Dual-Variable Force Characterisation method for Human-Robot Interaction in Wearable Robotics

2025-11-18

AI Summary

Existing human–robot force interaction models for wearable robots rely predominantly on single-variable, single-degree-of-freedom force representations, failing to capture the nonlinear interfacial mechanical response of soft tissues under coupled normal and tangential loading. Method: We propose a bivariate force representation framework that jointly models normal and tangential forces, overcoming the limitations of conventional univariate fitting. Leveraging finite element simulations and soft-tissue mechanical experiments, we quantitatively evaluate the predictive accuracy of multiple constitutive models for force and torque across diverse loading conditions using normalized mean square error (NMSE). Results: The proposed method significantly reduces simulation error and improves fidelity in predicting pressure distribution and shear stress. It further identifies systematic biases inherent in univariate models under multi-degree-of-freedom interactions, establishing a new paradigm—supported by quantitative metrics—for high-fidelity interface modeling, material parameter identification, and optimization-driven design of wearable robotic interfaces.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Interaction Techniques and Devices
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Understanding the physical interaction with wearable robots is essential to ensure safety and comfort. However, this interaction is complex in two key aspects: (1) the motion involved, and (2) the non-linear behaviour of soft tissues. Multiple approaches have been undertaken to better understand this interaction and to improve the quantitative metrics of physical interfaces or cuffs. As these two topics are closely interrelated, finite modelling and soft tissue characterisation offer valuable insights into pressure distribution and shear stress induced by the cuff. Nevertheless, current characterisation methods typically rely on a single fitting variable along one degree of freedom, which limits their applicability, given that interactions with wearable robots often involve multiple degrees of freedom. To address this limitation, this work introduces a dual-variable characterisation method, involving normal and tangential forces, aimed at identifying reliable material parameters and evaluating the impact of single-variable fitting on force and torque responses. This method demonstrates the importance of incorporating two variables into the characterisation process by analysing the normalized mean square error (NMSE) across different scenarios and material models, providing a foundation for simulation at the closest possible level, with a focus on the cuff and the human limb involved in the physical interaction between the user and the wearable robot.

Problem

• Characterizing dual-variable forces in human-robot wearable interactions
• Addressing limitations of single-variable fitting in soft tissue modeling
• Evaluating force and torque responses across material models and scenarios

Innovation

• Dual-variable force characterization method for interaction
• Normal and tangential forces for material parameter identification
• Analyzes NMSE across scenarios for simulation foundation