Modified electromyography-assisted optimization approach for predicting lumbar spine loading while walking with...

Area of Science:

• Biomechanics
• Human Movement Science
• Musculoskeletal Research

Background:

• Predicting lumbar spine loading is crucial for understanding musculoskeletal disorders.
• Electromyography-assisted optimization (EAO) is a common method, but requires maximal voluntary contraction (MVC) measurements.
• MVC measurements can be challenging for certain populations.

Purpose of the Study:

• To modify the EAO approach to eliminate the need for MVC measurements.
• To introduce an optimal lower boundary condition for trunk muscle co-activation.
• To assess the accuracy of the modified EAO approach in predicting lumbosacral joint compression force during walking with backpack loads.

Main Methods:

• A modified EAO approach was developed, using a linear electromyography-force relationship and an optimal lower boundary condition.
• Kinematic, kinetic, and electromyography data from 10 healthy men walking with backpack loads were collected.
• A computational algorithm identified the optimal lower boundary condition and analyzed sensitivity.

Main Results:

• The modified EAO approach, with an optimal lower boundary condition of 0.92, showed minimal deviation in peak lumbosacral joint compression force (-18 N).
• The method achieved a standard error of 9 N and a root mean square difference of 73.8 N for the force profile.
• The modified approach provided comparable estimations to the original EAO.

Conclusions:

• The modified EAO approach simplifies experimental procedures by removing the MVC requirement.
• This simplified method offers a viable alternative for predicting lumbosacral joint compression force, especially for individuals unable to perform MVC.
• The findings have implications for ergonomic assessments and clinical applications involving spine loading.