[Optimal solution and analysis of muscular force during standing balance]

Area of Science:

• Biomechanics
• Human Movement Science
• Computational Biology

Background:

• Maintaining standing balance involves complex coordination of lower extremity muscles.
• Redundant muscle forces present a challenge in accurately modeling musculoskeletal systems.
• Understanding muscle force distribution is crucial for analyzing human posture and movement.

Purpose of the Study:

• To determine the optimal distribution of muscular forces in the human lower extremity during static standing balance.
• To develop and apply an optimized mathematical model for analyzing redundant muscle forces.
• To compare single-objective and multi-objective optimization approaches for muscle force calculation.

Main Methods:

• A simplified physical model of the lower extremity musculoskeletal system (3 joints, 9 muscles) was created.
• An optimal mathematical model was formulated to address redundant muscle forces.
• Particle Swarm Optimization (PSO) was employed for both single-objective and multi-objective calculations.

Main Results:

• Numerical simulations were performed using the PSO algorithm.
• Multi-objective optimization yielded more reasonable distributions and variations of the nine muscle forces compared to single-objective methods.
• The study successfully simulated muscle force dynamics during standing balance.

Conclusions:

• Multi-objective optimization offers a more biologically plausible approach to modeling lower extremity muscle forces.
• The developed model provides insights into muscle coordination during standing balance.
• Simulation results facilitate qualitative analysis of muscle group function in maintaining posture.