On-body textile hysteresis estimation for personalized physical human-robot interaction

Abstract

Nearly all soft wearable robots rely on textiles to distribute actuation forces to the human body; however, the mechanical hysteresis of these materials significantly complicates device control. If not properly accounted for, this history-dependent behavior can result in substantial over-/under-support for which the human user must actively compensate. While a number of hysteresis modeling approaches have been proposed, these techniques are either (a) heuristic-driven and do not accurately reflect the observed physical behavior or (b) rely on complex benchtop calibration procedures that are not amenable to wearable applications where the complete human-robot system must be holistically considered. In this work, we present a new strategy to predict the complex hysteretic response of the combined human-robot system given its full state history using a mathematical technique known as a Preisach model. Our approach is directly personalized to each individual with data collected on the body in $\sim 90$ seconds. We demonstrate the technique with a previously proposed soft wearable robot for shoulder assistance, though the concept is applicable to any joint. To benchmark the efficacy of our approach against previously proposed strategies, we performed an open-loop trajectory tracking procedure with 12 human participants and an articulated mannequin. Our strategy achieved an average shoulder elevation angle tracking accuracy of 5.3° across human participants, representing a significant improvement compared to prior techniques. We anticipate that this new approach will facilitate significantly improved soft wearable robot control by providing reliable estimates of the full hysteretic system response, enabling more robust physical human-robot interaction and coordination.