Journal of NeuroEngineering and Rehabilitation

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.

Wearable exoskeletons are a promising tool for augmenting balance and reducing fall risk. Recent work suggests that active ankle exoskeletons need to act faster than the human to improve reactive balance control. However, the magnitude of exoskeleton torque that is best for improving reactive balance remains unknown. Drawing from the optimal torque for minimizing metabolic expenditure, we hypothesized that reactive balance would improve with increased exoskeleton torque. Participants wearing bilateral ankle exoskeletons were instructed to maintain standing balance during 15cm backward support-surface perturbations. Three exoskeleton plantarflexion torque conditions were tested: NO (Off), LOW (15Nm), or HIGH (30Nm). LOW torque improved balance performance compared to NO torque (p<0.001), with a 7{+/-}3% decrease in peak center of mass (CoM) displacement. Although HIGH torque caused a 9{+/-}11% decrease in peak CoM displacement compared to NO torque (p=0.12), it was not significant due to high intersubject variability. Whereas LOW torque decreased peak CoM displacement in all (range: -0.2 to -1.6cm), HIGH torque only decreased it in some (range = 1.2 to -2.6cm). The change in CoM displacement from LOW to HIGH torque was associated with balance ability, quantified by the narrowing beam test (R2=0.29, p=0.06), while this relationship didnt meet conventional statistical significance, likely due to the small sample size, it suggests that higher levels of exoskeleton torque may hinder balance performance in individuals with better balance ability. Taken together, more exoskeleton torque is not always better for balance, highlighting a potential need to personalize exoskeleton torque for balance augmentation.

The opportunity to collect movement data from smartphones for prolonged periods has opened new perspectives in the field of clinical movement analysis. However, when monitoring peoples mobility in free-living conditions, smartphone placement can influence the validity of the extracted digital mobility outcome. This study aimed to develop and validate an automatic smartphone placement recognition classifier and to investigate potential critical factors that can influence performance. The classifier was trained on data from 15 healthy participants using inertial signals collected from smartphones placed at six body placements during free-living walking and externally validated on over 3,000 individuals from external datasets, including blind participants and patients with cardiovascular or Parkinsons disease. A decision-tree ensemble model was developed using feature subsets of increasing dimensionality, with the optimal subset comprising 50 features. Classification accuracy increased consistently when front and back pocket placements were aggregated (81.1%) and further improved when coat pocket was also included in the pocket class (88.5%), underscoring the challenge of distinguishing between fine-grained pocket placements. The best-recognized placements across the external datasets were lower back (precision: 100%, recall: 72.5%), hand (precision: 94.2%, recall: 94.5%), and the aggregated pocket class (precision: 86.7%, recall: 90.2%). Recognition accuracy changed across cohorts (0.73 - 0.85), activities (0.63 - 0.94) and speed (0.79 - 0.87), however it stayed consistent across various technological and environmental factors. Overall, this study demonstrates the feasibility of robust placement recognition in walking and underscores the importance of accounting for key influencing factors when designing frameworks intended for deployment in heterogeneous real-world or clinical contexts. HighlightsO_LIMachine learning accurately identifies smartphone placement during real-world gait C_LIO_LISix on-body placements recognized, including pockets, hand, bag, and lower-back C_LIO_LIFree-living data used for training, ensuring robust performance across conditions C_LIO_LIFeature selection and hyperparameter tuning optimize classification accuracy C_LIO_LIExternal validation confirms generalizability across >3,000 healthy and diseased adults C_LI

