Journal of NeuroEngineering and Rehabilitation

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.

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.

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

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.

BackgroundLower-limb EEG is a rehabilitation-facing control signal for stroke neurorehabilitation and future non-invasive brain-spine interfaces, but a public external benchmark that jointly audits source construction, minimal adaptation burden, and confound sensitivity is lacking. We therefore tested whether lower-limb effort-versus-rest decoders trained on healthy public EEG transport to a stroke target domain. MethodsWe conducted a retrospective public-data external benchmark using three public EEG datasets harmonised to a common lower-limb effort-versus-rest target. Classical and deep models were compared under zero-shot transport, 10-shot temperature calibration, and 10-shot fine-tuning. For few-shot analyses, each target participant contributed a trial-disjoint subject-internal support set of 10 labelled trials per class and a held-out remainder test set. Prespecified analyses audited source construction, support-resampling sensitivity, and montage controls. Uncertainty was summarised with participant-level bootstrap confidence intervals. ResultsWithin this benchmark, healthy-to-stroke zero-shot transport was weak. The best zero-shot result was classical rather than deep, with CSP+LDA reaching area under the receiver operating characteristic curve (AUROC) 0.603, whereas EEGNet remained near chance (AUROC 0.527). Ten-shot calibration improved operating behaviour more than discrimination: for CSP+LDA, expected calibration error fell from 0.267 to 0.035 and specificity increased from 0.180 to 0.485, whereas AUROC remained essentially unchanged (0.603 to 0.604). Ten-shot fine-tuning produced only modest gains; the best overall AUROC was 0.605 for pooled dataset-balanced CSP+LDA, numerically tied with pooled raw CSP+LDA (0.605). MILimbEEG-only source training was consistently weak, exploratory deep domain-generalisation variants did not rescue transport, and frontal and temporal montage controls remained relatively competitive. ConclusionsWithin this public benchmark, source construction and minimal adaptation burden mattered more than model novelty, and retrospective montage controls limited motor-specific interpretation. The results support harmonised prospective validation of lower-limb EEG transport over further retrospective model iteration.

Objective: To develop the first inventory to measure psychosocial concerns about use of the non-preferred hand, toward the long-term goal of identifying the casual factors of left-right hand choices ("hand usage"). Design: Cross-sectional Setting: Online question battery Participants: 181 healthy adults Interventions; Not applicable Main Outcome Measures: Self-reported concerns about emotional and physical consequences of using the non-preferred hand. Results: Emotional and physical consequences reflected internally consistent categories (Cronbach's > 0.9) that were moderately correlated with each other ({rho} = 0.783 p = 0.002). Concerns were activity-dependent in each category ({rho} < 1x10-100). Reliability analysis and principal components analysis were used to reduce the battery to the 51-item Changed Hand Usage Concerns inventory, which encompasses everyday tasks and concerns about physical and emotional consequences of using the non-preferred hand in those tasks. Conclusions: Concerns about emotional vs. physical consequences of non-preferred hand use reflect coherent and internally consistent categories The Changed Hand Usage Concerns inventory allows assessment of psychosocial concerns about usage of the non-preferred hand for future attempts to manipulate hand usage via rehabilitation in patients with unilateral or asymmetric impairment.

BackgroundRespiration is a key central nervous system rhythm that modulates sensorimotor function in healthy individuals, but the neurophysiological mechanisms of volitional breathing-mediated sensorimotor modulation and its preservation in stroke patients remain unclear. This study aimed to characterize the effects of volitional fast inspiration on sensorimotor pathway excitability in healthy and stroke populations, and provide a mechanistic basis for respiratory-integrated post-stroke rehabilitation. MethodsA multimodal case-control neurophysiology study was conducted in 52 healthy volunteers (26 {+/-} 3 years, 30 males) and 44 first-ever subacute stroke patients (66 {+/-} 10 years, 30 males). Three complementary experiments assessed transcranial magnetic stimulation-induced motor-evoked potentials (MEPs), peripheral nerve stimulation-induced somatosensory-evoked potentials (SEPs), and functional electrical stimulation -evoked muscle force under three breathing conditions: volitional fast inspiration (IN), fast expiration (EX), and spontaneous breathing (CON). Two-way and one-way repeated measures ANOVA with Bonferroni post hoc tests were used for statistical analysis. ResultsVolitional fast inspiration significantly enhanced sensorimotor pathway excitability and muscle force generation in both groups. Volitional fast inspiration increased MEP amplitudes relative to spontaneous breathing and fast expiration (p {inverted exclamation} 0.05), with further amplification during active muscle contraction (p {inverted exclamation} 0.05). It also elevated SEP amplitudes in healthy parietal/frontal cortical regions and the stroke parietal cortex (p {inverted exclamation} 0.05). Synchronizing volitional fast inspiration with voluntary finger contraction increased muscle force evoked by functional electrical stimulation by 16-18% relative to spontaneous breathing (p {inverted exclamation} 0.05), with non-significant force gains at rest. ConclusionsVolitional fast inspiration bidirectionally enhances corticospinal transmission, somatosensory integration, and functional force generation in both healthy individuals and stroke patients, with preserved respiratory modulation in stroke-damaged neuropathways. By demonstrating preserved respiratory modulation in stroke-damaged neuropathways, our results provide mechanistic support for integrating controlled breathing into low-cost, non-invasive post-stroke rehabilitation paradigms.

