Comparison of predictions between an EMG-assisted approach and two optimization-driven approaches for lumbar spine loading during walking with backpack loads

Objective: The efficacy of two optimization-driven biomechanical modeling approaches has been compared with an electromyography-assisted optimization (EMGAO) approach to predict lumbar spine loading while walking with backpack loads.
Background: The EMGAO approach adopts more variables in the optimization process and is complex in data collection and processing, whereas optimization-driven approaches are simple and include the fewest possible variables. However, few studies have been conducted on the efficacy of using the optimization-driven approach to predict lumbar spine loading while walking with backpack loads.
Method: Anthropometric information of 10 healthy male adults as well as their kinematic, kinetic, and electromyographic data acquired while they walked with various backpack loads (no-load, 5%, 10%, 15%, and 20% of body weight) served as inputs into the model for predicting lumbosacral joint compression forces. The efficacy of two optimization-driven models, namely double linear optimization with constraints on muscle intensity and single linear optimization without any constraints, was investigated by comparing the resulting force profile with that provided by a current EMGAO approach.
Results: The double and single linear optimization approaches predicted mean deviations in peak force of −5.1%, and −19.2% as well as root-mean-square differences in force profile of 16.2%, and 25.4%, respectively.
Conclusion: The double linear optimization approach was a relatively comparable estimator to the EMGAO approach in terms of its consistency, slight bias, and efficiency for predicting peak lumbosacral joint compression forces.
Application: The double linear optimization approach is a useful biomechanical model for estimating peak lumbar compression forces while walking with backpack loads. Copyright © 2019, Human Factors and Ergonomics Society.