Purpose: To identify, through iterative user-centered design, the auditory biofeedback requirements and sound preferences supporting gait training in children with cerebral palsy (CP), and to determine which feedback variables, sound mappings, and sound types yield clinically viable and movement-interpretable paradigms. Methods: The iterative process spanned two prototype phases. Prototype A comprised seven paradigms demonstrated to two experienced physiotherapists (Workshop 1A). Two of these were subsequently discarded owing to poor sound-movement interpretability and two were modified. Six paradigms were added to Prototype B, demonstrated to four children, five parents, and one therapist (Workshop 1B) and two therapists (Workshop 2B). Data were analyzed using systematic text condensation. Results: Within-child sound preferences varied with energy level and sensory state on a given day. Sound-movement interpretability tended to suffer for paradigms with greater acoustic complexity (e.g. computer-generated music). Therapists endorsed a repertoire spanning both movement quality and movement quantity targets. Participants independently proposed paradigms rewarding restrained and controlled movement, a feedback category absent from the current prototype. Conclusions: Session-level calibration is preferable to fixed sound profiles, requiring real-time interface support for paradigm adjustment. Acoustic complexity must remain subordinate to movement-sound interpretability. Paradigms targeting movement restraint are a development priority unaddressed in the literature.

Background: Accurate evaluation of fine motor abilities is a key aspect of neurological rehabilitation. However, conventional approaches like goniometry are limited by variations among raters and their difficulty in detecting active movement. On the other hand, computer vision-based software delivers non-invasive and quantitative analysis of hand movements. An innovative computer-vision-based software tool, F.A.I.R. Chance(C), was developed to track and analyze individual finger joint movements on a camera-equipped laptop and give real-time numerical feedback. However, its metrics require validation in a healthy population before the tool can be used for clinical purposes. Objective: To assess the reliability and validity of finger movement assessment by the F.A.I.R. Chance computer vision-based tool in healthy adult participants. Methods: An observational cross-sectional study was done at MGM School of Physiotherapy, comprising 30 healthy participants between 18 and 60 years of age. Finger movements like flexion, extension, abduction, and adduction were measured with a standard handheld goniometer. These same finger movements were then measured with the tool at two time points separated by a 30-minute interval to determine the test-retest reliability. The tool's measurements were compared with the goniometric measurements to determine its concurrent validity. Test retest reliability was checked by the Intra-class Correlation Coefficient ICC (2,1), while concurrent validity was tested through Pearson's correlation coefficients. Results: Metacarpophalangeal and proximal interphalangeal joint motions demonstrated moderate to good test-retest reliability (ICC: 0.716-0.953) for the F.A.I.R. Chance tool. However, distal interphalangeal joint movements had lower consistency. Good reliability (ICC: 0.754-0.908) was seen for movements of abduction and adduction in the fingers. Strong concurrent validity for extension movements of the metacarpophalangeal joints (r=0.760-0.914) and moderate concurrent validity for flexion movements of the metacarpophalangeal joints (r=0.427-0.604) was demonstrated for all fingers for the F.A.I.R. Chance tool. Concurrent validity for adduction and abduction movements demonstrated a low to fair correlation with goniometric measurements (r=0.210-0.440). This is consistent with previous research showing poor agreement between goniometry and adduction-abduction movements of the fingers. Conclusion: The F.A.I.R. Chance tool shows good reliability and acceptable concurrent validity to assess fine motor movements in the healthy adult population. This sets a basis for further clinical study of the tool in the target population with fine motor impairments. Keywords: artificial intelligence; assistive technology; computer vision; fine motor evaluation; hand function;

Background and Objectives Patients with peripheral neuropathies (PN) commonly exhibit balance impairment. In clinical practice, balance is typically assessed using the Rombergs test and ataxia scales, which rely on examiner interpretation, while objective biomarkers for quantifying balance remain lacking. Wearable sensors are valuable tools for objectively quantifying gait abnormalities in PN patients and may capture clinically meaningful changes over time. By integrating these parameters, artificial intelligence (AI) can assist in generating a digital score that enables easy, objective, and reproducible monitoring of patients postural balance. This study aims to generate and assess an AI-generated digital Rombergs test to quantify balance impairments in a cohort of PN patients. Methods PN patients were assessed in a longitudinal study using a wearable system composed of inertial sensors placed on the trunk and plantar pressure sensors integrated in insoles. Patients performed the Rombergs test under both eyes-open and eyes-closed conditions and were classified according to ataxia severity (mild, moderate, or severe) following the score obtained in item 1 of MICARS and SARA scales. Results We included 97 patients with PN (including autoimmune and hereditary polyneuropathies), and 117 healthy controls (HC). Significant differences in trunk sway and center of pressure (COP) were observed between groups, particularly with eyes closed. Using wearable sensor parameters, we developed an AI digital Rombergs test, which correlated with clinician-rated Rombergs test performance and distinguished patients with and without ataxia (AUC=0.632) and across different PN pathologies. Longitudinally, digital Rombergs test and iRODS showed concordant trajectories. Also, changes [&ge;]25% in the score were associated with clinical changes in ataxia severity measured by an increase in MICARS-SARA score (+1.42 points), whereas improvement was associated with a decrease (-0.20 points) in the scale. Discussion This study demonstrates that wearable sensors are useful to detect and quantify balance impairment. The AI-generated Rombergs test is an objective and reproducible tool for postural balance assessment, with robust discriminatory performance across clinical ataxia severity in PN. Scores longitudinal changes aligned with clinical severity, supporting its potential for monitoring disease progression and treatment response. Its strong association with balance measures reinforces its role as a quantitative biomarker of postural control in ataxia patients.

