Muscle Tendon Passive Parameter Estimation Using Musculoskeletal Optimal Control

Area of Science:

• Biomechanics
• Human Movement Analysis
• Musculoskeletal Modeling

Background:

• Accurate measurement of passive muscle-tendon parameters (PMPs) is vital for understanding human movement.
• Existing methods for PMP measurement are often inconsistent or invasive.

Purpose of the Study:

• To develop and validate a novel noninvasive method for estimating PMPs.
• To utilize a direct collocated optimal control algorithm for PMP estimation.

Main Methods:

• Employed an optimal control algorithm for PMP estimation in silico, mechanical analogues, and in vivo.
• Used a quasi-static knee and ankle flexion protocol to isolate passive properties.
• Validated the method against experimental data using forward dynamic simulations.

Main Results:

• In silico simulations showed <3.5% error for muscle stiffness and tendon slack length, <6% for tendon stiffness.
• Mechanical analogue model yielded a maximum 9% error for spring stiffness.
• In vivo validation demonstrated average RMSE <0.56° for motion and 0.012 Nm/kg for torque.

Conclusions:

• The proposed noninvasive method accurately estimates PMPs in knee flexors/extensors.
• This technique provides valuable biomechanical insights.
• Potential applications include rehabilitation, sports performance optimization, and injury prevention.