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.

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.

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.

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.

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.

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.

Purpose: Lower-limb prosthesis use involves interacting physical, psychosocial, and device-related outcomes that may not be fully captured by conventional clinical assessment. This study aimed to develop and evaluate a stakeholder-informed framework of outcome domains relevant to meaningful everyday prosthesis use. Materials and Methods: A mixed-methods participatory design comprised a structured synthesis of selected clinically relevant content from five established patient-reported outcome measures; semi-structured interviews and importance and actionability ratings with 18 contributors (12 prosthesis users, four clinicians, and two industrial partners); and integration of the synthesis, qualitative, and rating findings. Interview records were analysed using reflexive thematic analysis, and ratings were analysed descriptively. Results: The resulting framework comprised four interrelated domains: Mobility, Physical Function, Psychosocial Wellbeing, and Prosthesis Experience. Mobility showed the clearest convergence across stakeholder perspectives. Prosthesis users showed the largest importance actionability gap for Prosthesis Experience (4.5 vs 3.0), whereas clinicians showed the largest gap for Psychosocial Wellbeing (5.0 vs 3.0). Interviews highlighted day-to-day variability in prosthesis use and the influence of confidence, fatigue, comfort, environmental conditions, social context, and device usability. Conclusions: Meaningful outcome assessment in prosthetic rehabilitation should extend beyond mobility alone to consider physical function, psychosocial wellbeing, and prosthesis experience within everyday contexts. The proposed framework provides a stakeholder-informed foundation for multidimensional outcome assessment in prosthetic rehabilitation.

Objective Conduct a randomized controlled trial to investigate the safety, feasibility, and efficacy of wearable vibrotactile stimulation shirts and gloves for upper extremity stroke rehabilitation at the inpatient rehabilitation unit. The primary outcome measure was the Fugl-Meyer Assessment Upper Extremity (FMA-UE) motor score. The secondary outcome measures were the Modified Ashworth Scale (MAS) and statistics on device safety and feasibility. Exploratory outcome measures were the FMA-UE sensation, passive joint motion, and joint pain scores. Also, the MAS for the fingers, wrist, and elbow scores. Methods A total of 24 ischemic stroke patients were recruited for this study (45 to 85 years old) during their stay at the inpatient rehabilitation unit, which averaged 15.3 {+/-} 4.5 days. Patients were randomly assigned to a treatment or control group, with 12 participants in each group. The control group received conventional therapy only, while the treatment group received both conventional therapy and vibrotactile stimulation. Participants in the treatment group wore the vibrotactile stimulation gloves and shirts for 1.5 hours per day, 5 days per week. Upper extremity impairment and spasticity were assessed with the FMA-UE and MAS at both admission and discharge from the rehabilitation unit. In addition, feedback from patients in the treatment group was collected through a questionnaire to evaluate comfort, usability, and satisfaction. The trial was prospectively registered at ClinicalTrials.gov (NCT06244719). Results The vibrotactile stimulation was safe and well-tolerated by stroke patients. No statistically significant differences were noted in FMA-UE motor scores and total MAS scores between the groups; however, exploratory analyses revealed a significant improvement in FMA-UE joint pain scores in the treatment group. A trend towards reduced wrist spasticity was observed in the treatment group, but this effect did not remain significant after correcting for multiple comparisons. Responder analyses showed a greater proportion of responders in the treatment group for both FMA-UE and MAS outcomes. Conclusions Our results show promise for a larger sample size study and follow-up work with longer durations of vibrotactile stimulation with gloves and shirts for upper extremity stroke rehabilitation. Although no significant improvements were observed in FMA-UE motor function or total MAS scores, vibrotactile stimulation was associated with reduced joint pain, potential benefits for distal spastic hypertonia, and increased responder rates.