Walking impairments in Parkinsons disease (PD), including reduced speed, cadence, and stride length, and increased variability, impair mobility and raise fall risk. Conventional treatments may fail to address these deficits, underscoring the need for complementary non-invasive alternatives. This study examined whether combining rhythmic auditory cueing with transcranial direct current stimulation (tDCS) over the supplementary motor area (SMA), a critical region for internally-generated movement, would enhance gait performance in PD. Thirty-three participants with PD and thirty-two healthy controls completed two sessions (anodal vs. sham tDCS) with gait assessed during stimulation, immediately after stimulation, and 15 minutes after stimulation under two auditory conditions: walking in silence and walking to music paced 10% faster than baseline cadence. Spatiotemporal, variability, and stability gait parameters were analyzed using linear mixed-effects models. Rhythmic auditory cueing significantly increased cadence and speed during, immediately after, and especially 15 minutes after stimulation, suggesting sustained effects of rhythmic entrainment. Anodal tDCS produced faster cadence, as well as lower stride time variability and stride width, particularly in individuals with PD. Although both music and anodal tDCS affected gait, no interaction was observed, indicating independent effects. Individuals with PD had greater gait variability overall, and adjusted temporal gait parameters less to music than healthy controls did. Anodal stimulation reduced walking variability in PD, reducing the group differences observed under sham conditions. These findings suggest that rhythmic cueing and SMA stimulation target complementary mechanisms, highlighting the promise of combined tDCS-music interventions for gait rehabilitation in PD.

Objectives: Among individuals attending stroke rehabilitation, we aimed to determine the proportion who participated in cardiorespiratory exercise, identify patient characteristics predicting participation, and describe exercise characteristics. Design, setting, and participants: This was an observational cohort study involving all patients admitted to four stroke rehabilitation centres in Ontario, Canada, during March or October 2019, or over 12 months starting in 2021. Main measures: Patient characteristics extracted during chart review included age, sex, marital status, employment status, date of stroke, time post-stroke at admission, length of stay for rehabilitation, past medical history that could affect exercise participation, Functional Independence Measure, Functional Ambulation Category, mobility aid use, Chedoke-McMaster Stroke Assessment, Montreal Cognitive Assessment, National Institutes of Health Stroke Scale, and details describing cardiorespiratory exercise completed. Results: 40.1% of stroke patients participated in cardiorespiratory exercise, with 26.4% having it included in their treatment plan. Diagnosed cardiac disease (OR=0.74), poor left ventricular function (OR=0.09), history of mental health conditions (OR=0.69), lower functional ambulation ability (OR=0.74), and wheelchair use at rehabilitation admission (OR=0.46) were associated with lower odds of participating in cardiorespiratory exercise after stroke (p-values<0.05). Use of a walker or rollator at rehabilitation admission (OR=3.22), having a cardiorespiratory exercise goal (OR=2.13), and longer lengths of stay (OR=1.01) were associated with higher odds of participating in cardiorespiratory exercise after stroke (p-values<0.05). Only 1.5% of patients (N=9/601) who participated in cardiorespiratory exercise completed it with recommended intensity and duration. Conclusion: Improving participation in cardiorespiratory exercise during stroke rehabilitation may require addressing cardiovascular, mental health, and mobility-related barriers.

