Journal of NeuroEngineering and Rehabilitation

Prosthesis non-use and abandonment remain common among people with lower-limb amputation, yet current outcome measures capture only limited aspects of how prostheses are used in everyday life. Clinical assessments are typically conducted in controlled settings and rely on self-report or aggregate activity counts, which do not adequately represent functional performance, physiological effort, or lived experience during real-world prosthesis use. Wearable and ambient sensing offer a means of addressing this gap, but existing approaches tend to measure single dimensions in isolation and are rarely validated against laboratory reference standards before free-living deployment. This protocol describes an integrated multimodal framework for assessing real-world lower-limb prosthesis use across three complementary domains: classification of activities of daily living, estimation of energy expenditure, and assessment of emotional state. Approximately 40 adults with unilateral transfemoral or transtibial amputation complete a two-phase protocol. In the laboratory phase, wearable inertial, physiological, and ambient sensing are validated against established reference standards, including video annotation and indirect calorimetry. In the free-living phase, validated models are applied during a single seven-day home monitoring period, unifying all three domains within one deployment. A defined data harmonisation and quality-control procedure aligns heterogeneous sensor streams and preserves traceability between laboratory calibration and free-living measurement, enabling reproducible interpretation of functional behaviour, metabolic cost, and momentary emotional experience in relation to established clinical outcome domains. By integrating multimodal sensing at the level of study design rather than post-hoc analysis, the framework provides a validated, reproducible methodology for characterising prosthesis use beyond the capacity of conventional instruments, offering a transferable approach for real-world monitoring in rehabilitation research

Back-ground. Beneficial rehabilitation interventions for severely impaired stroke patients are limited. Owing to practical constraints in the routine clinical use of electroencephalogram (EEG)-based brain-computer interfaces, this study investigates the feasibility of using a more practical electromyography (EMG) to detect movement intention in severe stroke subjects without visible movement. Currently, no large-scale studies provide strong evidence in favour of EMG-based human-machine interaction for closed-loop control of robotic assistance for severe stroke. Objective. To screen severely impaired stroke subjects without active wrist extension for the presence of residual EMG activity. Methods. High-density surface EMG was recorded from the wrist extensor muscles of 100 severely impaired stroke survivors while they repeatedly attempted wrist extension. EMG activity during "Rest" and "Move" states was compared, and subjects showing statistically greater muscle activity during Move than Rest were classified as having residual EMG. A modified Hodges detector combined with the probability difference-sum ratio (PDSR) was used for classification, with a threshold of 0.73 identifying subjects with residual EMG. Results. Of the 100 subjects without active wrist extension (Muscle power: MRC < 2), 64 exhibited residual EMG activity, supporting the feasibility of EMG for movement intention detection. Among these, 35 demonstrated consistent muscle activity (Detection probability > 0.2); representing suitable candidates for EMG-driven robot-assisted therapy. Conclusions. A substantial proportion of severely impaired stroke subjects without active movement could benefit from a simpler EMG-based interface for robot-assisted therapy. Distinct neural mechanisms (intact voluntary drive or abnormal co-activation) may contribute to the residual muscle activity, which should be considered while designing control strategies.

ObjectiveTo establish short-term feasibility and usability of wrist-worn wearable sensors to capture arm/hand activity of stroke survivors and to explore the association between factors related to use of the paretic arm/hand. Methods30 chronic stroke survivors were monitored with wrist-worn wearable sensors during 12h/day for a 7-day period. Participants also completed standardized assessments to capture stroke severity, arm motor impairments, self-perceived arm use and self-efficacy. Usability of the wearable sensors was assessed using the adapted System Usability Scale and an exit interview. Associations between motor performance and capacity (arm/hand impairments and activity limitations) were assessed using Spearmans correlations. ResultsMinimal technical issues or lack of adherence to the wearing schedule occurred, with 87.6% of days procuring valid data from both sensors. Average sensor wear time was 12.6 (standard deviation: 0.2) h/day. Three participants experienced discomfort with one of the wristbands and three other participants had unrelated adverse events. There were positive self-reported usability scores (mean: 85.4/100) and high user satisfaction. Significant correlations were observed for measures of motor capacity and self-efficacy with paretic arm use in the home and the community (Spearmans correlation {rho}s: 0.44-0.71). ConclusionsThis work demonstrates the feasibility and usability of a consumer-grade wearable sensor to capture paretic arm activity outside the laboratory. It provides early insight into stroke survivors everyday arm use and related factors such as motor capacity and self-efficacy. ImpactThe integration of wearable technologies into clinical practice offers new possibilities to complement in-person clinical assessments and to better understand how each person is moving outside of therapy and throughout the recovery and reintegration phase. Insights gained from monitoring stroke survivors arm/hand use in the home and community is the first step towards informing future research with an emphasis on causal mechanisms with clinical relevance.

