A proprioception based regulation model to estimate the trunk muscle forces

Area of Science:

• Biomechanics
• Musculoskeletal modeling
• Spinal physiology

Background:

• Accurate assessment of spinal loads necessitates understanding trunk muscle forces.
• Muscle redundancy poses a challenge, requiring effective force attribution strategies.
• Existing models like optimization or EMG-based approaches have limitations.

Purpose of the Study:

• To present a new trunk muscle regulation model based on the proprioception hypothesis.
• To investigate the model's ability to prevent spinal joint overloading.
• To compare the proposed model against existing optimization and hybrid models.

Main Methods:

• Development of a regulation model for trunk muscles incorporating proprioception.
• Comparison of the proposed model with an optimization model.
• Comparison with a model combining optimization criteria using electromyography (EMG) data.

Main Results:

• The proposed model significantly decreased spine postero-anterior shear forces compared to a standard optimization model.
• The model demonstrated a 38% improvement in fitting observed muscle activation from EMG data compared to a combined optimization model.
• Results suggest the model's physiological relevance due to its regulation of all spinal components.

Conclusions:

• The proprioception-based trunk muscle regulation model offers a more physiologically relevant approach to understanding spinal loading.
• This model provides a significant reduction in spinal shear forces and enhances EMG data correlation.
• The findings support the model's potential for improving biomechanical analyses of the spine.