Document Type
Article
Version Deposited
Submitted for publication (PrePrint)
Publication Date
5-2022
Publication Title
Applied Ergonomics
DOI
10.1016/j.apergo.2022.103693
Abstract
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.
Recommended Citation
M. Trkov, D.T. Stevenson, & A.S. Merryweather. (2022) Classifying hazardous movements and loads during manual materials handling. Applied Ergonomics 101, article 103693. DOI: 10.1016/j.apergo.2022.103693.
Comments
This is the submitted version of an article to be published in May 2022.