AbstractConventional motor rehabilitation for stroke remains labor intensive and shows limited efficacy for chronic survivors. Non-invasive functional electrical stimulation (FES) of the neuromuscular system is promising for restoring mobility. However, traditional FES interventions for stroke are limited by single-channel protocols that target isolated muscles and lack integration with neuromotor control strategies, thus failing to address the heterogeneity of post-stroke motor deficits. To overcome these challenges, we employed a personalized FES paradigm grounded in muscle synergies -- neuromotor modules that coordinate multimuscle activation during movement. By leveraging muscle synergies as biomarkers of motor impairment, our intervention delivers coordinated multi-muscle stimulation that mimics the healthy muscle patterns absent in each stroke survivor. Compared with sham group (N = 10), chronic stroke survivors in the treatment group (N = 23) demonstrated higher synergy similarity to the normative synergies after treatment (p = 0.01). Notably, this improvement correlated with the gain in lower-limb Fugl-Meyer (FM) score ({bigtriangleup}FM = 2.6 vs. 0.7, p = 0.04) and enhancement in gait symmetry. Our study shows, for the first time, that muscle synergy-based FES can address the one-size-fits-all limitation of conventional FES by offering a personalized, efficacious, and neuroscience-based intervention that may improve gait kinematics and motor control even in chronic stroke survivors through restoration of muscle synergies.

ObjectiveParkinsons disease can impair gait and stability, leading to reduced independence and increased fall risk. While speed dependent treadmill training (SDTT) is clinically effective, the specific biomechanical and neurophysiological mechanisms driving these improvements remain unclear. The "StepuP" multicenter randomized controlled trial aims to elucidate these mechanisms and determine whether training enriched with virtual reality or mechanical perturbations (SDTT+) enhances gait efficacy and transfer to daily life. MethodsWe will recruit 126 individuals with Parkinsons disease across four clinical sites and 21 healthy older adults as a reference group. Participants will be randomized to receive either standard SDTT or SDTT+ for 12 sessions. To capture the trajectory of recovery and retention, assessments will occur at three distinct timepoints: baseline, post-intervention, and a 12-week follow-up, each assessment including synchronized 64-channel electroencephalography (EEG), electromyography (EMG), and 3D kinematics. This multimodal setup allows for the quantification of cortical beta-band activity, corticomuscular coherence, and stability-related foot placement control. Furthermore, we will assess participants satisfaction, usability, and engagement through questionnaires and interviews to understand individual adherence and barriers to training. SignificanceThe primary clinical endpoint is comfortable overground walking speed. We hypothesize that gait improvements are mediated by improved stability-related foot placement and cortical sensorimotor integration. By correlating lab-based mechanistic changes with real-world mobility patterns and participant experiences, this study seeks to identify specific pathophysiological mechanisms engaged during the treadmill training. These insights will help distinguish responders from non-responders, facilitating the development of personalized, acceptable, and effective rehabilitation strategies.

