A sensor-driven hill-type muscle modeling framework integrating sEMG and pFMG for biceps brachii force estimation

Area of Science:

• Biomechanics and Motor Control
• Wearable Technology
• Physiological Modeling

Background:

• Hill-type muscle models offer interpretable force estimation but face limitations in wearable applications due to sensing and validation challenges.
• Accurate muscle force estimation is crucial for personalized rehabilitation and performance monitoring.

Purpose of the Study:

• To present a sensor-driven Hill-type muscle modeling framework for estimating biceps muscle force using wearable signals.
• To validate the model's performance under controlled isometric conditions.

Main Methods:

• Developed a Hill-type muscle model integrating surface electromyography (sEMG) and pressure-based force myography (pFMG).
• Estimated passive and total biceps brachii forces across various elbow angles under isometric conditions.
• Evaluated model consistency against a benchmark mechanical elbow model based on joint geometry and static equilibrium.

Main Results:

• Pressure-based force myography (pFMG) alone estimated length-dependent passive muscle force.
• Combined sEMG and pFMG accurately estimated total muscle force trends during active conditions.
• Sensor-driven estimates showed strong agreement with mechanically derived reference values, indicating high accuracy and low error.

Conclusions:

• The proposed framework physiologically separates neural activation and geometric deformation for interpretable muscle force estimation.
• This approach offers an experimentally feasible solution for wearable muscle force estimation.
• It provides a foundation for real-time, subject-specific neuromuscular modeling applications.