Introduction: Several gait rehabilitation approaches after stroke rely on explicit feedback to promote task-specific voluntary corrections of walking patterns. While these approaches show effectiveness at a group level, individual responses to voluntary corrections can differ, limiting the benefits and translation of task-specific gait interventions. Our goal is to identify biomechanical, neuromuscular, and cognitive characteristics associated with the ability to perform voluntary corrections of walking using explicit visual feedback in people with chronic stroke. Methods: Twenty-eight individuals with chronic stroke completed a single-session treadmill walking protocol, consisting of baseline walking, a voluntary correction condition guided by real-time visual feedback, and a short retention trial without feedback. Reducing step length asymmetry was used as the target to guide voluntary corrections. Clinical assessments included measures of motor impairment, balance, gait function, cognition, and walking capacity. Muscle coactivation was characterized using dimensionality reduction. Associations of clinical assessments with baseline step length asymmetry and residual error in step length asymmetry during voluntary correction were examined using univariate analyses and multivariate regression with LASSO-based variable selection. Results: Eighteen participants successfully reduced step length asymmetry using visual feedback, while ten participants did not reduce asymmetry. Greater residual asymmetry during voluntary correction was independently associated with greater baseline asymmetry, greater lower extremity motor impairment, reduced balance, and increased paretic muscle coactivation (adjusted R2 = 0.46). Neither the direction of asymmetry nor cognitive outcome measures were associated with the ability to correct asymmetry during walking. Immediate retention after feedback removal was limited, with only 4 participants maintaining improvements. Discussion: The ability to perform voluntary corrections of the walking pattern using voluntary corrections after stroke is constrained by motor impairment, balance function, and muscle coactivation. These findings suggest that explicit, feedback-based gait interventions to guide voluntary corrections may benefit individuals with mild to moderate impairments, while individuals with more severe impairments require alternative strategies to guide corrections of the walking pattern.

Background: Gait impairment is common among people with multiple sclerosis (PwMS) and is an important marker of disease progression. However, gait assessments typically require in-person evaluations. Objective: To describe the pose-estimation-based method for estimating spatiotemporal gait parameters from a single consumer-grade video, and evaluate the feasibility of home video collection by PwMS. Methods: In a single-center longitudinal digital phenotyping study, ambulatory adults with MS completed a standardized walking task recorded in the frontal plane. Pose estimation (MediaPipe Pose, Ultralytics) and custom scripts were used to estimate gait parameters from videos. Participants were invited to record walking videos at home using personal devices. Adoption and technical feasibility were evaluated across two home video data acquisition phases, with iterative protocol refinements. Results: The in-clinic study included 132 participants; 55 contributed home videos. In Phase I, while home video adoption was low (45% [30/66]), 87% [26/30] uploaded [&ge;]1 video of sufficient quality for gait analysis. After protocol refinements, 100% [25/25] uploaded [&ge;]1 high-quality video. Overall, high-quality frontal-plane videos were obtained at similar rates at home (92% [97/105]) and in-clinic (91% [423/467]). Conclusions: Home walking videos can feasibly be collected by PwMS to estimate gait parameters, providing an accessible approach for remote gait monitoring.

Impaired motor control post-stroke results in reduced walking speeds and increased gait variability. This variability reduces reliability and makes identifying longitudinal changes via gait analysis difficult since changes may occur within the margin of measurement error. We quantified intra-class correlation coefficients (ICC) and minimal detectable change (MDC) in post-stroke individuals and neurotypical individuals walking at matched speeds, to isolate the impact of gait speed and post-stroke impairments on gait-analysis reliability. We collected gait data over two days from N=15 post-stroke individuals walking on a treadmill at their self-selected speed, and from N=13 age- and sex-matched neurotypical controls walking at both their self-selected speed and a speed matched to a post-stroke participant. We calculated ICC and MDC values for spatiotemporal variables, bilateral joint ranges of motion (ROM), and bilateral peak propulsive and peak vertical ground reaction forces (GRF). Spatiotemporal ICCs showed excellent reliability across groups (range [0.813-0.988]), yet MDC values were greater post-stroke than in speed-matched controls. ICCs for joint ROM ranged from poor to excellent reliability across groups ([0.362-0.960]). Post-stroke joint ROM MDCs were 27%-53% of the gait ROM compared to 11%-42% in neurotypical controls. ROM MDCs were greater in the non-paretic compared to the paretic extremity. ICC for peak GRFs showed good to excellent reliability across groups (range [0.778-0.980]), with post-stroke peak GRF MDCs greater than in speed-matched controls. Our results suggest that stroke related neuromotor impairments influence reliability beyond the effects of walking speed alone, and we provide quantitative MDC benchmarks for interpreting gait changes post stroke following clinical interventions.