Wearable digital health technologies may complement traditional gait assessments in amyotrophic lateral sclerosis (ALS) by sensitively capturing real-world mobility changes. In this study, we validated six digital gait metrics derived from ankle-worn sensors in a natural history cohort of 182 individuals with ALS. Investigated metrics correspond to various aspects of gait, including volume, speed, intensity, similarity, variability, and fragmentation. Longitudinal analyses showed significant declines in step count, peak cadence, stride intensity, and stride similarity, with increasing stride duration variability and walking fragmentation over 52 weeks. Many participants exhibited greater relative change in the gait metrics than the self-reported ALS Functional Rating Scale-Revised (ALSFRS-RSE). Stratified analyses revealed that digital metrics captured significant functional decline even in participants with stable walking scores on the ALSFRS-RSE. These findings support the potential utility of these metrics for disease monitoring in ALS clinical care and trials.

Background: The clinical applicability of large language models (LLMs) in Parkinson's disease (PD) management remains insufficiently characterized, particularly in generative responses to clinical vignette scenarios. Objective: To evaluate the quality of clinical assessments and management plans generated by a general-purpose LLM (Gemini 1.5 Pro) and a medically specialized LLM (OpenEvidence), and to compare their performance. Methods: Models generated free-text responses to 45 open clinical queries, focused on assessment of the situation, and recommended management plan. Two movement disorders fellows rated outputs using 5-point Likert scales, dichotomized into clinically appropriate ([&ge;]4) versus inappropriate ([&le;]3). Discrepancies were adjudicated by a senior movement disorders specialist. Paired comparisons used McNemar's test; qualitative analysis examined severe errors. Results: Gemini 1.5 Pro and OpenEvidence showed high rates of clinically appropriate assessments (80.0% vs. 86.7%) but lower performance in management plans (48.9% vs. 57.8%). Cases in which both assessment and plan were clinically appropriate occurred in 46.7% and 55.6% of cases, respectively. None of these differences reached statistical significance. Severe errors were uncommon in assessments (6.7% vs. 8.9%) but more frequent in plans (26.7% in both), predominantly reflecting treatment strategy errors. Conclusions: In generative clinical reasoning tasks involving Parkinson's disease management vignettes, LLMs demonstrated reasonable performance in assessment, but consistent limitations in plan generation. The medically specialized LLM demonstrated several qualitative advantages but no statistically significant performance benefit over the general-purpose model. Therefore, these tools should be used with appropriate caution in Parkinson's disease management, particularly regarding treatment recommendations.

