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BackgroundVariability in movement is critical for performance under dynamic conditions. Stroke causes focal injury to the motor system, disrupts voluntary motor control, and leads to less smooth and more variable upper extremity movements. Few studies have characterized trial-by-trial variation in upper extremity movement smoothness and its clinical and neuroanatomic correlates in the first week post-stroke. ObjectiveTo evaluate trial-by-trial variation in upper extremity movement smoothness during planar reaching and relate it to clinical outcomes and neuroanatomical injury after acute stroke. MethodsTwenty-two patients (4.4 ± 1.7 days post-stroke) and 22 able-bodied adults completed a planar center-out reaching task. Smoothness was quantified with spectral arc length (SPARC). Median and interquartile range (IQR, a quantification of trial-by-trial variation) of SPARC values were assessed. Patients completed a clinical assessment battery acutely and at 90 days post-stroke. MRI-derived stroke lesions were analyzed to estimate basal ganglia, motor cortex, and corticospinal tract injury. Intraclass correlation, Spearman’s correlation, and multivariate regression evaluated trial-by-trial variation and its relation to clinical assessments, outcomes, and neuroanatomical injury. ResultsPost-stroke reaching was less smooth and more variable (larger IQR) compared to able-bodied adults. Variability in post-stroke smoothness was primarily driven by within-subject, trial-by-trial variation. More variable smoothness, even after controlling for median smoothness, related to worse performance on clinical assessments and 90-day outcomes. More variable smoothness related to greater corticospinal tract injury (ρ = 0.537,P = .011), but not to basal ganglia or motor cortex injury. ConclusionTrial-by-trial variation of movement is valuable for understanding sensorimotor control post-stroke and has implications for targeted neurorehabilitation. 

Abstract Experiential learning in biomedical engineering curricula is a critical component to developing graduates who are equipped to contribute to technical design tasks in their careers. This paper presents the development and implementation of an undergraduate and graduate-level soft material robotics design course focused on applications in medical device design. The elective course, offered in a bioengineering department, includes modules on technical topics and hands-on projects relevant to readings, all situated within a human-centered design course. After learning and using first principles governing soft robot design and exploring literature in soft robotics, students propose a new advance in the field in a hands-on design and prototype project. The course described here aims to create a structure to engage students in fabrication and the design approaches taken by practitioners in a specific field, applied here in soft robotics, but applicable to other areas of biomedical engineering. This teaching tips article details the pedagogical tools used to facilitate design and collaboration within the course. Additionally, we aim to highlight ways in which the course creates (1) opportunities to engage undergraduates in design in preparation for capstone courses, (2) outward facing opportunities to connect with practitioners in the field, and (3) the ability to adapt this hands-on experience within a typical lecture structure as well as a hybrid online and in-person offering, thus expanding its utility in bioengineering departments. We reflect on course elements that can inform future design-based course offerings in soft robotics and other design-based multidisciplinary fields in bioengineering. 

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.