Applied Ergonomics

Classifying hazardous movements and loads during manual materials handling using accelerometers and instrumented insoles

MitjaTrkov, Duncan T. Stevenson, Andrew S. Merryweather

Department of Mechanical Engineering, Rowan University, Glassboro


Manual material handling, Activity classification, Lifting load, frequency estimation


Improper manual material handling (MMH) techniques are shown to lead to low back pain, the most common work-related musculoskeletal disorder. Due to the complex nature and variability of MMH and obtrusiveness and subjectiveness of existing hazard analysis methods, providing systematic, continuous, and automated risk assessment is challenging. We present a machine learning algorithm to detect and classify MMH tasks using minimally-intrusive instrumented insoles and chest-mounted accelerometers. Six participants performed standing, walking, lifting/lowering, carrying, side-to-side load transferring (i.e., 5.7 kg and 12.5 kg), and pushing/pulling. Lifting and carrying loads as well as hazardous behaviors (i.e., stooping, overextending and jerky lifting) were detected with 85.3%/81.5% average accuracies with/without chest accelerometer. The proposed system allows for continuous exposure assessment during MMH and provides objective data for use with analytical risk assessment models that can be used to increase workplace safety through exposure estimation.

Moticon's Summary

This study aimed to identify and classify manual material handling activities using a machine learning algorithm and wearable sensor data in order to derive information on exposure and assess injury risk of workers. Manual material is a crucial aspect in terms of overexertion of workers which is the leading cause of non-fatal work related injuries. Participants were asked to perform a series of manual material handling activities which served to collect input data for the machine learning algorithm. Participants wore Moticon sensor insoles to collect acceleration, center of pressure and ground reaction force data at the foot as well as a chest mounted accelerometer to obtain further acceleration data. Data was subsequently filtered and pre processed to be input into selected machine learning classifiers. The found that their system sufficiently able detect different manual material handling activities and suggest that this could serve as a means to obtain information on worker exposure in real occupational settings.

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