Objective: To investigate the clinical validity of finger-worn accelerometers for providing a comprehensive assessment of upper-limb motor performance in stroke survivors in real-world environments, compared to wrist-worn accelerometers, and to examine how the clinimetric properties of wearable-based motor performance measures vary with the duration of patient data collection. Design: Cross-sectional observational design. Setting: Research laboratory and free-living environments. Participants: Twenty-seven stroke survivors aged 18-80 years with ischemic or hemorrhagic stroke at least six months prior to enrollment and mild-to-moderate upper-limb impairment without severe range-of-motion restrictions were enrolled. Three participants were ineligible and four withdrew, resulting in a final cohort of 20 participants (N = 20). Interventions: Not applicable. Main Outcome Measures: Wearable-based motor performance measures derived from fine-hand movements, gross-arm movements, and the combination of fine-hand and gross-arm movements captured by finger-worn and wrist-worn accelerometers in naturalistic settings for 6.4 {+/-} 1.8 days. Results: Wearable-based motor performance measures from fine-hand movements demonstrated the strongest convergent validity, known-group validity, and test-retest reliability, followed by those from combined and gross-arm movements. Convergent validity and test-retest reliability of wearable-based motor performance measures improved with longer monitoring durations, with four days being sufficient to obtain accurate and reliable upper-limb measures. Conclusions: Wearable-based motor performance measures from finger-worn accelerometers provide a more comprehensive assessment of upper-limb motor performance than those from wrist-worn accelerometers, supporting their use for real-world monitoring in stroke survivors. Furthermore, the improvements in clinimetric properties of wearable-based motor performance measures with longer monitoring durations highlight the importance of multi-day monitoring to mitigate day-to-day variability and ensure robust assessment.

Purpose: To explore how 12-weeks of robotic walking impacts physical function and sequelae of inactivity for individuals with pediatric-onset neuromotor impairments. Methods: A single-arm mixed-methods interventional study examined robotic walking for 12-weeks in home and community settings, with 12-week follow-up. Outcomes included family goals (Goal Attainment Scale (GAS)) and perspectives (Interviews), postural control (Early Clinical Assessment of Balance), physical activity (Actigraphy, Habitual Activity Estimation Scale, Patient Reported Outcome Measurement Information System (PROMIS) Physical Activity) and sequelae of inactivity (PROMIS Sleep Disturbances, Bowel Function Diary). GAS was collected pre-training, post-training, and 12-week follow-up. All other quantitative outcomes were collected every 4-weeks. Quantitative data are described with median (25th-75thpercentile) and analyzed using a Skillings-Mack test with post-hoc Wilcoxon Signed-Rank. Qualitative interviews were conducted before and after training and analyzed thematically. Results: 15 participants aged 4-23 completed this study. Participants had cerebral palsy (10/15) or rare genetic conditions (5/15), and most used a wheelchair in community settings. Postural control improved (test-statistic = 23.0, p<0.001) after 8 weeks (change=5.0(0.0-21.4), p=0.016) and was maintained through 12-week follow-up (change=13.7(3.1-23.7), p=0.008). Over half of the participants achieved goals (t-score > 50) after training. Exploratory analyses suggest improvements in sleep disturbance immediately after training (p=0.025) and 4-weeks after (p=0.047). All measures of physical activity did not improve. Parents reported improvements in walking, activities of daily living, and sequelae of inactivity (i.e., bowel function, appetite, and sleep). Conclusions: Improvements were seen across a range of measures and notably postural control improvements were maintained at the follow-up. Parents perceived improvements in physical function and activities of daily living. Future research is warranted to further understand the impacts of robotic walking for children and small adults with mobility impairments.

Exoskeletons that assist the hip, knee, and ankle joints have begun to improve human mobility, particularly by reducing the metabolic cost of walking. However, direct comparisons of optimal assistance of these joints, or their combinations, have not yet been possible. Assisting multiple joints may be more beneficial than the sum of individual effects, because muscles often span multiple joints, or less effective, because single-joint assistance can indirectly aid other joints. In this study, we used a hip-knee-ankle exoskeleton emulator paired with human-in-the-loop optimization to find single-joint, two-joint, and whole-leg assistance that maximally reduced the metabolic cost of walking for three participants. Hip-only and ankle-only assistance reduced the metabolic cost of walking by 26% and 30% relative to walking in the device unassisted, confirming that both joints are good targets for assistance. Knee-only assistance reduced the metabolic cost of walking by 13%, demonstrating that effective knee assistance is possible. Two-joint assistance reduced the metabolic cost of walking by between 34% and 42%, with the largest improvements coming from hip-ankle assistance. Assisting all three joints reduced the metabolic cost of walking by 50%, showing that at least half of the metabolic energy expended during walking can be saved through exoskeleton assistance. Changes in kinematics and muscle activity indicate that single-joint assistance indirectly assisted muscles at other joints, such that the improvement from whole-leg assistance was smaller than the sum of its single-joint parts. Exoskeletons can assist the entire limb for maximum effect, but a single well-chosen joint can be more efficient when considering additional factors such as weight and cost.

