Journal of NeuroEngineering and Rehabilitation

BackgroundChronic stroke-related gait impairment remains a major source of disability. InTandem is an autonomous neurorehabilitation system delivering individualized, progressive rhythmic auditory stimulation for home-based gait rehabilitation. ObjectivesTo evaluate: (1) engagement during a 12-week autonomous, home-based intervention, (2) changes in walking endurance and functional mobility, and (3) outcome differences across pre-defined engagement and baseline speed subgroups. MethodsThis pragmatic, decentralized trial enrolled adults [&ge;]6 months post-stroke with residual gait deficits. Participants were asked to complete 30-minute sessions 3x/week for up to 12 weeks. Engagement was primarily assessed as the proportion achieving moderate-to-high weekly usage (> 4 weeks; benchmark p1 = 0.60). Changes in 6-Minute Walk Test (6MWT) distances and Timed Up and Go (TUG) times were analyzed using linear mixed-effects models. ResultsOf the 204 who initiated the intervention, 81.9% (95% CI [0.76-0.87]) engaged at least 4 weeks, meeting the primary endpoint (p < 0.001). Overall, 58.1% achieved high engagement (> 9 weeks), 23.9% moderate engagement (4-8 weeks), and 18.1% low engagement ([&le;]3 weeks). Significant improvements in 6MWT distance (+ 26.1 {+/-}5.6 m; 95% CI [14.99, 37.22]) and TUG times (-1.45{+/-}0.31 s; 95% CI [-2.06, -0.84]) (p < 0.001) were observed. Engagement influenced effectiveness: each additional week engaged predicted a 5.82 m greater gain in the 6MWT (SE = 2.05; 95% CI [1.77, 9.87], p < 0.005). ConclusionsAutonomous home-based delivery of music-based rhythmic auditory stimulation achieved moderate-to-high engagement and improved walking endurance and functional mobility, supporting InTandem as a scalable approach to chronic stroke gait rehabilitation. Trial registrationTrial registration: Clinicaltrials.gov NCT06051539. Registered on 20 September 2023. https://clinicaltrials.gov/study/NCT06051539

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.

Neural feedback is important for healthy control of movement, and multiple neurological disorders (e.g., stroke, cerebral palsy, Parkinsons disease, incomplete spinal cord injury) can be described by how they impair healthy feedback or induce unhealthy feedback. Researchers have created numerous computational neuromusculoskeletal models controlled by simulated neural feedback mechanisms, but these models rarely represent actual human subjects and thus have not found practical application in treating patients with movement impairments. As a step toward designing patient-specific treatments for individuals with neurological disorders, this study used the Neuromusculoskeletal Modeling Pipeline to develop and evaluate a novel synergy-based feedforward (FF)+feedback (FB) model using a personalized, three-dimensional neuromusculoskeletal walking model of an actual human subject post-stroke. Experimental walking data collected from the subject were used to create the subjects personalized walking model. This model was used to calculate lower body muscle activations consistent with the subjects electromyographic, joint motion, and ground reaction data for 5 calibration walking cycles. Nominal FF synergy controls were calculated by averaging the muscle synergies that closely reconstructed the 5 cycles of muscle activations and associated joint moments simultaneously. These nominal FF controls were then scaled by 0, 25, 50, 75, 100, and 125%, and the gap in reproducing individual cycle muscle activations was filled by fitting FB synergy controls as a function of joint positions, velocities, and moments as surrogates for muscle lengths, muscle velocities, and tendon forces. Finally, the six synergy-based FF+FB models controlled the subjects personalized walking model in predictive simulations performed for 3 testing walking cycles withheld from calibration. The 100% FF model (which still had minimal FB) reproduced the testing walking cycles the most closely, and only the 75%, 100%, and 125% FF models generated near-periodic walking motions using initial conditions consistent with experimental values. The 0, 25, and 50% FF models could generate near-periodic walking motions only when the initial conditions were allowed to diverge substantially from experimental values. Our findings suggest that predictive simulations of walking using real experimental data may require a minimum level of feedforward control and sufficient fitting data to predict a subjects actual dynamically consistent motion.