People with lower limb amputation lack somatosensory feedback from their prosthesis, and this loss contributes to functional deficits, including balance and gait impairments. Recent advances in neuroprostheses have demonstrated that electrical stimulation of sensory nerves in the residual limb and spinal cord can restore lost sensations in the lower limb. To maximize the efficacy of these somatosensory neuroprostheses, the restored sensations should be intuitive, seamlessly integrating into the sensorimotor network. However, it is challenging to quantify the intuitiveness of these evoked sensations. Recent studies have proposed using crossmodal congruency effect (CCE) tasks for this purpose in people with upper-limb amputation. The current study tests the feasibility of the CCE task for assessing the intuitiveness of sensory feedback in the lower limb. We hypothesized that CCE score would reliably differentiate between a more natural (pneumatic) sensation and a less natural (electric) sensation at two locations: the knee and the foot. Across fifteen able-bodied individuals, we observed that the CCE task differentiates sensory modalities at the knee, but not at the foot. Identification of external factors affecting the CCE is needed before it can be implemented to measure intuitiveness of sensory feedback in lower-limb amputees.

Background: Post-stroke motor impairment is the leading contributor to long-term disability. Despite evidence that high dose, high intensity (HDHI) and virtual reality (VR) interventions are effective in reducing post-stroke motor impairment, access to such interventions is limited, especially in community-based models. The purpose of this study was to explore the effect of one community-based HDHI VR intervention, Next-Generation NeuroAnimation Therapy (NG-NAT), on motor impairment for community-dwelling stroke survivors. Methods: The study employed a retrospective pre-test post-test design of de-identified data sets of one cohort of stroke survivors who participated in an HDHI NG-NAT intervention at a community-based center from March to December 2025. The intervention consisted of three hours of daily therapy, five days a week, for three weeks. Two hours were allocated for NG-NAT gameplay, while one hour focused on non-VR activity. The NG-NAT was provided in a small studio with a large screen monitor and 12 motion caption cameras mapping client movements to play the game. The upper extremity Fugl Meyer Assessment was used to measure motor impairment at pre- and post-testing. Linear regressions were run to determine the relational strength between pre- and post-UEFMA scores. Wilcoxon Signed Rank Tests were run to calculate median differences in pre- and post-UEFMA scores and account for non-parametric data distributions at baseline and the small sample size. Effect size was explored using the Rank Biserial Correlation. Frequency of minimally clinically important differences (MCID), minimal detectable changes (MDC), recovery stage transition were calculated. Content analysis and co-review of documentation contextualized statistical findings. Results: Nineteen participants completed three weeks of intensive NG-NAT. All experienced positive UEFMA score improvements from pre- to post-testing with a median difference of 8 points. Fifteen achieved MDC and MCID; one experienced a ceiling effect. Eight participants transitioned into better recovery stages. There was a highly significant, positive relationship with narrow confidence intervals and pre-score predicted post-score (e.g., those with mild/moderate impairment improved better than those with severe impairment). Conclusion: This study provides evidence supporting the efficacy of NG-NAT as a community-based intervention to reduce motor impairment for individuals with stroke. Given its ability to deliver intense and engaging therapy, NG-NAT offers a promising adjunctive strategy to expand access for stroke survivors to improve clinically relevant health outcomes. These findings underscore the need for pragmatic trials evaluating effectiveness, implementation, and cost-effectiveness.