BACKGROUND: Dual task walking requires simultaneous management of cognitive and motor demands and is associated with changes in gait and cortical activation. However, the relationship between task related cortical recruitment and dual task related gait adjustments in healthy young adults remains unclear. This study aimed to investigate the effects of dual tasking on gait performance and cortical activation, and to examine the association between changes in cortical activity and dual-task costs. METHODS: This cross sectional study included 33 healthy young adults. Participants performed three conditions: single task walking, cognitive single task (verbal fluency), and dual task walking. Each condition was repeated 10 times using a repeated short block design with randomized trial presentation. Gait performance was assessed using an instrumented walkway, and cortical activation was measured using functional near infrared spectroscopy. Dual task costs were calculated for gait and cognitive outcomes. Statistical analysis included repeated measures analysis of variance (ANOVA) and Wilcoxon signed rank tests, with false discovery rate correction for multiple comparisons. Associations between changes in cortical activation and dual task costs were examined using correlation analyses. RESULTS: Dual task walking resulted in significant changes in gait, including reduced speed, step and stride length, and increased base of support, stance, and double support (all p < 0.05), while cognitive performance remained unchanged. Dual tasking was associated with increased cortical activation in left prefrontal and motor related regions. Greater increases in cortical activation were associated with lower dual task costs across most gait parameters, with significant correlations observed in the left dorsolateral prefrontal cortex (r {approx} 0.42 to 0.47 for speed and stride length; p < 0.05). Double support showed a distinct pattern, suggesting a specific temporal adjustment within the gait cycle. CONCLUSIONS: Dual task walking in young adults is associated with coordinated behavioral and cortical adaptations. Increased cortical recruitment is linked to reduced motor interference, suggesting that broader engagement of cortical networks may contribute to performance under cognitive motor load.

Background: Freezing of gait (FOG) in Parkinson's disease (PD) is provoked by turning, doorways and dual-task walking. We evaluated WALK, a cadence-linked vibration neuromodulation combined with motor-learning training. Methods: Single-centre, sham-controlled pilot randomised trial. Adults with PD (Hoehn and Yahr 2 to 4) and neurologist-verified FOG were randomised 1:1 to intervention (WALK; vibration enabled) or sham (WALK; vibration disabled), alongside identical supervised home-based training for 6 weeks (3 sessions per week). OFF-medication assessments were performed at S0, S8 and S16. At S8 and S16, assessments were completed without a device and then with a device (fixed order). The primary endpoint was the mZ-FOG total (0 to 36). Results: Forty participants completed follow-up assessments (intervention n=24; sham n=16) with 100% session adherence and no serious device-related adverse events. In the intervention group, mZ-FOG total improved when assessed with the device at S8 ({Delta}=8.08) and S16 ({Delta}=9.21) relative to S0, with partial retention when assessed without the device at S16 ({Delta}=5.54). Conclusions: Cadence-linked, localised vibration neuromodulation plus motor-learning training was feasible and was associated with clinically meaningful within-intervention-group reductions in FOG. Taken together, the effect sizes and task-specific pattern support progression to a multicentre, assessor-blinded trial with an active sham, powered for between-group comparisons and durability and/or adherence endpoints.

The Disconnection Symptom Discoverer (DSD) model proposes to predict long-term performance on neuropsychological tests from stroke lesion disconnection profiles. The model requires external validation to determine reproducibility and generalizability to new and different patients. Here, we investigated whether the DSD supports accurate multi-domain cognitive outcome predictions at three different timepoints post stroke, in a clinically representative independent cohort. In this study, the DSD was used to predict visuospatial attention, verbal memory, and language scores in an independent cohort of 74 stroke survivors (mean age = 69.2, 39% female) with 3 repeated cognitive assessments. DSD-predicted scores were compared to observed neuropsychological scores collected at <2 weeks, six months, and > 2 years post-stroke. DSD-predicted language outcomes were significantly correlated with observed behaviour at the <2 weeks timepoint, but no other significant correlations between DSD-predicted scores were identified. Importantly, DSD-predicted verbal memory and visuospatial domain scores were not significantly correlated with observed behaviour at any of the considered timepoints (minimum p-value = 0.33). Across all tests and timepoints, DSD-predicted scores had an average Mean Absolute Error (MAE) of 0.21 (SD = 0.13, range = 0.04-0.43), with the highest errors occurring between predicted and observed memory scores. Larger stroke lesions were associated with higher MAE, indicating that the DSD performance was modulated by stroke severity. Overall, these results indicate that the DSD did not yield informative predictions of long-term cognitive outcomes in this external dataset. This finding provides an important illustration of potential overfitting issues within cognitive outcome prediction models, highlighting the need for caution when aiming to predict long-term post-stroke cognitive outcomes and further external validation of proposed models.

