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

TL;DR

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 is introduced.

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.

Figures (10)

• Figure 1: Forearm phantom. The phantom is composed of Ecoflex 0050. The indentation spots are marked with red ink, providing positional references for subsequent simulations. Each spot represents different boundary conditions as the representative bones and the Ecoflex volume involved in the indentation vary.
• Figure 2: Phantom mesh. A segmentation example illustrates the mesh which two element types are used to model the forearm segment: 45,184 tetrahedral elements represent the superficial geometry, bone boundaries, and the transition between element types; and 3,429 hexahedral elements define the soft tissue complex. For both meshes, the indenter is defined as a rigid body and 1,280 tetrahedral elements.
• Figure 3: Experimental results of the forearm phantom at four points. Force and torque data are illustrated with continuous lines. The force-displacement curves are shown at the top, and the torque-rotation curves are at the bottom.
• Figure 4: Ogden coefficient space. It illustrates 250 sets of coefficients and their corresponding NMSE values, ranging from 0 to 2.5 (color scale). Points outside this range are shown in black, and the minimum NMSE is highlighted in red. Point 3 is shown to exemplify the behaviour of the NMSE in the parameter space. Distribution of NMSE across 250 parameter sets per point, illustrating a minimum at 0.1034 and varying NMSE ranges per point. The upper limit is set at 4.0 to highlight the threshold and range, as a few sets exceed this value.
• Figure 5: Force and torque responses for the Ogden model. Dotted lines represent three fitted curves based on the minimum NMSE of the sum of force and torque errors (red curves), the minimum NMSE considering only torque error (yellow curves), and the minimum NMSE considering only force error (purple curves). Blue circles indicate experimental data. Each row illustrates the response for point 1 and 3, top to bottom. Two dotted lines, shown in the top-left figure, as the yellow and red curves are identical. Second row represents the response for point 3.