We present three quantitative methods: 1) estimation of exoskeleton mechanical power and energy ratio from published data, 2) a systematic review of the exoskeleton literature on reported energy ratios, and 3) timing correction analysis of the replication experiment, to address concerns raised by Collins et al. (2026) about Luo et al. (2024). Together, these analyses support the reported metabolic reductions and the validity of exoskeleton control via learning in simulation. The critique rests on an unsupported premise: that exoskeleton energy ratios above 4 are physiologically implausible. This premise of Collins et al. (2026) is not supported by the cited evidence, and the error originates in their own cited source. Sawicki and Ferris (2009), the paper they invoke as authority for the limit of 4, state explicitly that "reported values of the muscular efficiency range from 0.10 to 0.34, with many sources assuming an average of [~]0.25." The value of 4 corresponds to this average, it is not a physiological ceiling. Treating an average as a physiological upper limit is a fundamental error. The published exoskeleton literature further contradicts the claim, including work by the authors of the critique themselves (Collins et al., 2015: 4.3; Young et al., 2017: 5.0) and independent work (Malcolm et al., 2013: 4.8; Seo et al., 2017: 6.7). In contrast, our walking energy ratio is 2.4, calculated directly from Fig. 4 of our paper. Our device delivers higher peak torque (14.1 Nm vs. 10.9 Nm, Lim et al., 2019) and achieves a slightly larger metabolic reduction (24.3% vs. 21%). Independent groups have since demonstrated meaningful metabolic reductions using learning-in-simulation frameworks, including Barati et al. (2026, 15.2% mean and 22.5% maximum) and Zhou et al. (2025, [~]20% during running). The claim of Collins et al. (2026) that this problem "remains unsolved" is directly contradicted by these independent results. The experiment in the critique is not a valid replication of our method. Our controller is a neural network with [~]10,000 parameters learned through deep reinforcement learning in musculoskeletal simulation; the critique instead applies a pre-programmed fixed torque curve with no learnable parameters. Beyond this, the replication contains three methodological errors: 1) a heel-strike timing assumption producing offsets up to 30% of the gait cycle; 2) an averaged torque profile that discards subject-specific control; and 3) a device [~]50% heavier than ours (4.8 kg vs. 3.2 kg) without measuring the metabolic penalty of the added weight. The critique also misreports Samsung data, with reported values approximately double those in the original publication, errors that directly underpin their physiological limit argument.

Sensorimotor adaptation is a motor learning process that contributes to movement flexibility and is thought to arise from the interaction of fast and slow adaptive processes. Evidence suggests that declarative memory contributes to adaptation through its influence on the fast process. Although adaptation deficits are common following stroke, the mechanisms underlying these deficits remain unclear. This study investigated differences in locomotor adaptation rate and forgetting between individuals with chronic stroke and age-matched controls and examined how these measures were associated with immediate declarative memory performance. Individuals with chronic stroke (n = 23) and age- and education-matched controls (n = 21) completed four 4-minute bouts of split-belt treadmill adaptation separated by rest breaks. Adaptation rate, adaptation magnitude, and forgetting were quantified from exponential fits to normalized step-length asymmetry data. Immediate declarative memory was quantified using the Repeatable Battery for the Assessment of Neuropsychological Status, and associations between adaptation measures and immediate declarative memory were evaluated using robust linear regression. Participants with stroke adapted less (p = 0.001) and more slowly (p = 0.039) than controls during early adaptation and forgot less of the adapted behavior during the first rest break (p = 0.024). Notably, poorer immediate declarative memory performance was associated with reduced forgetting during the initial rest break, irrespective of group assignment (p = 0.035). This relationship supports the hypothesis that declarative memory contributes to adaptation through a cognitively mediated fast process. These findings suggest that cognitive impairment contributes to altered adaptation following stroke and highlight the importance of considering cognitive factors when investigating motor learning mechanisms and rehabilitation outcomes in neurological populations.