Dystonia treatment evaluation in cerebral palsy (CP) is limited by the lack of clinician-assessed scales linking dystonia severity to functional impact. We asked 7 pediatric movement disorder specialists to review videos of 27 children with CP while performing an upper extremity task and while walking. Experts rated arm and leg dystonia severity using the Global Dystonia Severity Rating Scale (GDRS) and task-specific functional impact on a five-point scale adapted from the Dyskinetic Cerebral Palsy Functional Impact Scale. Arm GDRS scores correlated with functional impact on the upper extremity task (linear regression R^2=0.48, p=0.0005). Leg GDRS scores correlated with gait impact (R^2=0.43, p=0.001). A four-point increase in total GDRS corresponded to a one-point worsening in combined functional impact. By demonstrating how expert-rated limb dystonia severity correlates with task-specific functional impact in children with CP, these results could help clinically identify functionally-meaningful differences in dystonia severity.

Walking speed is widely used to assess gait recovery following stroke, yet it provides limited insight into how walking performance is mechanically organized. This study examined how center of mass (COM) work organization and propulsion-support coupling vary across walking speeds in individuals with post stroke hemiparesis to distinguish recovery of gait organization from recovery of limb level mechanical function. Eleven individuals with post stroke hemiparesis performed treadmill walking across speeds ranging from 0.2 to 0.7 m/s while ground reaction forces were recorded. Limb specific COM power and work were computed using an individual limbs framework, and interlimb asymmetry in net and positive work, along with the propulsion-support ratio (PSR), were quantified. A qualitative transition in gait organization was observed: at lower walking speeds, COM power exhibited a simplified two phase pattern, whereas at higher walking speeds (approximately >=0.5 m/s), a structured four phase COM power pattern emerged, including identifiable push off and preload phases. Despite this recovery of gait organization, interlimb work asymmetry remained elevated and paretic PSR remained reduced across all speeds, indicating persistent limb level mechanical deficits. These findings demonstrate that increases in walking speed and the emergence of typical COM power structure reflect recovery of gait organization rather than restoration of underlying limb level mechanical capacity. Consequently, walking speed alone is insufficient to characterize gait recovery after stroke, and biomechanically informed measures of COM work organization and propulsion-support coupling provide complementary insight by distinguishing organizational recovery from limb-level mechanical recovery.

Stroke is the third leading cause of death and disability combined worldwide and often results in hemiparesis. Functional magnetic resonance imaging (fMRI) is a non-invasive technique used to investigate changes in brain activations during tasks aimed at restoring the lost motor function. Participants with chronic stroke and residual hemiparesis in the upper extremity were recruited for a clinical intervention that included neurofeedback training and fMRI sessions with motor-execution and motor-imagery tasks. The present study provides a baseline characterization of brain activations prior to neurofeedback training. Since lesion site and volume varied across participants, two fMRI preprocessing pipelines were applied. The first one was used for twelve participants with lesions restricted to a single hemisphere and for one participant with small secondary lesions in the contralesional hemisphere, whereas the second one was used for two participants with large bilateral lesions. These were followed by quality control measures and statistical analysis. First-level (i.e., single-participant) analysis returned the strongest and most extensive activation across participants during motor-execution tasks, with clusters identified in the ipsilesional parietal lobe, bilateral occipital lobes, and cerebellum after Family-Wise Error correction. Second-level (i.e., group-level) analysis involving participants who underwent the first fMRI preprocessing pipeline revealed a significant cluster in the cerebellum after False Discovery Rate correction. These results are consistent with previous studies involving participants with chronic stroke performing motor-tasks. Cerebellar recruitment observed consistently across participants could reflect compensatory mechanisms supporting motor control after stroke.

