Finalization and Validation of a Field Approach for Predicting Back Loading Based on Laboratory Data Abstract 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. Produced Under this Project Scientific Reports Mise au point et validation d’une approche terrain de prédiction des chargements au dos basée sur des données de laboratoire Research Report: R-1164-fr Simplified Articles Prédire les chargements au dos en situation réelle Volume 35, n0 4 Scientific Publications Low back pain risk assessment: Combining trunk kinematic and electromyographic data through a deep learning neural networkDelisle A., Thénault F., Plamondon A. , Hakim MecheriSource : (2019). PREMUS 2019, the 10th International Scientific Conference on the Prevention of Work-Related Musculoskeletal Disorders: From research to evidence based sustainable interventions and practices, (p. 259).A generic lumbar moment estimator: combining inertial sensors and electromyographic data through an artificial neural networkDelisle A., Thénault F., Plamondon A. Source : (2018). Communication présentée à 20th Congress of the International Ergonomic Association, Florence, Italie. Other Project(s) You May be Interested in Expert/novice comparison of handling injury risksDevelopment of an ambulatory method for estimating back loading: integration of back kinetics and surface electromyographyHandling and women: from a biomechanical and ergonomic perspective Additional Information Type: Project Number: 2018-0007 Status: Completed Year of completion: 2022 Research Field: OSH and Sustainable Prevention Work Environment Team: Alain Delisle (Université de Sherbrooke)François Thénault (Université de Sherbrooke)André Plamondon (IRSST)Hakim Mecheri (IRSST)