A three-dimensional whole-body model to predict human walking on level ground

Area of Science:

• Biomechanics
• Computational modeling
• Robotics

Background:

• Predictive simulation of human walking is crucial for clinical motion analysis and rehabilitation engineering.
• Current models face limitations due to high computational costs and dependence on measurement data for initial conditions.

Purpose of the Study:

• To develop a computationally efficient model for predicting 3D human walking motions and forces.
• To assess different optimization objectives for realistic gait prediction without relying on measurement data.

Main Methods:

• Combined optimization and inverse dynamics for predictive simulation.
• Explored two optimization objectives: minimizing mechanical energy expenditure and minimizing the time integral of normalized joint torque.

Main Results:

• The model successfully predicted 3D whole-body motions and forces during human walking without measurement data.
• Minimizing the sum of time integrals of normalized joint torques resulted in a more realistic walking gait compared to minimizing mechanical energy expenditure.

Conclusions:

• The time integral of normalized joint torques is a more effective optimization criterion for realistic 3D human walking prediction than minimizing mechanical energy expenditure.
• Mechanical energy expenditure may only partially explain the complex criteria governing human walking in three dimensions.