ObjectiveTo describe the design and development of NeuroRehab VR, a fully immersive, specific and gamified virtual reality (VR) software aimed at improving the quality of life and reducing disability in post-stroke patients. MethodsA public-private collaborative research project was carried out between 2022 and 2024 by a multidisciplinary and multicenter team comprising neurologists, rehabilitation specialists, physiotherapists, exercise and sport sciences professionals and members of the company Dynamics VR Rehab, including engineers, developers, computer programmers, game designers, and digital artists. The project was structured into three phases: preproduction, production, and postproduction, with periodic focus group meetings and testing sessions with patients in the subacute phase of stroke held every one to two months. ResultsIn the Preproduction phase, the multidisciplinary team discussed the initial concepts and, using the SCRUM methodology together with feedback from pilot patients, developed the software design. In this process, three thematic environments (i.e., home, nature, and science fiction) were established, along with five activity types targeting upper limb rehabilitation: fine motor skills, gross motor skills, balance, rhythmic movements, and movement speed. The software incorporated fully immersive VR, advanced hand tracking technology, and adaptive gamification elements. During the Production phase, these components were implemented and consolidated into a functional prototype. Finally, in the post-production phase, several adjustments were made after identifying minor issues, with the aim of improving activity responsiveness and refining the user experience for both patients and clinicians. ConclusionNeuroRehab VR represents a promising tool to be integrated into post-stroke rehabilitation programs and is being tested though a clinical trial. Moreover, this public-private, multidisciplinary, and multicenter collaboration model constitutes an effective framework for the design and development of technologically driven solutions applicable to clinical rehabilitation settings.

Physical human-device interaction is central to many emerging technologies in neurorehabilitation and assistive robotics, but simulation-based research in this area remains fragmented across musculoskeletal models, assistive-device representations, task definitions, and controller-development workflows. This fragmentation limits the accessibility, reproducibility, and extensibility of studies on prostheses, exoskeletons, wearable rehabilitation devices, and related human-device systems. Here we introduce MyoAssist 1.0, an open-source framework for neuromechanical simulation of physical human-device interaction built within the MyoSuite ecosystem. MyoAssist organizes each simulation environment as a composed human-device-task system that combines compatible musculoskeletal, assistive-device, and task-scenario components through a shared composition pipeline. The current release includes 15 assistive-device models spanning gait assistance, upper-body support, manipulation, and seated mobility and supports compatible musculoskeletal models ranging from reduced lower-limb models to a 416-muscle full-body model. These human-device systems can be simulated within the broad task scenarios provided by MyoSuite, while MyoAssist adds locomotion-specific task scenarios with configurable terrain and target-velocity conditions for gait-assistive studies. MyoAssist also provides two complementary controller-development frameworks: a reinforcement-learning framework for training adaptive policies and a controller-optimization framework for tuning structured, interpretable human and device controllers. Both frameworks operate on the same simulation environments and provide standardized evaluation outputs for inspecting, comparing, reusing, and extending learned and structured control strategies. By integrating modular human models, assistive-device models, task scenarios, and training workflows under a shared open-source interface, MyoAssist aims to lower the barrier to reproducible simulation-based research and to support collaborative development of assistive technologies for neurorehabilitation and physical human-device interaction.

Chronic stroke gait disorders involve impaired motor coordination. While high-intensity gait training (HIGT) is supported by current clinical practice guidelines, and Functional Electrical Stimulation (FES) to tibialis anterior addresses foot drop, extending FES to multiple muscles may improve functional outcomes. Leveraging a fast-to-don FES sleeve, we tested feasibility and preliminary efficacy of a personalized multichannel FES (MFES) intervention based on the individuals motor coordination impairment paired with HIGT. Fourteen chronic stroke survivors were randomly assigned to either HIGT or MFES+HIGT for six weeks. Feasibility was evaluated by measuring setup time and collecting feedback from participants and four therapists. Gait speed, endurance, gait biomechanics, and muscle synergies were assessed at baseline, midpoint, post-training, and one-month follow-up. System setup time plateaued at 4.53 minutes by the ninth session. Both participants and therapists rated the intervention highly feasible, acceptable, and usable. Adherence was high, with no dropouts in the MFES+HIGT group. While most participants reached target heart rate zones, those with severe impairments (N=3, <0.4 m/s gait speed) struggled to maintain these levels. Despite the small sample, only the MFES+HIGT group demonstrated significant endurance gains from baseline to post-training and follow-up, while both groups improved walking speed, impaired limb step length, and muscle synergy similarity to normative data. When excluding household ambulators, only the MFES+HIGT group showed post-training and follow-up gains in endurance and self-selected walking speed. This study demonstrates that synergy-based MFES is feasible for integration into chronic stroke gait rehabilitation supports larger-scale trials to validate clinical efficacy and identify responders.

