Repetitive Motion in Manual Material Handling Systems Using a Digital Twin Framework
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Manual material handling accounts for more than 122,000 workplace injuries at U.S. One of the major reasons for injuries in the workplace is due to accidents caused by improper execution of the material handling fundamental moves. This may lead to serious musculoskeletal disorders. Research has been carried out to analyze the musculoskeletal disorders, but there are only very few related to manual material handling. This research proposes a methodology to analyze the quality of motion during lifting task performed in the manual material handling environment. The methodology consists of a motion capture environment, a system of sensors, a processor that collects time series data, and a data analysis module. Using motion capture cameras, data is collected on a variety of human subjects performing manual lifting task related to a material handling activity. The parameters for lifting experiment are obtained from the Snook’s table. The collected data are analyzed through Dynamic Time Warping (DTW) technique which will compare the similarities between two motion sequences. At the end, the quality of the motion is analyzed through quality control charts which will provide the behavior of each motion. This research has potential impact for contribution in the manual material handling industry. Using the latest developments in motion capture technology and data analytics, the analysis of the quality of motion will enable an industry to modify the human motion operations that are injurious to the operator and also help eliminate the non-value-added motions from the operations.