IRSST - Institut de recherche Robert-Sauvé en santé et en sécurité du travail

Finalization and Validation of a Field Approach for Predicting Back Loading Based on Laboratory Data


Measurements of back loading make it possible to better understand the development of low back problems and thus help to prevent them. However, quantifying such loads in the workplace is still a challenge.

Recent research has studied the potential of integrating electromyography and back kinematics with the help of an artificial neural network. Although promising, this approach needs to be enhanced to make it easier to use in the workplace.

The objective of this activity is to develop a generic neural network based on data from previous laboratory studies. This easy-to-use network will make it possible to assess workers’ back loading on the ground. Researchers will be able to better gauge the effects of interventions targeting back loading in the workplace and choose the ones with the best potential for preventing backache.

Additional Information

Type: Project
Number: 2018-0007
Status: Completed
Year of completion: 2022
  • Alain Delisle (Université de Sherbrooke)
  • François Thénault (Université de Sherbrooke)
  • André Plamondon (IRSST)
  • Hakim Mecheri (IRSST)