BackgroundThe evidence-based quantification of the relation between changes in movement quality and functionality can assist clinicians in achieving more effective structuring or adaptations of therapy. Facilitating this quantification through computational tools can also result in the generation of large-scale data sets that can inform automated assessment of rehabilitation. Interpretable automated assessment can leave more time for clinicians to focus on treatment and allow for remotely supervised therapy at the home. MethodsIn our first experiment, we developed a rating process and accompanying computational tool to assist clinicians in following a standardized movement assessment process relating functionality to movement quality. We conducted three studies with three different versions of the computational rating tool. Clinicians rated task, segment, and movement feature performance for 440 videos in which stroke survivors executed standardized upper extremity therapy tasks related to functional activities. In our second experiment, we used the 440 rated videos, in addition to 140 videos of unimpaired subjects performing the same tasks, to improve our previously developed automated assessment ensemble model that automatically generates segmentation times and task ratings across impaired and unimpaired movement. The automated assessment ensemble integrates expert knowledge constraints into data driven training though a combination of HMM, transformer, MSTCN++, and decision tree computational modules. In our third experiment, we used the therapist and automated ratings to develop a four-layer Hierarchical Bayesian Model (HBM) for computing the statistical relation of movement quality changes to functionality. We first calculated conditional layer probabilities using clinician ratings of task, segment, and movement features. We increased the granularity of observation of the HBM by formulating {Delta}HBM, a correlation graph between kinematics and movement composite features. Finally, we used k-means clustering on the {Delta}HBM to identify three clusters of features among the 16 movement composite and 20 kinematic features and used the centroid of these clusters as the weights of the input data to our computational assessment ensemble. ResultsWe evaluated the efficacy of our rating interface in terms of inter-rater reliability (IRR) across tasks, segments, and movement features. The third version of the interface produced an average IRR of 67%, while the time per session (TPS) was the lowest of the three studies. By analyzing the ratings, we were able to identify a small number of movement features that have the highest probability of predicting functional improvement. We evaluated the performance of our automated assessment model using 60% impaired and 40% unimpaired movement data and achieved a frame-wise segmentation accuracy of 87.85{+/-}0.58 and a block-segmentation accuracy of 98.46{+/-}1.6. We also demonstrated the performance of our proposed HBM in correlation to clinicians ratings with a correlation over 90%. The HBM also generates a correlation graph, {Delta}HBM that relates 16 composite movement features to the 20 kinematic features. We can thus integrate the HBM into the computational assessment ensemble to perform automated and integrated movement quality and functionality assessment that is driven by computationally extracted kinematics. ConclusionsCombining standardized clinician ratings of videos with knowledge based and data driven computational analysis of rehabilitation movement allows the expression of an HBM that increases the observability of the relation of movement quality to functionality and enables the training of computational algorithms for automated assessment of rehabilitation movement. While our work primarily focuses on the upper extremity of stroke survivors, the models can be adopted to many other neurorehabilitation contexts.

