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.

Background: Gait propulsion (anterior ground reaction force) is commonly impaired after stroke. Force plates and wearable sensors can quantify propulsion but are not routinely available in clinical settings. Walking speed alone does not adequately capture the heterogeneity of propulsion deficits. Objective: To determine whether standardized clinical measures can estimate post-stroke propulsion metrics and classify propulsion impairment. Methods: Forty individuals post-stroke participated: 33 for model development and cross-validation (11 with repeated observations for longitudinal validation) and 7 for independent-cohort validation. Clinical measures included the 6-Minute Walk Test (6MWT), comfortable walking speed (CWS), Functional Gait Assessment (FGA), Fugl-Meyer Assessment of the Lower Extremity (FMA-LE), Short Physical Performance Battery (SPPB), Timed Up and Go test (TUG), and stroke chronicity. Regression models were developed to estimate three propulsion metrics: paretic propulsion peak and impulse, and interlimb propulsion symmetry. Model performance was evaluated using R2 and RMSE. Propulsion impairment was classified from symmetry estimates using a prespecified cutoff of 31.5%, with classification accuracy reported. Results: Models using the 6MWT, SPPB, TUG, and chronicity estimated paretic propulsion peak (R2 = 0.77, RMSE = 3.00 %body weight) and impulse (R2 = 0.81, RMSE = 0.65 %body weight {middle dot} %stance phase). FMA-LE, SPPB, TUG, and chronicity estimated propulsion symmetry (R2 = 0.75, RMSE = 8.56%) and classified propulsion impairment with 84.8% accuracy. Classification accuracy was 91.7% in longitudinal validation and 85.7% in independent-cohort validation. Conclusions: A multidomain clinical assessment framework may support practical and accurate estimation and classification of post-stroke propulsion impairment in clinical settings where specialized instrumentation is unavailable.

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: 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.

Introduction: Approximately 40-80% of individuals post-stroke fail to use their paretic arm despite sufficient capacity, a phenomenon known as arm nonuse. This behavior arises from an interaction between cognitive and motor factors, necessitating evaluation approaches that capture the impact of complex task demands on arm nonuse. However, existing measures require specialized equipment, extensive setup, or prolonged monitoring, limiting their clinical feasibility. To address this gap, this study evaluated the reliability, validity, and usability of a novel virtual reality-based arm nonuse (VR-NonUse) tool designed to measure arm nonuse during naturalistic reaching under varying cognitive and motor demands. Methods: Fifty-one individuals with stroke and 23 controls completed the VR-NonUse assessment across three conditions varying in cognitive and motor demand. Usability and tolerability were evaluated. Concurrent validity was examined by assessing associations between paretic arm nonuse on the VR-NonUse tool and established nonuse assessments. Discriminant validity was evaluated by comparing these associations with a motor impairment measure. Construct validity was examined by determining the effects of cognitive and motor demand on arm use. Results: The VR-NonUse tool was usable and well-tolerated. Paretic arm nonuse on the VR-NonUse tool was associated with established nonuse measures, and more strongly related to nonuse than to motor impairment, supporting concurrent and discriminant validity. Higher cognitive demand reduced paretic arm use, supporting construct validity. A brief cognitively demanding condition yielded a robust index of nonuse. Discussion: The VR-NonUse tool is a reliable, valid, and practical assessment of arm nonuse in chronic stroke.

Importance: Upper extremity impairment is a leading cause of post-stroke disability, yet clinically approved neuromodulation therapies are currently limited by the risks and costs of surgical implantation. There is an unmet need for non-invasive brain stimulation. Objective: To evaluate the feasibility, safety, and preliminary clinical efficacy of a novel form of non-invasive brain stimulation, kilohertz transcranial magnetic perturbation (kTMP), for upper limb motor recovery after stroke. Design: A single-center, open-label trial was conducted between September 2025 and April 2026 and comprised a 4-week standard-of-care baseline period, 4 weeks of active treatment with kTMP combined with motor training, and a 4-week follow-up. Setting: Academic research center. Participants: Seven individuals with chronic stroke and upper extremity motor deficits. Intervention: Four weeks of kTMP (3 sessions/week), a brain stimulation approach using magnetic induction to generate subthreshold electric fields, paired with motor tasks. Main Outcomes: Change in Fugl-Meyer Assessment for Upper Extremity (FMA-UE) scores across study periods (standard-of-care baseline, active treatment, follow up). Secondary outcomes included measures of movement smoothness (Spectral Arc Length, SPARC) and fragmentation (mean velocity peak count), and intervention tolerability ratings. Results: Across 7 participants (mean [SD] age, 57.4 [8.7] years; 4 males; mean [SD] time poststroke, 3.5 [2.1] years). No device-related adverse events occurred, and the intervention was well-tolerated (participants rated sensation intensity at an average of less than 0.5 on a 0-10 scale). FMA-UE scores remained stable during the baseline period (mean change, +0.14 points; 95% CI, -1.21 to 1.50; adjusted P = .98), increased significantly following the kTMP-intervention (mean change, +7.00 points; 95% CI, 5.31 to 8.69; adjusted P = .047), and were sustained at the 4-week follow-up (mean change, -1.33 points; 95% CI, -2.42 to -.25 points; adjusted P = .13). Movement smoothness improved significantly within 1 week of kTMP onset (mean change, 1.48 points; 95% CI, .49 to 2.47; adjusted P = .03); movement fragmentation showed a similar but nonsignificant trend. Conclusions and Relevance: This pilot trial provides preliminary evidence that kTMP combined with motor tasks is safe, feasible, and may support upper extremity recovery. These pilot data warrant large, randomized controlled trials to evaluate efficacy in support of poststroke motor recovery. Trial Registration: ClinicalTrials.gov Identifier: NCT06317194