Background and Objectives: Cervical spondylotic myelopathy (CSM) is a leading cause of neurological disability in older adults. However, validated, scalable tools to quantify disease severity and changes over time are lacking. Recent advances in smartphone technology have opened new avenues for longitudinal, objective, and remote monitoring of neurological conditions. We performed a preliminary evaluation of the reliability and validity of SynapTrack, a smartphone-based digital platform for objective remote CSM assessments. Methods: In this single-center prospective cohort study, 265 participants (151 with CSM, 114 healthy controls) completed in-person SynapTrack assessments related to tapping, pinching, and vibratory detection, along with reference laboratory measures of dexterity (Box and Block Test, 9-Hole Peg Test) and vibratory sensation (tuning fork). A subset completed repeated home-based testing to assess test-retest reliability. We evaluated convergent validity, construct validity against the modified Japanese Orthopedic Association (mJOA) score, known-groups validity, and test-retest reliability (intraclass correlation coefficient, ICC). Results: Smartphone-derived metrics demonstrated good-to-excellent test-retest reliability, with the strongest stability for vibratory detection threshold (ICC = 0.92), overall and non-dominant tapping speed (ICC = 0.90 each), and pinching successful targets (ICC = 0.90). Convergent validity was supported by moderate-to-strong correlations between digital metrics and reference laboratory dexterity tests ({rho} up to 0.60 for tapping speed; up to -0.65 for the vibratory threshold). Construct validity against the mJOA was strongest for the vibratory threshold ({rho} = -0.53 to -0.54) and Level 2 non-dominant pinching errors ({rho} = -0.45). Selected metrics distinguished CSM patients from controls with good discrimination, including non-dominant tapping speed (AUROC = 0.76, 95% CI 0.68-0.85), Level 2 dominant pinching successful targets (AUROC = 0.78, 95% CI 0.62-0.94), and the non-dominant vibratory threshold (AUROC = 0.77, 95% CI 0.64-0.90). Conclusions and Relevance: A smartphone-based battery of upper-extremity sensorimotor tasks demonstrated preliminary reliability and validity in CSM. Furthermore, to our knowledge, the novel vibratory detection task represents the first smartphone-based sensory assessment used for CSM. Collectively, these findings position SynapTrack as a scalable platform for objective, remote neurological monitoring of CSM.

Background: Large language models (LLMs) demonstrate strong performance in controlled medical environments such as multiple choice exams, but their utility in real-world clinical workflows remains unproven. The NHS Advice & Guidance (A&G) service, where Primary Care clinicians can submit text-based queries to specialists, provides an environment for evaluating the clinical performance of LLMs as a specialist. Methods: We compared responses from MedGemma 4B-IT, an open-weight model deployed locally on hospital infrastructure, against specialist neurologist responses across 50 adult neurology A&G cases from University College London Hospital. Two neurologists and two GPs rated 80 blinded and 20 unblinded responses for outcome, safety, efficacy, and feasibility using standardised criteria; outcome was a binary correct/incorrect, while other domains were scored 1-5. Inter-rater reliability was assessed using intraclass correlation coefficients. Results: Although there were no statistically significant differences between blinded specialist neurologists and LLM responses across any domain (outcome: 84% vs 82%, p=0.67; safety: 3.98 vs 4.02, p=0.85; efficacy: 4.06 vs 3.98, p=0.61; feasibility: 4.39 vs 4.20, p=0.45), 10% of LLM responses received concerning scores ([&le;]2 average score) compared to 0% of human responses, indicating potentially clinically important tail risk. Furthermore, unblinded results showed a preference for human responses, with human ratings being preferred across all domains. Only 51% of binary outcomes had unanimous agreement and inter-rater agreement was moderate across other domains (ICC 0.50-0.52). Conclusions: In this pilot study, aggregate scores between blinded human and LLM responses were similar, and no statistically significant differences were detected in this exploratory sample. However, aggregate metrics masked clinically important edge-case failures in LLM responses. Pronounced inter-rater variability and the potential impact of LLM/human syntax on blinded rater judgements highlight the challenges in establishing robust evaluation frameworks for clinical LLM deployment