Importance: There are very few interventions designed for individuals with the most severe mobility impairments. Robotic walking may be an effective way to facilitate exercise in this population. Objective: To examine how robot-assisted walkers physical parameters and user characteristics moderate the exercise intensity achieved by individuals with neuromotor disorders causing mobility impairments. Design: A prospective study. Intervention: A single-session intervention involving an overground robot-assisted walker that can be used in an endurance mode requiring no voluntary movement or a strength mode during which voluntary movement could impact the gait pattern. Participants: Individuals with pediatric-onset mobility impairments Main Outcome Measures: Participants were characterized based on their age, sex, diagnosis, and Gilette Functional Assessment Questionnaire (FAQ) levels. Heart rate during the final minute of four 5-minute walking conditions: strength mode at fast speed, strength mode at slow speed, endurance mode at fast speed and endurance mode at slow speed was expressed as a percentage of each participant heart rate reserve (%HRR). Linear mixed-effects models were used to evaluate the impact of speed, device mode and user characteristics on the level of exercise achieved. Results: 29 individuals (aged 2-26 years) with mobility impairments (FAQ levels 1-6) completed this study. Fast speeds were associated with a higher %HRR (beta= 2.11, SE = 1.03, p = 0.044). Participants in FAQ class 1 exhibited significantly higher %HRR compared with those in FAQ classes 2 and 3 (beta=18.6, SE=7.31, p=0.017; beta= 16.9, SE = 8.13, p = 0.047, respectively). No other device or participant characteristics were associated with exercise intensity. Conclusions: To facilitate higher exercise levels, users of robot-assisted walkers can increase their speed. Individuals who cannot take steps due to their neuromotor impairments experience the highest levels of exercise. Relevance: The findings in this study highlight the promise of robot-assisted walkers to improve health, particularly in those who often face the greatest barriers to exercise.

BackgroundCommercially-available microprocessor-controlled prosthetic knees are unable to fully replicate the biomechanical function of the missing biological limb. While powered prostheses have the capacity to restore joint level kinetics, current systems rely on intrinsic control schemes that do not allow the user to volitionally modulate movement under neural commands. This limitation may compromise functional performance and hinder prosthetic embodiment, the sense that the device is part of the users body. In a case study on one test participant, we evaluate the functional and perceptual benefits of a bone-anchored, neurally-controlled knee prosthesis by comparing it to the participants microprocessor-controlled prosthesis. MethodsWe conducted a within-subject study on an individual with a transfemoral amputation, with an osseointegrated implant and surgically reconstructed agonist-antagonist muscle pairs. We tested a neurally-controlled powered knee and conventional microprocessor knee across a set of activities, including seated volitional control tasks, sit-to-stand transitions, squatting, level-ground walking, stair ascent, and uninstructed standing. Performance metrics included knee kinematics, prosthesis-generated mechanical power, and functional outcomes such as gait speed, stair ascent time, and weight-bearing symmetry derived from ground reaction forces. Functional mobility and control were complemented by self-reported embodiment, assessed through a questionnaire targeting agency, ownership, and body representation. ResultsThe neurally-controlled prosthesis enabled intuitive and responsive control. Compared to the subjects prescribed prosthesis, the prosthesis yielded improved temporal gait symmetry during walking (symmetry index: 0.93 vs. 0.59, with 1 indicating perfect stance time symmetry), increased prosthetic-side weight-bearing during sit-to-stand and squatting, and successful execution of a step-over-step stair ascent strategy--an outcome not achievable with the subjects prescribed device. Embodiment scores were consistently higher with the neurally-controlled prosthesis compared to the prescribed device across multiple domains, including agency, ownership and body representation. ConclusionsThis study is the first to directly compare a prescribed microprocessor knee with a bone-anchored, neurally-controlled powered prosthesis. By combining osseointegration, surgically reconstructed agonist-antagonist muscle pairs, and powered actuation, the system improved gait symmetry, greater prosthetic-side loading, and step-over-step stair ascent. These results demonstrate the novelty and promise of integrating surgical and mechatronic innovations to restore both functional mobility and embodied control after transfemoral amputation. Trial registrationThis study was approved by the Institutional Review Board at MIT (Protocol No. 2503001589).

Background and Purpose: Walking after stroke is often characterized by persistent biomechanical impairments and reduced walking capacity. While visual biofeedback can improve gait mechanics and fast walking can enhance capacity, it is unclear whether individuals post-stroke can effectively use biofeedback at higher walking speeds to address both deficits simultaneously. This study examined the effects of walking speed on the ability of participants with chronic stroke to reduce step length (SL) errors using visual biofeedback. Methods: Sixteen individuals with chronic stroke walked on a treadmill at slow, self-selected, and fast speeds with and without visual SL biofeedback. Absolute SL error relative to individualized targets was calculated for paretic and non-paretic limbs. Linear mixed-effects models with piecewise linear splines assessed the effects of speed, limb, and feedback condition. Post hoc comparisons were performed for significant interactions. Results: At lower speeds, increasing speed reduced SL error in both limbs (p < 0.001). At higher speeds, the effects of speed were dependent on limb and condition (p < 0.001). Paretic SL error increased with speed without feedback but remained stable with feedback (p < 0.001). Non-paretic SL error decreased with speed regardless of condition. SL error was greater in the paretic limb overall (p < 0.001). Discussion and Conclusions: Fast walking alone did not reduce paretic SL errors. Participants with chronic stroke can effectively use visual biofeedback to reduce paretic SL errors at higher speeds, supporting its integration into high-intensity gait training to simultaneously treat biomechanical impairments and walking capacity deficits after stroke.