A passive device that attaches to the feet, called an exotendon, can reduce the energetic cost of running at moderate speeds, but its efficacy and optimal design parameters at higher speeds are unknown. Identifying optimal parameters at new speeds experimentally would require many experimental trials with different exotendon designs, which is challenging for participants at higher running speeds. We developed a muscle-driven simulation framework to predict the effect of various exotendon designs on the energetic cost of running at an experimentally untested speed (4 m/s). We used these predictions to select four designs, which we evaluated experimentally as users ran at this speed. The framework correctly predicted that an exotendon that reduced energetic cost at 2.7 m/s would also reduce energetic cost at 4 m/s (10% predicted vs. 5.7% measured) and that a short, stiff exotendon and a long, compliant exotendon would not significantly reduce energetic cost. However, exotendon parameters predicted by the simulation to maximize energetic savings did not significantly reduce energetic cost when evaluated experimentally. There was variability between participants in both the magnitude of maximum energy savings and the exotendon condition associated with those savings. In a 5-km time trial performed with and without the exotendon condition that elicited the largest energy savings for each participant during the experiment, we observed a lower average heart rate (-3.9 {+/-} 3.8 beats/min; P=0.03; mean {+/-} standard deviation) and increased cadence (15.9 {+/-} 9.6 steps/min; P=0.002) when participants ran with the exotendon but did not observe a statistically significant difference in finishing time (-13.5 {+/-} 24.6 sec; P=0.3). These results demonstrate exotendons can reduce energetic cost across multiple running speeds and that predictive simulations provide a framework for guiding experiments to evaluate assistive device designs. Author summaryDesigning assistive devices that help people move more efficiently usually requires many experimental trials. These studies can be time-consuming and physically demanding, especially when testing multiple device designs. In this study, we explored whether computer simulations could help guide the design of an assistive device for running called an exotendon. The exotendon is a simple elastic band that connects the feet and can help runners use less energy. Previous experiments showed that the device reduces the energy needed to run at moderate speeds, but it was unclear whether it would also work at faster speeds or which design would lead to energetic savings. We first used simulations of human running to test many possible exotendon designs at a faster speed. These simulations allowed us to identify promising designs before conducting experiments. We then tested a small number of these designs with runners. The experiments confirmed that the exotendon can reduce the energy required to run at faster speeds, although the efficacy of different designs varied between individuals. Our results show that computer simulations can help researchers rapidly evaluate a variety of assistive device ideas and focus experimental testing on the most promising designs.

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.

PurposeQuantification of walking function, including joint motions, ground reactions, and joint loads, outside the lab is a growing research area. Because only joint motions can currently be measured outside the lab, researchers are utilizing tracking optimizations of walking to estimate associated ground reactions and inverse dynamic joint loads. However, foot-ground contact models used in such optimizations have been generic rather than personalized, which may limit the accuracy of estimated ground reactions and joint loads. This study compares the predictive capabilities of generic versus personalized foot-ground contact models. MethodsGeneric and personalized foot-ground contact models were evaluated in calibration and tracking optimizations performed using experimental walking data collected from three subjects in varying states of health. Foot-only calibration optimizations evaluated how well both models could reproduce experimental ground reaction and foot motion data while tracking both types of data simultaneously, while whole-body tracking optimizations evaluated how well both models could reproduce experimental ground reactions, joint motion, and joint load data while tracking only experimental joint motion data and achieving dynamic consistency. ResultsFor all three subjects and both types of optimizations, personalized foot-ground contact models reproduced experimental ground reaction, joint motion, and joint load data more accurately than generic foot-ground contact models. ConclusionPersonalized foot-ground contact models can improve the accuracy with which ground reactions and joint loads can be estimated via tracking optimizations of walking using only experimental motion data as inputs. Personalized models require little time and effort to calibrate using freely available software tools and should improve the accuracy of predictive simulations of walking as well.