Optimizing assistive wearable devices is crucial for their efficacy and user adoption, yet state-of-the-art methods like Human-in-the-Loop Optimization (HILO) and biomechanical modeling face limitations. HILO is time-consuming and often restricted to optimizing control parameters, while inverse dynamics assumes invariant kinematics, which is unreliable for adaptive human-device interaction. Predictive simulation offers a powerful alternative, enabling computational exploration of design spaces. However, existing approaches often lack systematic optimization frameworks and rigorous validation against experimental data. To address this, we developed a Design Optimization Platform that integrates predictive simulations within a two-level optimization structure for personalizing assistive device design. This paper primarily validates the platforms predictive simulations against a publicly available dataset of the passive Biarticular Thigh Exosuit (BATEX), assessing its reliability. Our findings show that the model can sufficiently predict the kinematics and major muscle activations, except for the pelvis tilt and some biarticular muscles. The key finding is that successful identification of personalized optimal BATEX stiffness parameters needs acceptable prediction of metabolic cost trends, not their precise values. Our analysis further reveals that the models accuracy in predicting Vasti muscle activation in the baseline condition is a significant indicator of its success in predicting metabolic cost trends. This demonstrates that accurate prediction of performance trends is more important for effective simulation-based design optimization than perfect biomechanical accuracy, advancing targeted and efficient assistive device development.

AO_SCPLOWBSTRACTC_SCPLOWMuscle fibre type distribution influences both the metabolic and contractile properties of individual muscles. However, as humans tend to self-optimize their gait pattern to minimize cost of transport, these changes in muscle properties may influence gait biomechanics in manners that are difficult to isolate in in vivo experiments. The purpose of this study was to predict the influence of muscle fibre type distribution on the metabolic cost and biomechanics of simulated walking and running. We implemented a muscle model that could predict recruitment of slow and fast twitch muscle fibres in a framework for predictive musculoskeletal simulation. Subsequently, we employed the framework to investigate how metabolic cost of transport, stride length, stride frequency, and mechanical work performed by slow and fast twich muscle fibres were influenced by fibre type distribution across locomotion speeds from 1.0 to 4.5 m {middle dot} s-1. Our results predict that cost of transport increases as slow twitch area fraction decreases, while stride length and frequency was minimally affected by fibre type distribution at speeds resulting in walking. In contrast, fibre type distribution interacts with locomotion speed at speeds resulting in running. Specifically, we predict the existence of a threshold speed below which cost of transport decreases with an increasing proportion of slow twitch fibres, while cost of transport increases with increasing proportions of slow twitch fibres above it. The shift in fibre type distribution was accompanied by an increase in stride frequency and decrease in stride length. These shifts in spatiotemporal characteristics appear to allow the muscles to operate at speeds close to those that achieve peak mechanical efficiency. Taken together, the results of this study predict that muscle fibre type distribution may influence both the energetics and biomechanics of gait, and that this influence is dependent upon the locomotion speed.

Sensorimotor learning and tool use involve synchronizing with external dynamics. Many everyday tools possess nonlinear hidden dynamics. Here we investigate how learning to synchronize with the complex dynamics of a tool depends on the degree of predictability and reciprocal coupling between user and tool. We introduce the concept of optimal coupling to measure adaptive user-tool coordination. Groups of participants practiced tracking an auditory stimulus in three conditions: 1) the tool was non-interactive and produced a periodic stimulus, 2) non-interactive and unstable stimulus, and 3) unstable but interactive stimulus which was coupled weakly to the participants movements and thus afforded control. Learning, retention, and transfer to visual modality were assessed using unpracticed test stimuli. Directional effective coupling was quantified using transfer entropy. Results showed that learning tended to be task-specific and there was no transfer to the visual modality. Interactive unstable practice exhibited some retention and generalization. We found a convergent reorganization of coupling during practice with the interactive unstable tool: stimulus-to-human coupling started high and decreased while human-to-stimulus coupling started low and increased. This suggests that embodiment of personalized rehabilitation technologies brings optimal reciprocal coupling in which sensorimotor-tool control is consistent with the minimal intervention principle postulated for within-body control.

Markerless motion capture has emerged as a potential substitute for traditional marker-based systems, offering scalable, non-invasive acquisition of human movement. Despite increasing adoption in research and sports applications, its clinical utility for children with complex gait patterns remains an open question. To address this gap, simultaneous marker-based and markerless data were collected in 202 pediatric children (12.1 {+/-} 3.9 years). Marker-based kinematics were processed using the Shriners Children's Gait Model (SCGM), while markerless outputs were computed using Theia3D with identical Cardan sequences. Agreement between systems was evaluated using statistical parametric mapping (SPM), root-mean-square error (RMSE), and a gait pattern classification based on the plantarflexor-knee extension index. Markerless output systematically underestimated pelvic tilt, hip rotation, and knee rotation and demonstrated reduced between-subject variance in the transverse plane. SPM revealed widespread waveform differences, although most were of negligible effect, especially in the sagittal plane. Mean sagittal-plane RMSEs were < 5{degrees} for the knee and ankle and < 8{degrees} for the pelvis and hip. Coronal-plane deviations were < 7{degrees}, whereas transverse-plane errors exceeded 10{degrees}. RMSE increased significantly with body mass index and use of a walker (p < 0.001). Agreement in sagittal-plane gait classification was moderate between systems ({kappa} = 0.60; 67% overall concordance). These results indicate that markerless motion capture is suitable for analyses emphasizing sagittal deviations but remains limited for applications requiring precise axial or frontal-plane estimation. Future work should address algorithmic underestimation of transverse motion and evaluate markerless performance across increasing severity of gait deviation.