Importance: Severe stroke is a leading cause of death and disability worldwide. Survivors and their families face long-term unmet needs, including care that does not reflect patients' values, fragmented care, and high rates of psychological distress among caregivers. Objective: To describe the conceptual framework of the longitudinal transdisciplinary neuropalliative care support (LOTUS) intervention and assess its fidelity in a pilot feasibility study. Design: Pilot feasibility randomized study; fidelity was assessed using weekly checklists completed by the LOTUS nurse and qualitative analysis of weekly LOTUS team meeting transcripts. Setting: Single comprehensive stroke center in Western New York. Participants: Patients hospitalized with severe stroke and their caregivers. Dyads were randomized to usual care or intervention. Intervention: The LOTUS intervention is implemented in a stepped-care fashion using 5 strategies: Awareness, Assistance, Adjustment, Acceptance and Alignment (5As). Led by a specially trained nurse with a chaplain, social worker, psychologist, and neuropalliative care physician, the LOTUS team follows dyads from early in the hospital course through 6 months. Main Outcomes and Measures: Fidelity, the degree to which the intervention was delivered as intended, assessed via (1) utilization of 5A activities from weekly LOTUS checklists; (2) thematic analysis of weekly LOTUS team meeting transcripts. Results: Of 26 patients in the trial, 13 were randomized to intervention. The LOTUS nurse completed 108 checklists, with an average of 619 minutes of direct contact per participant over 6 months. Each component of the 5A's was utilized. Awareness and Assistance predominated early after enrollment and revolved around personhood, support, and self-efficacy. Adjustment was especially relevant during care transitions and was typically supported by the LOTUS social worker. Acceptance and Alignment were more prevalent during later meetings, with the LOTUS psychologist supporting identification and modeling of coping skills and the LOTUS physician guiding prognosis and goals-of-care conversations. The LOTUS nurse served as primary point of contact, providing continuity and a trusting relationship, while other team members functioned in a predominantly advisory role. Conclusions: The LOTUS intervention was delivered with fidelity to the 5A-framework, supporting a future randomized clinical trial to evaluate its efficacy in patients with severe stroke and their caregivers.

BACKGROUND: Aging and Parkinsons disease (PD) reduce gait automaticity and increase cognitive demand during walking. Although dual task (DT) paradigms investigate cognitive motor interference, evidence remains limited by heterogeneous tasks, predominant focus on prefrontal cortex (PFC) activity, and variability in functional near infrared spectroscopy (fNIRS) methods. This study investigates whether longitudinal changes in cortical activation during DT walking differ among young adults, older adults, and individuals with PD, and how these changes relate to DT costs over 5 years. METHODS: This longitudinal observational study follows STROBE and fNIRS guidelines and will be conducted in a controlled laboratory (Rede Amparo, CEPID NeuroMat, University of Sao Paulo). Participants will be evaluated annually under three randomized conditions: motor single-task walking, cognitive single task phonemic verbal fluency and DT walking with phonemic verbal fluency, each repeated 10 times. The primary outcome measure will be longitudinal changes in cortical activation during DT walking, quantified by oxygenated hemoglobin (HbO) signals measured with fNIRS in prefrontal and premotor cortical regions. The main predictors of interest will be motor and cognitive DT costs. Covariates will include age, sex, education, cognition, balance, mood, and disease severity in the PD group. Spatiotemporal gait parameters, including gait speed, step length, stride length, step time, base of support, double support, stance phase, and variability, will be recorded using the GAITRite system, and DT costs will be calculated for selected parameters. Cortical activation will be assessed using a 66 channel wearable fNIRS system with short separation channels. DISCUSSION: By combining randomized task blocks, separate motor and cognitive conditions, broader cortical coverage, and concurrent neural and gait assessment across three groups annually, this protocol is expected to provide a comprehensive characterization of cognitive motor interference during walking and its evolution, supporting interpretation of cortical and behavioral responses. The study may help distinguish age related adaptations from PD specific alterations and clarify whether increased cortical recruitment during DT gait reflects compensation, reduced neural efficiency, or ceiling effects, refining understanding of gait automaticity decline and informing rehabilitation and non invasive brain stimulation approaches.

