Applied Ergonomics

| 2022

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

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.

Keywords

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

Author/s

Mitja Trkov, Duncan T. Stevenson, Andrew S. Merryweather

Institution / Department

Department of Mechanical Engineering, Rowan University, Glassboro

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The wireless data transmission from OpenGo Sensor Insoles to the mobile device uses intelligent handshake technology to avoid the loss of data packages, even when the connection quality is below 100 %.

Typical wireless range of the Bluetooth Low Energy connection in indoor settings is ≥ 10 m, in in-field settings up to 20 m.

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