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

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number103693
JournalApplied Ergonomics
Volume101
DOIs
StatePublished - May 2022

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Physical Therapy, Sports Therapy and Rehabilitation
  • Safety, Risk, Reliability and Quality
  • Engineering (miscellaneous)

Fingerprint

Dive into the research topics of 'Classifying hazardous movements and loads during manual materials handling using accelerometers and instrumented insoles'. Together they form a unique fingerprint.

Cite this