BackgroundEar-EEG-based brain-computer interfaces (BCIs) provide improved wearability and comfort compared to traditional scalp-EEG systems. However, their performance is constrained by low signal-to-noise ratios (SNRs) and high rates of BCI illiteracy under conventional luminance-modulated steady-state visual evoked potential (SSVEP) paradigms. MethodsThis study introduces a text-sequence stimulation paradigm to address these limitations by leveraging ventral visual pathway responses that are more accessible to electrodes near the ear. Using offline frequency-sweeping experiments across 4-8 Hz, we identified optimal stimulus parameters (4.6-6.8 Hz with 0.25{pi} phase shifts) and integrated them into a 12-target BCI system. We further conducted online experiments to compare the response characteristics and real-time spelling performance between the proposed text-sequence paradigm and conventional luminance stimulation. ResultsComparative experiments with 14 participants demonstrate that text sequence stimuli achieve an average information transfer rate (ITR) of 44.59 {+/-} 10.50 bits/min, outperforming luminance modulation by 76.18% in ITR. Notably, text sequence stimulation effectively mitigated BCI illiteracy, with all participants achieving near or above 70% accuracy (mean: 86.37 {+/-} 9.61%). This represents a significant improvement over luminance modulation, where 50% of users fell below 70% accuracy. ConclusionsBy reducing the flicker area by 14% and mimicking the natural luminance variations that occur during reading, the proposed method enhanced visual comfort. The online results further validate text-sequence stimulation as a high-performance and user-friendly paradigm for ear-EEG BCIs, supporting their practicality for assistive applications.

Biomechanical simulations of complex hand motions remain scarce, due to challenges that span computation and data acquisition. Using a computer vision-based motion capture approach, a 23-degree of freedom musculoskeletal model, and direct collocation optimization, we performed muscle-driven simulations to track hand kinematics from 7 participants performing American Sign Language gestures. While proximal joints were tracked accurately, interphalangeal joint tracking was significantly worse, with a consistent flexion bias. Modifications to finger extensor muscle paths that incorporated the dual-inserting nature of the extensors improved accuracy, suggesting better representation of extensor force distribution across distal joints may be necessary for accurate hand simulations.

Introduction: High-intensity locomotor training (HIT) is recommended for improving walking capacity, but treatment responses are variable. Understanding the brain changes underlying responsiveness to training could provide insight into this variability. Emerging evidence suggests upregulation of the contralesional cortico-reticulospinal tract (CRST) may contribute to walking function after stroke. However, it is unclear whether CRST upregulation is supportive or maladaptive, and no studies have examined CRST changes after HIT. This study investigated how CRST and corticospinal tract (CST) strength and laterality reorganize, and their relationship with walking capacity after locomotor HIT. Methods: Ten participants with chronic stroke completed a 4-week no-intervention control phase then 4-weeks of HIT. Diffusion MRI and 6-minute walk distance were obtained at weeks 0, 4, and 8. Analysis tested changes in ipsilesional and contralesional CRST and CST strength and laterality. Associations between changes in tract laterality and walking capacity were examined. Results: During the treatment phase (vs. the control phase), there were significantly greater increases in contralesional CRST strength (1.02 SD [95% CI: 0.25, 1.79]), contralesional CRST laterality (4.44 [2.15, 6.72]), and 6-minute walk distance (33 meters [17, 50]). Walking capacity improvements were associated with changes in CRST laterality (r = 0.77, p = 0.01), but not CST laterality (r = -0.01, p = 0.98). Discussion: Following HIT, increases in contralesional CRST strength and laterality were observed. CRST laterality changes were strongly associated with walking improvements, suggesting a possible supportive role of contralesional CRST in mediating training-related improvements in walking function after stroke.