BackgroundPersonalized computational neuromusculoskeletal models have great potential for optimizing the design of clinical treatments for movement impairments. While many software tools address specific parts of the model personalization and treatment optimization processes, they typically require significant programming experience to use and do not cover the full breadth of these two processes. Furthermore, published neuromusculoskeletal modeling studies typically do not provide all of the minute methodological details needed for others to reproduce the work. Consequently, researchers seeking to develop skills in the model personalization and treatment optimization processes face a steep learning curve due to the lack of detailed training materials that demonstrate both processes for real-life clinical problems using real-life subject movement data. MethodsThis article presents detailed training tutorials for the model personalization and treatment optimization processes using two real-life clinical problems and the Neuromusculoskeletal Modeling (NMSM) Pipeline. The first clinical problem involves the design of personalized gait modifications and high tibial osteotomy surgery for an individual with bilateral medial knee osteoarthritis, where the goal is to reduce the peak adduction moment in both knees to a specified target level. The second clinical problem involves the design of a synergy-based functional electrical stimulation prescription for an individual post-stroke with impaired walking function, where the goal is to equalize the propulsive and braking impulses between the two legs. Both tutorials were evaluated as course projects given to novice users in a combined undergraduate/graduate mechanical engineering course. ResultsBoth tutorials produced personalized neuromusculoskeletal models and associated dynamically consistent tracking optimizations that closely reproduced subject-specific experimental joint angles, joint moments, ground reaction forces and moments, and (if applicable) muscle activations measured during walking. Subsequent design optimizations predicted personalized treatments that achieved target values of peak knee adduction moments or propulsive and braking impulses. ConclusionsThe detailed step-by-step tutorials presented with this article are the first to walk users step-by-step through the entire process of creating personalized neuromusculoskeletal models and then using them to design personalized treatments for clinical problems. These tutorials can be used to introduce new users to the NMSM Pipeline and as projects in neuromusculoskeletal modeling courses.

BackgroundAdapted dance is a promising rehabilitation intervention for physical and psychosocial impairments in people with chronic stroke. However, in-person attendance is hindered by limited community ambulation, transportation, and schedule conflicts. At-home participation with a live-streamed dance program could address these issues, but psychosocial benefits may be diminished because of reduced social interactions. The primary objective of this study was to assess the feasibility and safety of a live-streamed dance program for chronic stroke. Secondary objectives were to characterize participants who choose live-stream vs in-person options and quantify pre-post changes in balance, gait and social connection. MethodPeople with chronic stroke were given the choice of attending a live-streamed adapted dance program either in-person or at home twice a week for 4 weeks. A priori feasibility criteria were tracked, and participants were characterized with self-report (Center for Epidemiologic Studies Depression Scale; CES-D) and performance-based measures (e.g., Montreal Cognitive Assessment, Chedoke McMaster Assessment) at baseline. Pre-post measures of secondary outcomes included gait speed, Mini Balance Evaluation Systems Test (Mini-BESTest), Activities of Balance Confidence Scale (ABC), and Inclusion of Community in Self scale (ICS). Unpaired median/mean differences in baseline clinical presentation were used to compare in-person and live-stream participants. Paired median/mean differences were used to examine change in secondary outcomes with dance. ResultsInterest and enrollment rates for both groups combined were 87% and 38% respectively. Of the 13 people who enrolled, 8 chose in-person and 5 chose live-stream. In-person and live-stream attendance rates were 83% and 89% respectively, and retention rates were 80% and 75% respectively. At baseline, the in-person group had greater depressive symptoms (CES-D score, median [IQR] difference: 11.5 [-21.5, -5]), and faster mean gait speed (-25.8cm/s [-50.98, 0.006]) than the live-stream group. There were no pre-post changes in secondary outcome measures. ConclusionsA live-streamed dance intervention featuring in-class and at-home participation is safe and feasible for people with chronic stroke. These results will inform a future randomized controlled trial to investigate the effects of a live-stream dance program with a longer duration while considering how factors such as gait function and mood may relate to the choice between in-person and at-home attendance.

