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An electromyography-assisted biomechanical cervical spine model: Model development and validation.

Mina Alizadeh¹, Alexander Aurand¹, Gregory G Knapik¹

¹Spine Research Institute, The Ohio State University, 520 Baker Systems, 1971 Neil Avenue, Columbus, OH 43210, USA.

Clinical Biomechanics (Bristol, Avon)

|September 12, 2020

Summary

A new electromyography-assisted cervical spine model accurately predicts neck loading during dynamic activities. This tool helps quantify occupational neck disorder risks by estimating individual muscle forces and spinal tissue loads.

Keywords:

Computational model Curved muscle model Multi-body dynamics Neck model Occupational neck injury

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Area of Science:

Biomechanics
Occupational Health
Computational Modeling

Background:

Occupational neck disorders are common, yet existing cervical spine models primarily focus on impact scenarios, not daily activities.
Current models lack muscle simulation, dynamic analysis, and passive structures, hindering accurate estimation of occupational neck disorder risks.
This limits the ability to precisely quantify tissue loads and assess injury risks in occupational settings.

Purpose of the Study:

To develop and validate an electromyography-assisted cervical biomechanical model for estimating neck loading during complex 3D motions.
To enable simulation of dynamic movements relevant to workplace exposures and daily living activities.
To accurately predict spinal tissue loads and assess the risk of occupational neck disorders.

Main Methods:

Developed a 3D cervical biomechanical model incorporating curved muscle geometry and personalized muscle force parameters.
Implemented separate passive and electromyography-driven active muscle force components for dynamic simulation.
Utilized calibration algorithms to reverse-engineer personalized muscle properties and calculate individual active and passive muscle forces.

Main Results:

The electromyography-assisted model accurately predicts spinal tissue loads during both isometric and dynamic head and neck movements.
Calibration algorithms successfully determined personalized muscle properties for individuals.
The model demonstrates capability in simulating complex, dynamic motions relevant to occupational exposures.

Conclusions:

The developed electromyography-assisted cervical spine model with curved muscle geometry accurately predicts spinal tissue loads.
Personalized muscle force algorithms enhance the investigation of individual muscle forces and spinal tissue loads.
This model offers a robust tool for understanding and mitigating occupational neck disorders.