Muscle tissue is prone to changes in composition and architecture following stroke. Changes in muscle tissue of the extremities are thought to increase passive muscle stiffness and joint impedance. These effects likely compound neuromuscular impairments exacerbating movement function. Unfortunately, conventional rehabilitation is devoid of quantitative measures yielding to subjective assessment of passive joint mobility and end feel. Shear wave ultrasound elastography is a conventional tool used by ultrasonographers that may be readily available for use in the rehabilitation setting as a quantitative measure, albeit at the muscle-tissue level, filling the gap. To support this postulation, we evaluated the criterion validity of shear wave ultrasound elastography of the biceps brachii by investigating the relationship to a laboratory-based criterion measure for quantifying elbow joint impedance in individuals with moderate to severe chronic stroke. Measurements were performed under passive conditions at seven positions spanning the arc of elbow joint extension in both arms of twelve individuals with hemiparetic stroke. Surface electromyography was utilized for threshold-based confirmation of muscle quiescence. A significant moderate relationship was identified near end range of elbow extension and all metrics were greater in the paretic arm. Data supports the progression toward clinical application of shear wave ultrasound elastography in evaluating altered muscle mechanical properties in stroke stipulating the confirmation of muscle quiescence. Considering the lack of bedside robotics in clinical practice, shear wave ultrasound elastography will likely augment the conventional method of manually testing joint mobility. Tissue-level measurement may also assist in identifying new therapeutic targets for patient-specific impairment-based interventions. New & NoteworthyMethods for quantifying passive (non-reflex mediated) joint mobility are absent in stroke rehabilitation. Rehabilitation specialists are left to subjective assessment of the impact on function. Here, we compare the application of shear wave ultrasound elastography for estimating mechanical properties of muscle with a robotic method (criterion measure) of measuring passive elbow extension joint impedance. Data support the clinical application of shear wave ultrasound elastography, especially with the absence of bedside robotics.

BackgroundSevere paresis of the contralesional upper extremity is one of the most common and debilitating post-stroke impairments. The need for cost-effective high-intensity training is driving the development of new technologies, which can complement and extent conventional therapies. Apart from established methods using robotic devices, immersive virtual reality (iVR) systems hold promise to provide cost-efficient high-intensity arm training. ObjectiveWe investigated whether iVR-based arm training yields at least equivalent effects on upper extremity function as compared to a robot-assisted training in stroke patients with severe arm paresis. Methods52 stroke patients with severe arm paresis received a total of ten daily group therapy sessions over a period of three weeks, which consisted of 20 minutes of conventional therapy and 20 minutes of either robot-assisted (ARMEOSpring(R)) or iVR-based (CUREO(R)) arm training. Changes in upper extremity function was assessed using the Action Research Arm Test (ARAT) and user acceptance was measured with the User Experience Questionnaire (UEQ). ResultsiVR-based training was not inferior to robot-assisted training. We found that 84% of patients treated with iVR and 50% of patients treated with robot-assisted arm training showed a clinically relevant improvement of upper extremity function. This difference could neither be attributed to differences between the groups regarding age, gender, duration after stroke, affected body side or ARAT scores at baseline, nor to differences in the total amount of therapy provided. ConclusionThe present study results show that iVR-based arm training seems to be a promising addition to conventional therapy. Potential mechanisms by which iVR unfolds its effects are discussed. Registry numberDRKS00032489