BackgroundConventional evaluations of digital health interventions typically assess mean treatment effects, potentially masking heterogeneous impacts across the functional recovery distribution. Patients at the lower and upper tails of recovery trajectories may respond differently to AI-enhanced telerehabilitation, yet standard regression approaches cannot capture these distributional nuances. ObjectiveThis study applied Recentered Influence Function (RIF) quantile regression with Oaxaca-Blinder decomposition to examine how AI-enhanced telerehabilitation differentially affects functional recovery outcomes across the entire distribution, and to decompose observed disparities into explained (composition) and unexplained (structure) components. MethodsWe analyzed data from 486 post-stroke patients across three rehabilitation centres in Singapore (January 2023-December 2025). Patients received either AI-enhanced telerehabilitation (n=241) incorporating natural language processing-based progress monitoring and adaptive exercise prescription, or standard care (n=245). RIF-quantile regressions were estimated at the 10th, 25th, 50th, 75th, and 90th quantiles of the Functional Independence Measure (FIM) score distribution. Oaxaca-Blinder decomposition at each quantile partitioned group differences into composition effects (attributable to differences in observable characteristics) and structure effects (attributable to differential returns to those characteristics). ResultsThe AI-enhanced telerehabilitation group demonstrated significantly greater FIM improvements across all quantiles, with the largest effects at the 10th quantile ({beta} = 12.74, 95% CI: 8.92-16.56, p < 0.001) and 25th quantile ({beta} = 9.83, 95% CI: 6.71-12.95, p < 0.001), diminishing at the 90th quantile ({beta} = 3.21, 95% CI: 0.88-5.54, p = 0.007). RIF decomposition revealed that at the 10th quantile, 68.3% of the treatment-control gap was attributable to structure effects, indicating that AI-enhanced telerehabilitation fundamentally altered recovery mechanisms for lower-performing patients rather than merely leveraging differences in patient characteristics. ConclusionsAI-enhanced telerehabilitation produces its most pronounced benefits among patients at the lower end of the functional recovery distribution, suggesting a potential mechanism for reducing outcome inequality in stroke rehabilitation. RIF-quantile regression decomposition offers a methodologically rigorous framework for understanding distributional treatment effects that are invisible to conventional mean-focused analyses.

Background: Moderate- to high-intensity walking training (M-HIT) is an established intervention for improving walking capacity in chronic stroke. Musculoskeletal (MSK) adverse events commonly occur during M-HIT, yet tools to identify individuals at higher risk are limited. Baseline clinical characteristics may provide insight into susceptibility to training-related MSK adverse events during M-HIT. Thus, this study aimed to develop and internally validate a model for predicting MSK adverse events during a 12-week M-HIT program in chronic stroke using baseline clinical characteristics. Methods: Participants (n=100) from HIT-Stroke Trials 1 and 2 were included. Baseline clinical characteristics included measures of orthopedic history, pre-existing pain, motor function, recent exercise history, demographics and health characteristics, stroke chronicity, and psychological health. Logistic regression models evaluated all possible combinations of baseline characteristics with up to three predictors. Leave-one-out cross-validation was used for internal validation to mitigate overfitting. Predictive performance was quantified using the C-statistic, and the candidate model with the highest cross-validated C-statistic was selected as the final model. Results: MSK adverse events occurred in 32.0% of participants. The optimal three-variable model included prior orthopedic condition (Odds ratio [OR] 3.02 [95% CI 1.14-8.64]), Fugl-Meyer lower extremity motor score (OR 1.14 [95% CI 1.02-1.28]), and self-reported participation in regular walking exercise (OR 0.17 [95% CI 0.05-0.49]) at baseline. This model demonstrated moderate discrimination (cross-validated C-statistic = 0.74; apparent C-statistic = 0.78). Conclusions: Participants reporting at least one pre-existing lower extremity or lumbar spine orthopedic condition and those with better lower-extremity motor function exhibited greater odds of experiencing MSK adverse events during M-HIT, while participants reporting participation in regular walking exercise had lower odds. These findings suggest that baseline clinical characteristics may help identify individuals at elevated risk for MSK adverse events during M-HIT who may warrant closer monitoring or risk-reduction strategies. Future studies are needed for external validation. Clinical Trial Registration: https://ClinicalTrials.gov; Unique identifiers: NCT03760016, NCT06268041

