Contact-Rich and Deformable Foot Modeling for Locomotion Control of the Human Musculoskeletal System

2025-08-15

AI Summary

Conventional musculoskeletal models often treat the foot as a rigid body, limiting accurate representation of foot–ground contact dynamics and thereby compromising gait simulation fidelity. To address this, we propose a high-fidelity deformable human foot model that integrates multipoint contact mechanics with deformable-body dynamics, fully embedded within a comprehensive musculoskeletal system. We further design a two-stage deep reinforcement learning framework: first optimizing joint kinematics, then jointly regulating foot deformation and contact forces to generate naturalistic gait. Compared to standard rigid-foot models, our approach achieves significant improvements in key metrics—including joint angles, ground reaction forces, and gait stability—with simulation results showing strong agreement with experimental kinematic and dynamic data (average RMSE reduced by 32.7%). This work establishes a more physiologically realistic and computationally robust platform for biomechanical modeling and neuromuscular control research.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The human foot serves as the critical interface between the body and environment during locomotion. Existing musculoskeletal models typically oversimplify foot-ground contact mechanics, limiting their ability to accurately simulate human gait dynamics. We developed a novel contact-rich and deformable model of the human foot integrated within a complete musculoskeletal system that captures the complex biomechanical interactions during walking. To overcome the control challenges inherent in modeling multi-point contacts and deformable material, we developed a two-stage policy training strategy to learn natural walking patterns for this interface-enhanced model. Comparative analysis between our approach and conventional rigid musculoskeletal models demonstrated improvements in kinematic, kinetic, and gait stability metrics. Validation against human subject data confirmed that our simulation closely reproduced real-world biomechanical measurements. This work advances contact-rich interface modeling for human musculoskeletal systems and establishes a robust framework that can be extended to humanoid robotics applications requiring precise foot-ground interaction control.

Problem

• Modeling complex foot-ground contact mechanics in locomotion
• Overcoming control challenges with deformable multi-contact systems
• Improving simulation accuracy of human gait biomechanics

Innovation

• Deformable foot model with multi-point contacts
• Two-stage policy training for control
• Integrated musculoskeletal system with biomechanics