Comparison of Predictions Between an EMG-Assisted Approach and Two Optimization-Driven Approaches for Lumbar Spine Loading During Walking With Backpack Loads

Abstract

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.