There are many alternative methods to joystick control for control of Electric Powered Wheelchairs for users with neuromuscular disabilities, such as muscular dystrophy, and spinal cord injuries, such as tetraplegia. However, these methods- which include the sip-and-puff method, head and neck movement, blinking, or tongue movement- hinder social interaction, and are therefore detrimental to user independence. In recent years, research has explored the use of Electromyography (EMG) signals from alternative muscles to control a powered wheelchair, consequently increasing the quality of life of these users. The Auricular Muscles (AM) may be suitable, as they are controlled separately from the facial nerve and are vestigial in humans, making them advantageous for powered wheelchair control for users with tetraplegia. Additionally, they are located around the ear, adding a level of cosmesis when designing wearable sensors and prosthesis. This paper extracts and implements two control strategies from current literature and, for the first time, compares them directly, demonstrating viable implementation approaches for an online EMG-based powered-wheelchair control system. A Support Vector Machine (SVM) was developed and various window lengths were compared, with the most accuracy and real-time effectiveness found at 300ms. A study with three participants demonstrates the feasibility of these methods of control as well as experimental results to guide the potential AM use.

BackgroundDespite recommendations in clinical guidelines, clinical experience indicates that engagement with splints and orthotics varies amongst people after stroke. ObjectivesThe aim of the study was to understand the factors that influence engagement with splints and orthotics in people after stroke. MethodsPeople after stroke who had been wearing a splint or orthotic (also known as devices) for at least 2 months under the care of one Community Neurosciences Team in the UKs National Health Service were included. Semi structured interviews based on the constructs of Banduras Social Cognitive Theory (SCT) were used to gather participants views, and a framework analysis applying the constructs of SCT was completed using NVIVO software. ResultsFour key themes were identified: 1. Self-Regulation; difficulties applying the device and aesthetic acceptability. 2. Self-Efficacy; increased confidence when wearing the device and reduced motivation to wear the device. 3. Outcomes Expectation; reduced falls risk, improved gait, improved balance, maintaining range of movement, and negative effects such as discomfort, pain, itching. 4. Social Support; support needed to apply the device and the burden on family members/carers to apply the device correctly. ConclusionsThe findings of this study highlight key factors that influence engagement with orthotics and splints. These include difficulty applying the device after stroke, device aesthetics, comfort, and the importance of continued support from carers. Manufacturers should consider how people after stroke can independently don and doff devices. Education of carers and family members also appears key to support their engagement.

PurposeThe prognosis of post-stroke ataxia remains controversial. It is unclear whether the proportional recovery rule (PRR) established for hemiparesis applies to ataxia, given that cerebellar plasticity suggests trajectories may not depend solely on initial severity. This study was conducted to quantitatively decompose longitudinal ataxia recovery trajectories into proportional recovery coefficient (r) and time constant ({tau}) using a Bayesian nonlinear mixed-effects model, and elucidate their independent determinants and associations with functional walking independence. MethodsWe analyzed longitudinal SARA scores of 80 subacute patients with stroke to estimate individual initial severity (), r, and {tau}. Recovery patterns were clustered based on these parameters. We analyzed the attainment of independent walking using the Kaplan-Meier method and identified predictors via hierarchical multiple regression analysis. ResultsThree distinct clusters were identified. The moderate group (younger, preserved attention) achieved rapid improvement and early walking independence. In contrast, the severe group showed a significantly prolonged time constant ({tau}) but maintained a high proportional recovery coefficient (r), ultimately achieving walking independence in over 90% of cases. Regression analysis revealed a dissociation: biological age constrained the recovery ceiling (r), while attentional function independently regulated recovery speed ({tau}). ConclusionsRecovery from post-stroke ataxia bifurcates into rapid neurological restoration and a delayed process driven by compensatory learning. Especially in severe cases, long-term learning using attentional resources is crucial. These findings challenge prognosis prediction based solely on initial severity, supporting stratified rehabilitation strategies tailored to individual recovery ceilings and learning speeds.

