Determination of a reliable method for detecting load-in-hands during typical MMH lifting tasks

Overview

Keywords: Manual Materials Handling; Lifting; Electromyography (EMG); Motion Capture

Timeline: 2009 - 2012

Researchers: Joan M. Stevenson (Principal Investigator, Queen’s University), Patrick A. Costigan (Queen’s University), Evelyn L. Morin (Queen’s University), Susan A. Reid (Queen’s University)

Funder:  Centre of Research Expertise for the Prevention of Musculoskeletal Disorders (CRE-MSD)

Project type: Seed Grant

Sector/Workplace type: All

Theme:
Theme 3 Risk assessment and hazard identification

Background/rationale

Task and work demands analyses of work captured in the field is difficult.  Creation of models to be used as assessment tools might enable health and safety specialists to more easily assess tasks.

Research question/objectives/methods

The objective of this study was to examine different strategies to determine when and how much load was in the hands for use in cumulative spinal loading field studies.

15 male participants performed various lifts of varying weight from floor to waist height. Participants were instrumented with 3D motion capture and EMG of several arm and back muscles. Unique strategies using EMG or segment accelerations were studied to determine the best muscles and segments to investigate further in future studies. EMG results revealed that lift onset was best predicted and using Erector Spinae (T7 or T12), anterior deltoid and biceps brachii after using a zero load lift best captured lifting technique.

Key findings

Load mass could be predicted by these same muscles as well as by acceleration data from sensors on the wrist/forearm and upper back/sternum. The techniques that seem most successful were parallel cascade models and quadratic discriminate analyses.  

Implications for the prevention of MSDs

Future work will involve integration of these two approaches and improved equipment to assess lift onset and lift duration to possibly enhance field work in estimating work and task demands.