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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. 

Abstract IntroductionHigh-intensity gait training is widely recognized as an effective rehabilitation approach after stroke. Soft robotic exosuits that enhance post-stroke gait mechanics have the potential to improve the rehabilitative outcomes achieved by high-intensity gait training. The objective of thisdevelopment-of-conceptpilot crossover study was to evaluate the outcomes achieved by high-intensity gait training with versus without soft robotic exosuits. MethodsIn this 2-arm pilot crossover study, four individuals post-stroke completed twelve visits of speed-based, high-intensity gait training: six consecutive visits of Robotic Exosuit Augmented Locomotion (REAL) gait training and six consecutive visits without the exosuit (CONTROL). The intervention arms were counterbalanced across study participants and separated by 6 + weeks of washout. Walking function was evaluated before and after each intervention using 6-minute walk test (6MWT) distance and 10-m walk test (10mWT) speed. Moreover, 10mWT speeds were evaluated before each training visit, with the time-course of change in walking speed computed for each intervention arm. For each participant, changes in each outcome were compared to minimal clinically-important difference (MCID) thresholds. Secondary analyses focused on changes in propulsion mechanics and associated biomechanical metrics. ResultsLarge between-group effects were observed for 6MWT distance (d = 1.41) and 10mWT speed (d = 1.14). REAL gait training resulted in an average pre-post change of 68 ± 27 m (p = 0.015) in 6MWT distance, compared to a pre-post change of 30 ± 16 m (p = 0.035) after CONTROL gait training. Similarly, REAL training resulted in a pre-post change of 0.08 ± 0.03 m/s (p = 0.012) in 10mWT speed, compared to a pre-post change of 0.01 ± 06 m/s (p = 0.76) after CONTROL. For both outcomes, 3 of 4 (75%) study participants surpassed MCIDs after REAL training, whereas 1 of 4 (25%) surpassed MCIDs after CONTROL training. Across the training visits, REAL training resulted in a 1.67 faster rate of improvement in walking speed. Similar patterns of improvement were observed for the secondary gait biomechanical outcomes, with REAL training resulting in significantly improved paretic propulsion for 3 of 4 study participants (p < 0.05) compared to 1 of 4 after CONTROL. ConclusionSoft robotic exosuits have the potential to enhance the rehabilitative outcomes produced by high-intensity gait training after stroke. Findings of thisdevelopment-of-conceptpilot crossover trial motivate continued development and study of the REAL gait training program. 

A quantitative analysis of human gait patterns in space–time provides an opportunity to observe variability within and across individuals of varying motor capabilities. Impaired gait significantly affects independence and quality of life, and thus a large part of clinical research is dedicated to improving gait through rehabilitative therapies. Evaluation of these paradigms relies on understanding the characteristic differences in the kinematics and underlying biomechanics of impaired and unimpaired locomotion, which has motivated quantitative measurement and analysis of the gait cycle. Previous analysis has largely been limited to a statistical comparison of manually selected pointwise metrics identified through expert knowledge. Here, we use a recent statistical-geometric framework, elastic functional data analysis (FDA), to decompose kinematic data into continuous ‘amplitude’ (spatial) and ‘phase’ (temporal) components, which can then be integrated with established dimensionality reduction techniques. We demonstrate the utility of elastic FDA through two unsupervised applications to post-stroke gait datasets. First, we distinguish between unimpaired, paretic and non-paretic gait presentations. Then, we use FDA to reveal robust, interpretable groups of differential response to exosuit assistance. The proposed methods aim to benefit clinical practice for post-stroke gait rehabilitation, and more broadly, to automate the quantitative analysis of motion.