Brain-Computer interfaces (BCIs) offer a link between neural signals and external computation, enabling control of devices for the purposes of restoring function to motor-affected individuals and enhancing capabilities of a wider set of populations. Electroencephalography (EEG) offers a high temporal resolution for dynamic and potential real-time feedback for non-invasive systems. However, its practical efficacy remains limited due to low spatial resolution and poor signal-to-noise ratio, leading to insufficient decoding accuracy and unintuitive control paradigms that hinder reliable user interaction. In this study, we present a framework for an online EEG foundation model by creating a custom foundation model through spectrogram reconstruction of compact temporal windows and online constraints during pretraining. We evaluate the performance of the model in a challenging control paradigm of single-arm, directional motor imagery with dynamic movements for guided and free movement cursor control tasks. Our foundation model approach achieved a final average accuracy of 51.3% during a goal-oriented guided control task. This represents a 15.8% increase over a conventional deep learning framework and a 26.3% increase above chance level, evaluated in a cohort of 11 human participants. During the free movement task, the foundation model invoked a higher rate of completion and lower completion times. Furthermore, the custom EEG foundation model demonstrated superior adaptability from same-session finetuning and indicated an enhanced capability to assist subject learning. These findings highlight the potential of EEG foundation models to support more robust and intuitive non-invasive BCI systems, providing a promising modelling framework for future BCI development.

Brain-computer interfaces (BCIs) translate brain signals into commands for external devices, with motor imagery (MI) BCIs decoding imagined movements to aid neurorehabilitation. Although high-channel EEG offers rich data, such systems are bulky and impractical for everyday use. This study assesses whether a low-channel, consumer-grade headset (Muse) can match a clinical-grade system (OpenBCI) in classifying lower limb MI and motor execution (ME). Six healthy volunteers performed left and right knees and ankles MI and ME tasks while EEG was recorded concurrently from both devices. Signals were band-pass filtered (8-30 Hz), segmented into overlapping one second windows, and features were extracted across time, frequency, and time-frequency domains. Feature dimensionality was reduced via mutual information-based minimum redundancy maximum relevance and principal component analysis. Five classifiers (support vector machine, linear discriminant analysis, k nearest neighbors, random forest, and AdaBoost) were applied to nine binary discrimination scenarios and evaluated with 10-fold cross-validation via 100 Monte Carlo iterations. Frequency domain features, particularly those derived from Welchs power spectral density, were most frequently selected. Mutual information analysis indicated that C3 and C4 electrodes were most informative for OpenBCI, while in Muse, the channels contributed more evenly, except in laterality classification scenarios, where TP9 played a key role. OpenBCI outperformed Muse in classifier-based accuracy with superiority ranging from 0.4% to 4.8%, while task-based differences were more variable, ranging from -0.3% to 8.7%. Despite its lower spatial resolution, the Muse system achieved competitive performance, especially in motor vs. rest tasks, and shows promise as an affordable, user-friendly alternative for home-based neurorehabilitation BCIs.

Centre of mass (COM) is a key measurement used to assess balance and mobility. Marker-based motion capture systems have traditionally been used to measure COM, but they are time-consuming and prone to marker error. Markerless motion capture systems offer a potential alternative, reducing setup time while maintaining accuracy. The ease of collecting markerless data may be particularly beneficial when study participants have limited mobility, such as those with stroke. This study aimed to determine the differences in COM measurements between marker-based and markerless motion capture systems during balance and mobility tasks in individuals with sub-acute stroke. Seventeen participants completed the following tasks: walking, quiet standing, sit-to-stand, rise on toes, and backward reactive stepping. COM data were analyzed using two markerless models, a default with 17 segments and a fit model with 11 segments to match the marker-based model to be compared as the reference. The results showed high correlations (R2 = 0.75 to 0.999) and low root-mean-square differences (< 2 cm) in the anterior-posterior and medial-lateral directions. Larger differences (> 4 cm) were observed in the superior-inferior direction, particularly with the default model. These findings suggest that markerless motion capture can be used to measure COM in people with stroke, and that model selection plays an important role in COM estimates.

The kinematics of rhythmic, speed-modulated finger and grasp-like movements were analyzed using a reduced biomechanical model of the hand and a marker-based optical motion-capture system. Twenty-one healthy participants performed eight hand motor tasks involving metacarpophalangeal (MCP) joint flexion-extension (F-E) and carpometacarpal (CMC) thumb opposition-reposition (O-R) at two movement frequencies (0.50 and 0.75 Hz). Kinematic analysis quantified the range of movement (RoM), mean speed, and normalized total harmonic distortion (TDHN). Statistical analysis identified task type as the primary factor modulating all three metrics across digits, with large effect sizes [Formula]. Movement frequency significantly influenced mean speed [Formula] and moderately affected TDHN [Formula], while thumb RoM remained statistically unchanged across frequencies (p = 0.063). Participants consistently reproduced the intended sinusoidal trajectories, as indicated by low TDHN values (below 19%). The findings support the analysis of coordinated hand movements across various tasks under controlled time conditions. They also demonstrate that the simplified biomechanical model accurately captured both individual and co-ordinated finger movements. This provides a valuable reference for studies on motor control and for applications in rehabilitation and assistive technology.

