Affiliations: [a] Department of Kinesiology, University of Waterloo, Waterloo, Canada
| [b] School of Human Kinetics, University of Ottawa, Ottawa, Ontario, Canada
Correspondence:
[*]
Address for correspondence: Steven L. Fischer, Department of Kinesiology, Faculty of Applied Health Sciences, 200 University Avenue West, Waterloo, Ontario, Canada N2 L 3G1. Tel.: +1 519 888 4567, ext. 30368; [email protected].
https://orcid.org/0000-0002-3347-5403
Abstract: BACKGROUND:Physical employment standards (PES) ensure that candidates can demonstrate the physical capacity required to perform duties of work. However, movement competency, or an individual’s movement strategy, can relate to injury risk and safety, and therefore should be considered in PES. OBJECTIVE:Demonstrate the utility of using artificial intelligence (AI) to detect risk-potential of different movement strategies within PES. METHODS:Biomechanical analysis was used to calculate peak flexion angles and peak extensor moment about the lumbar spine during participants’ performance of a backboard lifting task. Lifts performed with relatively lower and higher exposure to postural and moment loading on the spine were characterized as “low” or “high” exposure, respectively. An AI model including principal component and linear discriminant analyses was then trained to detect and classify backboard lifts as “low” or “high”. RESULTS:The AI model accurately classified over 85% of lifts as “low” or “high” exposure using only motion data as an input. CONCLUSIONS:This proof-of-principle demonstrates that movement competency can be assessed in PES using AI. Similar classification approaches could be used to improve the utility of PES as a musculoskeletal disorders (MSD) prevention tool by proactively identifying candidates at higher risk of MSD based on movement competency.