Conventional therapy after stroke focuses on reducing physical impairments. However, the decisions that guide peoples movements may have far-reaching consequences towards recovery. We lack the tools to characterize these decisions. Recently, researchers have created a quantitative behavioral assessment of effort-based decision-making and applied it to some clinical populations. The purpose of this paper is to examine the feasibility of evaluating effort-based decision-making during walking after stroke. We recruited five neurotypical participants in an initial study. We conducted a subjective effort valuation on the neurotypical individuals with and without a knee immobilizer to simulate the biomechanics of reduced knee flexion during post-stroke gait. Participants cleared obstacles of varying heights during overground walking, followed by rating their perceived effort and then completing an effort choice paradigm to calculate subjective effort value. In a second experiment, we recruited five individuals with stroke to perform a similar protocol without an immobilizer during harnessed treadmill walking. We found that rated perceived effort increased monotonically with obstacle height across groups, that individuals could recall obstacle heights without cues, and that subjective effort value increased with knee immobilization in the control group as expected. We conclude that adapting an effort-based decision-making assessment to a walking context in people with stroke is feasible.

This study aimed to develop a novel rehabilitative approach for post-stroke hand movement using a simple detached robotic hand and synergistic torso muscle activities for reaching and to perform a pilot test on its functionality and feasibility. In reference to a mental practice that does not activate hand muscles, enhanced cognitive engagement would be achieved without hand activation using the externally present, visible, and audible robotic hand by activating the non-hand muscles associated with hand function. A simple and low-cost robotic hand was developed and placed distal to the hidden resting hand as if it were a functional extended hand. The opening and closing motions of the detached robotic hand were controlled by electromyogram of the anterior and posterior torso muscles associated with reaching and retrieving while providing visual and auditory feedback. The functionality of the developed system was confirmed on the repeatability of the range of duration, excursion, and response time with low variability within an acceptable range. An able-bodied adult and five mildly impaired stroke survivors embodied the detached robotic hand by successfully controlling it with or without concurrent testing of their biological finger. In the concurrent finger tests, increased reactive force and hand muscle activity were observed in most participants. These observations confirmed that the developed approach that controls a detached robotic hand with reaching-associated torso muscles is functional and applicable to stroke survivors with and without involving the biological human hand. The robotic hand system detached from the user and controlled by the voluntary effort of their reaching-associated torso muscles has enabled future studies to examine the efficacy of synergistic muscle-robot interaction as a potential rehabilitation tool.

Background: Prolonging ambulation is an important treatment goal in children with Duchenne muscular dystrophy (DMD). Clinical management targets 'actionable' (i.e., modifiable) impairments, such as progressive muscle weakness and contractures, that underlie gait pathology. Gait classification may improve clinical decision-making, but the utility of gait classification in clinical practice depends on understanding how underlying, actionable impairments contribute to distinct gait patterns, which remains insufficiently understood. The research questions were: (1) Can DMD gait patterns be accurately classified from actionable impairments? and (2) Can the model's predictions be explained, and do these explanations provide clinical utility and increase trust in the model? Methods: A retrospective dataset of 274 lower-limb observations from 137 assessments in 30 boys with DMD was analyzed, including 3D gait analysis, instrumented strength assessment, and clinical examination (manual muscle testing, goniometry and clinical stiffness scale). Observations were classified into the mildly affected, tiptoeing, or flexion gait pattern. Ten predictors representing actionable impairments were included: nine predictors related to muscle weakness and contractures, and body mass index (BMI). A balanced random forest classifier was evaluated with leave-one-group-out cross-validation. Model interpretability was explored using SHapley Additive exPlanations to generate global and local explanations. An interview with a clinical expert assessed the utility of the explanations as the primary outcome, with trust in and expectations of both the model and the explanations as secondary outcomes. Results: The model achieved an accuracy of 74.5%. Global explanations identified hip and knee weakness, gastrocnemius-soleus contractures, and BMI as the most important predictors across gait patterns. Local explanations illustrated how patient-specific impairments informed individual predictions. The user study demonstrated the clinical utility of the explanations, as they were perceived as interpretable, provided useful insights, and these insights were actionable. The explanations largely aligned with the expectations and increased self-reported trust in the model. Conclusions: Gait patterns in DMD can be predicted from clinically actionable impairments, and explainable artificial intelligence can translate model outputs into meaningful clinical insights. This approach is promising for supporting both general and personalized rehabilitation and orthopedic strategies aimed at prolonging ambulation in DMD. Further validation in larger, multi-center cohorts is needed.