Reproducing Human Arm Strategy and Its Contribution to Balance Recovery Through Model Predictive Control.

Area of Science:

• Biomechanics
• Robotics
• Control Systems

Background:

• Human balance recovery is crucial for rehabilitation and robotic control.
• Existing research primarily focuses on walking, with limited data on arm strategy during quiet standing.
• Ankle and hip strategies are well-studied, but the role of arm movements in standing balance is less understood.

Purpose of the Study:

• To investigate the contribution of arm strategy to human balance recovery during quiet standing.
• To develop and validate a human-like balance control system using nonlinear model predictive control.
• To analyze the effects of different arm configurations (active, passive, fixed) on balance.

Main Methods:

• A simplified human model incorporating arms was created.
• A nonlinear model predictive control (NMPC) controller was designed for balance.
• Simulations were performed with active, passive, and fixed arm states under external perturbations.
• Key metrics like joint angle deviation and energy consumption were analyzed.

Main Results:

• The model successfully reproduced human-like balance recovery with and without arm rotation.
• Active and passive arm usage demonstrated significant contributions to balance stability.
• Arm strategy was shown to reduce recovery energy consumption compared to fixed arms.
• Computational results were supported by supplementary human balance experiments.

Conclusions:

• Arm strategy plays a vital role in human balance recovery during quiet standing.
• The developed NMPC controller effectively mimics human-like balance control.
• Understanding arm contributions can advance both human rehabilitation and humanoid robot design.