ABSTRACT Background Stroke often leads to upper-extremity motor impairments, including abnormal neuromuscular coordination (i.e., motor modules), which reduce independent joint control and impair activities of daily living. Conventional rehabilitation mainly targets the resultant motor deficits (e.g., impaired movement) rather than the underlying neuromuscular dysfunction, potentially limiting transferability of gains beyond trained tasks. Therefore, we propose a customized, motor module-guided exercise facilitated by human-machine interaction to improve neuromuscular coordination and reduce motor impairment. Methods Five chronic stroke survivors with severe-to-mild upper extremity impairment participated in a six-week motor module-guided intervention in which they learned to selectively activate muscle groups identified in healthy individuals. In addition, three stroke survivors completed a force-guided intervention as an active control group. Standardized clinical assessments, including the Upper Extremity Fugl-Meyer Assessment and Action Research Arm Test, were performed before and after training and repeated one and three months later to assess retention. Neuromuscular coordination was quantified using non-negative matrix factorization of surface electromyography, and inter-joint coordination was evaluated during dynamic tasks using a motion capture system before and after training. Results Four of the five participants in the motor module-guided intervention improved neuromuscular coordination. These participants also showed clinically meaningful improvements in motor impairment, which were generally maintained at one and three months after training. In contrast, although the force-guided group showed improvements in task-specific performance and force control, no significant changes in neuromuscular coordination or motor impairment were observed. Notably, improvements in motor impairment correlated with enhanced stroke-induced motor module composition. Finally, findings suggest potential transferability of enhanced neuromuscular control from the trained isometric condition to untrained dynamic behaviors. Conclusion This pilot study showed that motor module-guided rehabilitation could induce improvement of stroke-affected neuromuscular coordination, even in severely impaired chronic stroke survivors, reducing motor impairment and improving motor control beyond the trained task. These findings support neuromuscular coordination-guided rehabilitation as a promising framework for post-stroke recovery.

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.

Background: Motor imagery neurofeedback (MI-NF) engages sensorimotor brain networks and has shown potential for motor recovery after stroke. While laboratory-based studies have demonstrated neural and behavioral effects, evidence on real-world MI-NF training and its impact on brain activity and connectivity in stroke survivors remains limited. The Motor Imagery Neurofeedback Training in Stroke (MINTS) study investigates the effects of intensive, home-based MI-NF training using mobile EEG in chronic stroke survivors. Specifically, the study examines whether MI-NF training induces changes in neural activity and functional connectivity and whether these changes are accompanied by improvements in upper-limb motor function. Additionally, the study explores the temporal evolution of neural and behavioral changes across repeated baseline and training measurements. Methods: MINTS is a prospective, single-group, multimodal intervention study using an across-subjects multiple-baseline design. Twenty-one individuals with chronic stroke (at least 6 months post-stroke) and persistent upper-limb motor impairments undergo a pre-assessment phase, a variable-length baseline period, an intervention phase with 14 MI-NF training sessions, a post-assessment phase, and an exploratory three-month follow-up. MI-NF training is performed every second day at participants' homes using mobile EEG. Repeated baseline and training measurements include grip strength, resting-state EEG, motor imagery tasks with and without neurofeedback, and daily-life movement monitoring. Pre- and post-assessments comprise comprehensive motor and cognitive testing, stationary EEG, and functional MRI. Primary outcomes are resting-state EEG coherence as an index of functional connectivity and MI-related EEG activity derived from mobile EEG within the multiple-baseline design. Secondary outcomes include MI-related EEG activity derived from stationary EEG, fMRI-based measures of brain activity and connectivity, clinical motor outcomes, and quantitative movement characteristics derived from movement sensors and video recordings. Tertiary outcomes and exploratory directions address broader motor and cognitive performance, associations between neural and behavioral changes, lesion-related effects, and persistence of training-related changes. Discussion: By combining intensive home-based MI-NF training with repeated multimodal assessments, MINTS is designed to characterize neural and behavioral dynamics of motor recovery after stroke and to evaluate the potential clinical relevance of MI-NF training in everyday life settings. Trial Registration: The study protocol was registered in the German Clinical Trials Register (DRKS00036147) on 25 March 2025.