(1) Background: Brain cancer is the ninth leading cause of cancer death in the US, with approximately 76,000 newly diagnosed cases annually. Studies show that at time of diagnosis, up to six-months post-treatment, 50%-80% of brain cancer survivors (BCS) report cognitive dysfunction. Mild cognitive impairment (MCI) has gained increasing attention as a persistent disability experienced by up to 75% of all BCS, which affects memory, concentration, executive function, etc. Studies show cognitive training with computerized gaming as improving cognitive function for patients with stroke, dementia, and Parkinsons. It is of significant clinical interest to develop innovative interventions that reduce MCI. Aim: To improve cognitive performance of BCS suffering with MCI by evaluating the feasibility, acceptability and effect of a Virtual Reality Cognitive Rehabilitation Training (VR-CRT) platform during four weeks of cognitive training. (2) Methods: We employed a quasi-experimental pretest/posttest non-randomized/non-blinded single-arm design for 4 weeks, with an experimental group (n=6, after attrition) using VR-CRT. Participants were selected based on convenience sampling using the electronic medical record to identify qualified patients, guided by inclusion/exclusion criteria. Feasibility was defined by retention as >80%, with usability testing using the System Usability Scale (SUS) and NASA-TLX surveys. The Hopkins Verbal Learning Test (HVLT), Controlled Oral Word Association (COWA) test, and Trail Making A-B (TM-A/B) test were used to measure cognitive performance, comparing baseline to post week-four. (3) Results: The feasibility criteria of >80% was met. All SUS and NASA scores were in the higher index, suggesting a high degree of usability, with low workload demand. For effect, the COWA findings showed a significant improvement (41.38%), with a paired sample T-Test confirming that the participants COWA scores improved significantly from pre- to post-intervention (p = 0.03), indicating enhanced verbal fluency and executive functioning after intervention. HVLT (combined) showed improvements of 18.75% for Form A and 11.32% for Form B, which also showed a significant improvement (p = .04) in the retention discrimination index from pre- to post-test. The TM-A/B test showed an improvement (25.97%), suggesting that the participants spent less time completing both parts A and B, but was not statistically significant. (4) Conclusion: This study fulfilled our aim to demonstrate modest to significant cognitive improvement using VR-CRT with brain cancer patients with MCI. Despite the small sample size, we believe the use of virtual reality will lead to important advances for patients with MCI, particularly the frontal lobe brain region, expressed in executive function.

Background Continuous health monitoring enables early detection of diseases and improves therapeutic outcomes. Non-intrusive biosignal sensors, such as capacitive ECG (cECG), offer a practical solution for daily monitoring in private environments, such as smart homes and vehicles. However, artifacts reduce signal quality and compromise reliability. Methods Following a registered report protocol (Warnecke JM et al. Plos One. 2021; 16(7):e0254780), we record data of 44 subjects and develop an artifact index for cECG. We use three signal quality indices (SQIs): the correlation of QRS complexes (corSQI), the R-peak detection consistency (bSQI) and the absolute amplitude ratio (aSQI). Our index classifies overlapping 10s segments with a step-width of 2s into clean or artifact segments. We label a 2s interval as artifacts if all five overlapping segments indicate artifacts. We record cECGs using an armchair with integrated electrodes in a single-arm study involving 44 subjects performing two activities -- reading and watching television (TV); for 11 minutes each. We record a time-synchronized reference ECG with skin electrodes on the chest. To evaluate the artifact index, we compare it with manually generated ground truth. Moreover, we evaluate the clothing materials cotton, linen, jeans, and polyester in 5 subjects. Results Watching TV results in longer, continuously clean signal durations than reading. On average, 88.3% of the signal has a minimum continuous clean duration of 10s, versus 79.8% during reading. All clothing configurations achieve a clean signal duration exceeding 10s. Among the SQI metrics, bSQI performs best, achieving an accuracy of 90.7% and an F1 score of 79.9%. Combining the three SQI metrics in a voting approach improves accuracy to 92.0% and F1 score to 82.1%. Discussion Our artifact index automatically distinguishes clean from artifact cECG segments, promoting health monitoring in unsupervised real-world settings, earlier disease detection, and preventive health management. A limitation is the investigation of only two scenarios (reading and watching TV).