Individual-specific muscle maximum force estimation using ultrasound for ankle joint torque prediction using an...

Area of Science:

• Biomechanics
• Musculoskeletal modeling
• Medical imaging

Background:

• Electromyography (EMG)-driven models estimate muscle force using EMG signals as input.
• Accurate model parameters, like maximum voluntary contraction force (F(om)), are crucial but challenging to determine.
• Inaccuracies in F(om) can lead to substantial errors in biomechanical model outputs.

Purpose of the Study:

• To propose and validate a novel method for estimating F(om) using ultrasound (US) imaging.
• To improve the accuracy of EMG-driven biomechanical models by refining F(om) estimation.
• To compare the performance of a Hill-type ankle model using US-derived F(om) versus literature-based F(om).

Main Methods:

• Estimated muscle physiological cross-sectional area (PCSA) via US measurements of muscle thickness, volume, fiber length, and pennation angle.
• Calculated F(om) by multiplying estimated PCSA with a standard value for maximum muscle specific tension.
• Integrated US-derived F(om) into a Hill-type EMG-driven ankle model and compared its predictive accuracy against literature-derived F(om) using root mean square error.

Main Results:

• Ultrasound-derived F(om) estimation for Soleus, gastrocnemius medialis, and gastrocnemius lateralis muscles.
• A statistically significant reduction in root mean square error was observed when using US-obtained F(om) in the ankle model.
• Demonstrated improved accuracy of the EMG-driven model with the novel F(om) determination method.

Conclusions:

• Ultrasound-based estimation of PCSA offers a more accurate approach to determining F(om) for EMG-driven models.
• This method enhances the reliability and precision of muscle force estimations in biomechanical analyses.
• The findings support the use of US imaging for optimizing parameters in musculoskeletal modeling.