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EMG-based lumbosacral joint compression force prediction using a support vector machine.

Simon S W Li¹, Carlin C F Chu², Daniel H K Chow³

¹Department of Health and Physical Education, The Education University of Hong Kong, Hong Kong.

Medical Engineering & Physics

|September 21, 2019

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A new electromyography-based support vector machine (EMGB_SVM) approach simplifies lumbar spine load prediction during walking with backpacks. This method, using only electromyographic data, proves comparable to the complex electromyography-assisted optimization (EMGAO) approach.

Area of Science:

Biomechanics
Human Movement Analysis
Computational Modeling

Background:

Electromyography-assisted optimization (EMGAO) is standard for predicting lumbar joint loads but requires extensive data.
The complexity of EMGAO hinders practical application due to demanding data collection and processing.

Purpose of the Study:

To develop a simplified electromyography-based support vector machine (EMGB_SVM) approach for predicting lumbar spine load.
To assess the efficacy of the EMGB_SVM by comparing its predictions with the established EMGAO method.

Main Methods:

Collected anthropometric, kinematic, kinetic, and electromyographic data from 10 healthy males walking with varying backpack loads (0-20% body weight).
Utilized a biomechanical model incorporating this data to predict lumbosacral joint compression force.

Keywords:

Electromyography Machine learning Spinal loads Trunk muscle Walking

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Validated the EMGB_SVM approach against the EMGAO method.

Main Results:

The EMGB_SVM approach demonstrated an average deviation of -3.3% for peak forces and 5.1% for minimum forces.
A root mean square difference of 7.5% was observed in the force profiles compared to EMGAO.
The EMGB_SVM showed slight bias, favorable consistency, and efficiency in predictions.

Conclusions:

The EMGB_SVM approach offers a simplified and efficient alternative for predicting lumbosacral joint compression force.
This method effectively predicts lumbar spine load during walking with backpack loads using only electromyographic data.
EMGB_SVM presents a comparable and practical estimation tool for biomechanical load analysis.