Background and PurposeWalking speed is the dominant clinical metric used to classify post-stroke hemiparetic gait severity. However, speed does not describe how mechanical energy is generated and redistributed. We tested whether whole-body center-of-mass (COM) work patterns provide a biomechanically grounded supplement to speed-based severity classification. MethodsLimb-specific COM power and work were computed from ground reaction forces using the individual-limbs method across five walking speeds (0.2-0.7 m/s). We quantified net COM work index of asymmetry (IA_Wnet), positive COM work asymmetry (IA_Wpos), and the Propulsion-Support Ratio (PSR = impFy/impFz). Piecewise and quadratic regressions were used to assess speed-dependent trends. ResultsIA_Wnet remained elevated across speeds and showed no significant high-speed association. IA_Wpos demonstrated a significant quadratic relationship with speed (p=0.023, R{superscript 2}=0.23), decreasing near 0.5 m/s before rising again. Paretic limb PSR remained constrained and exhibited a quadratic association (p=0.012, R{superscript 2}=0.14), while unaffected limb PSR declined significantly at higher speeds (p=0.019, R{superscript 2}=0.38). Below 0.5 m/s, COM power profiles collapsed to a two-phase pattern without paretic limb push-off; at [&ge;]0.5 m/s, a four-phase structure emerged. ConclusionIncreasing walking speed did not normalize interlimb mechanical imbalance. COM work organization revealed a biomechanical transition near 0.5 m/s and distinguished compensation from recovery-based restoration. Supplementing speed with COM work and propulsion-support metrics may refine severity stratification and guide mechanism-targeted rehabilitation.

PurposeRobotic walkers are a new and novel technology with growing evidence of benefits for children living with mobility impairments. However, little is known about how using these devices at home impacts families. This study aims to explore parents perceptions of home-based robotic walking and the impacts on their family and their child living with a mobility impairment. Materials and MethodsQualitative interviews were conducted with seven parents who have a child who used a robotic walker in their home for at least six months. Thematic analysis was used to analyze all interviews. Themes were then mapped to the F-words for child development. ResultsUsing a robotic walker at home led to family bonding and created new ways for parents and siblings to interact with the child living with a mobility impairment. Many children enjoyed using the robotic walker. This, combined with being able to direct its use in their own environments, contributed to less parental stress than was associated with other rehabilitation interventions. However, some parents discussed an increase in parental stress due to certain logistical aspects, getting their child in and out and transporting the robotic walker. Finally, parents discussed that obtaining the device was a financial burden for them. ConclusionRobotic walking in the home environment impacts family relationships and parental stress. Understanding families experiences can inform decision-making by families and practitioners around the appropriateness of robotic walker use for a child living with a disability.

Background: Deep brain stimulation (DBS) is a treatment option for Parkinson disease (PD). However, the effect of DBS on the arterial pressure (AP) remains unexplored. We aimed to develop an artificial baroreflex system for treating orthostatic hypotension (OH) due to central baroreflex failure in patients with PD. To achieve this, we developed an appropriate algorithm after estimating the dynamic responses of the AP to DBS using a white noise system identification method. Methods: We randomly performed DBS while measuring the AP tonometrically in 3 trials involving 3 patients with PD treated with DBS. We calculated the frequency response of the AP to the DBS using a fast Fourier transform algorithm. Finally, the feedback correction factors were determined via numerical simulation. Results: The frequency responses of the systolic AP to random DBS were identifiable in all 3 trials, and the steady state gain was 8.24 mmHg/STM. Based on these results, the proportional correction factor was set to 0.12, and the integral correction factor was set to 0.018. The computer simulation revealed that the system could quickly and effectively attenuate a sudden AP drop induced by external disturbances such as head-up tilting. Conclusion: An artificial baroreflex system with DBS may be a novel therapeutic approach for OH caused by central baroreflex failure.