Stroke-induced hemiparesis often results in slow walking and an asymmetrical gait; however, these impairments vary widely across patients. Indeed, some individuals walk fast despite severe gait asymmetry, whereas others walk slowly despite mild asymmetry. We posit that speed and symmetry reflect distinct dimensions of walking and may serve as complementary biomarkers of stroke recovery. In this cross-sectional study, 57 individuals with chronic hemiparesis were classified as "slow" or "fast" and as having "severe" or "mild" asymmetry, using pre-defined thresholds (i.e., speed = 0.8 m/s; symmetry = 31.5%). Kruskal-Wallis and regression analyses were used to evaluate differences in gait biomechanics (i.e., limb and joint propulsive power and the metabolic cost of walking) and ambulatory function (i.e., speed, distance, and functional balance) associated with these impairment classifications. When examined independently, slower speeds and greater asymmetry were similarly associated with a higher metabolic cost of walking ({Delta} range: 77% to 83% higher, p < 0.05) and reduced balance and walking function ({Delta} range: 26% to 82% lower, p < 0.05). However, the two classification approaches revealed different underlying biomechanical mechanisms: whereas slower walking corresponded to reduced paretic propulsive power ({Delta} : 71% lower, p < 0.05), greater asymmetry indicated a distal-to-proximal shift in power generation ({Delta} : 200% shift from the ankle to the hip, p < 0.05). Furthermore, multiple regression analyses revealed that each classifier was an independent predictor of metabolic cost (R2=0.70, p < 0.001) and ambulatory function (R2 range: 0.34 to 0.55, p < 0.001). These findings demonstrate the complementary diagnostic value of measuring both walking speed and propulsion asymmetry, supporting the development of multi-dimensional phenotypes to enable precision neurorehabilitation.

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.

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.

Markerless motion capture offers a practical approach to biomechanical assessment in clinical populations outside of controlled laboratory environments. The purpose of this study was to evaluate the utility of markerless motion capture for quantifying static postural control impairment in individuals with stroke. Participants with subacute stroke (n=26) and healthy controls (n=171) performed a 30-second quiet standing task across three data collection sites in Kingston, Ontario, Canada. Video data were processed using Theia3D, and pelvis center of mass (COM) trajectories were extracted. Five discrete COM parameters were computed and transformed into Z-scores using normative models developed from the healthy population. An overall Task Score was calculated by aggregating parameter Z-scores. Forty-six percent of participants with stroke were identified as impaired on the task (Task Score > 95th percentile of the healthy distribution). At the parameter level, impairments were more prevalent in the mediolateral (ML) than anteroposterior (AP) direction (root-mean-square distance: 50% impaired ML vs. 12% impaired AP; mean velocity: 46% impaired ML vs. 35% impaired AP). Task scores and several task parameters demonstrated significant correlations with Berg Balance Scale scores (r = -0.64 to -0.81) and Functional Independence Measure Motor Sub-Scale scores (r = -0.62 to -0.77), with poorer performance associated with lower clinical scores. These findings demonstrate the utility of markerless motion capture for quantifying static postural control impairment following stroke. Future work should extend this framework to evaluate impairment in more dynamic postural control and gait tasks.

Background: Post-stroke spasticity is commonly assessed using the Modified Ashworth Scale (MAS), despite its limited objectivity and variable reliability. This study introduces ScoreE, a novel isokinetic dynamometry-derived metric designed to quantify elbow and wrist flexor hyper-resistance by comparing patients torque-angle profiles with a healthy reference corridor. Methods: Thirty-five post-stroke patients and twenty-one healthy controls were included. Elbow and wrist hyper-resistance was assessed using the MAS and an isokinetic dynamometer in two testing conditions: seated on the dynamometer chair (ergo chair) and seated in the patient wheelchair. Two dynamometer-derived metrics were analyzed: ScoreE and peak torque (PeakT). Feasibility, inter-rater and test-retest reliability, and associations with MAS grades were assessed. Exploratory PRE-POST changes following a 10-min local vibration session were also described. Results: ScoreE showed good to excellent inter-rater and test-retest reliability across joints and testing conditions. Inter-rater ICCs ranged from 0.81 to 0.89, and test-retest ICCs from 0.76 to 0.93. PeakT showed moderate to good reliability. In contrast, MAS agreement was lower and more variable. ScoreE showed moderate associations with MAS grades, particularly at the elbow, with weaker and less consistent associations at the wrist. MAS assessment was faster, but all assessments were well tolerated, with median pain ratings of 0/10 in all testing conditions. PRE-POST vibration changes were small and heterogeneous, preventing formal assessment of responsiveness. Conclusion: ScoreE provides a reliable, objective, and visually interpretable quantification of post-stroke elbow and wrist flexor hyper-resistance. By comparing the full torque-angle profile with normative data, this approach preserves information on both the magnitude and angular span of abnormal resistance, supporting its potential for targeted longitudinal monitoring.

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.