Background: Occupational therapists can improve stroke survivors hand and arm movement and participation in daily activities through action observation (AO). AO involves watching another persons hand or arm complete a movement or task. While research generally supports the use of AO with stroke survivors, there are limited AO videos are available to occupational therapists which makes applying AO challenging. Objective: The purpose of this work is to develop structured and widely accessible tool to support access to AO for stroke survivors, occupational therapists, and researchers. Methods: To develop an AO video library for stroke rehabilitation, functional and non-functional upper limb task deficits were first identified through clinical observations and clinician interviews to establish a prioritized list of daily activities. In collaboration with media production specialists, healthy adult volunteers were recruited and filmed performing these tasks from both first- and third-person perspectives. The recorded videos were then systematically edited, enhanced with instructional title slides, and distributed via a public YouTube channel for clinical application and a categorized digital repository for research purposes. Results: Initial assessments revealed a complete lack of familiarity, awareness, and utilization of AO resources among local occupational therapists, despite high perceived clinical utility. To address this gap, a final library of 150 tasks was established, resulting in the production of 419 finalized, standardized videos featuring six healthy volunteers. For clinical application, these videos were hosted on a free, public YouTube channel organized into 18 functional playlists, while a parallel set was structured into distinct movement categories for research repository storage. Conclusion: By providing a structured and highly accessible tool, this repository enables clinicians, researchers, and caregivers to readily implement evidence-based action observation interventions in both clinical and home settings.

Background. Abnormal muscle co-activation, also called abnormal synergies by clinicians, is an important contributor to arm impairment after stroke. While abnormal co-activation is well-described in chronic stroke, it remains unclear how early abnormal patterns appear and whether their spatial and temporal characteristics resemble those seen in the chronic phase. We sought to determine how soon after stroke abnormal muscle co-activation appears. Methods. In this cross-sectional study, thirty-nine participants with hemiparesis in the early subacute period (<21 days) and sixty-eight participants in the chronic period (>6 months) after stroke performed targeted reaching movements while surface electromyography (EMG) was recorded from nine upper-limb muscles. Muscle synergies (patterns of coordinated muscle activation) were identified using non-negative matrix factorization. Synergy composition (spatial structure) and activation profile (temporal structure) were compared across the contralesional arms of subacute and chronic participants and the ipsilesional arm, which served as the reference for normal coordination. Results. Three primary synergies accounted for most EMG variance during reaching in each arm group. A deltoid-dominant synergy characterized by abnormal co-activation of anterior and posterior deltoids, was present in both subacute and chronic stages in the contralesional arm but was absent in the ipsilesional arm. In addition, the elbow flexor synergy co-activated with the deltoid synergy in both contralesional groups but not in the ipsilesional arm. Abnormal co-activation between elbow flexor and elbow extensor synergies was also seen in contralesional, but not ipsilesional, arms. These abnormalities were already present 15 days after stroke and did not differ between subacute and chronic groups. Conclusions. Abnormal muscle co-activation appears within the first few weeks after stroke and persists in chronically impaired survivors. Its full development this early suggests these patterns arise rapidly rather than emerging gradually during recovery, and that interventions targeting abnormal co-activation may be most useful when applied early. Clinical Trial Registration? NCT03401762.

Human-in-the-loop optimization (HILO) is an established method for identifying subject-specific optimal controllers for performance augmentation. For HILO algorithms to be useful in rehabilitation, however, the optimization algorithm may need to account for how the human response changes over time in response to assistance. In this study, we tested a modified version of Bayesian optimization (BO), dynamic Bayesian optimization (DBO), in a three-parameter optimization problem that sought to identify participant-specific optimal solutions for increasing walking speed. As opposed to BO, DBO accounts for the non-stationarity of human responses. Sixteen healthy participants received bilateral hip torque pulses delivered by a hip exoskeleton. The exoskeleton torque parameters were determined using HILO with either DBO or BO. Validation iterations were introduced to objectively compare performance across optimizers at different time points of HILO. The results showed that both DBO and BO significantly increased walking speed compared to baseline. When comparing performance between DBO and BO, DBO emerged as an improvement over BO both in terms of efficacy, modeling accuracy, and personalization. DBO induced changes in walking speed relative to baseline that exceeded those induced by BO at three of the four validation iterations. DBO outperformed BO in modeling accuracy in later validation iterations. DBO personalization induced changes in walking speed that were significantly greater than those induced by previously identified assistive solutions, while this was not the case of BO. Overall, our results indicate that DBO outperformed BO due to its greater ability to account for non-stationary aspects of the human response.