Background: Clinical documentation and information retrieval consume over half of physicians working hours, contributing to cognitive overload and burnout. While artificial intelligence offers a potential solution, concerns over hallucinations and source reliability have limited adoption at the point of care. Objective: To evaluate clinician-reported time savings, decision-making support, and satisfaction with DR. INFO, an agentic AI clinical assistant, in routine clinical practice. Methods: In this prospective, single-arm pilot study, 29 clinicians across multiple specialties in Portuguese healthcare institutions used DR. INFO v1.0 over five working days within a two-week period. Outcomes were assessed via daily Likert-scale evaluations and a final Net Promoter Score. Non-parametric methods were used throughout. Results: Clinicians reported high perceived time saving (mean 4.27/5; 95% CI: 3.97-4.57) and decision support (4.16/5; 95% CI: 3.86-4.45), with ratings stable across all study days and no evidence of attrition bias. The NPS was 81.2, with no detractors. Conclusions: Clinicians across specialties and career stages reported sustained satisfaction with DR. INFO for both time efficiency and clinical decision support. Validation in larger, controlled studies with objective outcome measures is warranted. Keywords: Medical AI assistant, LLMs in healthcare, Agentic AI, Clinical decision support, Point of care AI

Prosthetic devices balance functionality and usability to support activities of daily living (ADLs). However, many designs rely on rigid end effectors that, while anthropomorphic in form, lack biomimetic design principles. This mismatch increases cognitive and physical burden, reducing adoption rates. We developed the Human-inspired Actuator Modeling and Reconstruction (HAMR) process, a user-centered framework informed by individual morphology and functional needs, to generate customized agonist/antagonist tendon-actuated end effectors. Using HAMR, we created the Tendon Actuated Prosthetic Hand (TAPH), which integrates human-derived geometry with adaptive force distribution to promote natural object interaction. In a study with 12 participants without limb difference, TAPH was compared to a structurally similar tendon-actuated hand with generalized anthropomorphic geometry across three ADL tasks of varying complexity. TAPH significantly improved task performance and reduced physical effort, mental workload, and frustration, particularly during gross motor tasks. For fine motor tasks, performance improved under stable conditions but not during tasks requiring dynamic precision and continuous coordination. These findings highlight the functional benefits of biologically informed prosthesis design and support biomimetic principles in enhancing performance and user experience.

Background: Lumbar spinal stenosis (LSS) can cause pain and severe walking limitation. Although surgery aims to improve walking, many patients do not achieve clinically meaningful gains. Rehabilitation can improve outcomes, yet existing programmes lack robust evidence and theoretical underpinning. This study aimed to (1) co-design a theory-informed rehabilitation programme to improve walking after LSS surgery, and (2) evaluate feasibility of conducting a future trial and acceptability of the intervention. Methods: A multi-methods study included intervention co-design followed by a single-arm feasibility study. Co-design used an adapted Experience-Based Co-Design approach with patients, carers, and healthcare professionals (n=39), integrating the Behaviour Change Wheel. This resulted in STructured Rehabilitation and InDividualised Exercise and Education (STRIDE), delivered over 12-week pre- and 12-weeks post-surgery, targeting knowledge, expectations, perceived control, physical capability, and fears. Adults aged [&ge;]50 years awaiting LSS surgery were recruited to a before-after feasibility study. Feasibility outcomes included recruitment and retention. Acceptability was assessed using the Theoretical Framework of Acceptability questionnaire (0-5 (high acceptability)) and focus groups. Clinical outcomes measured at baseline, post-prehabilitation, and post-rehabilitation included 6-minute walk distance (6MWD) and mean daily step count over 7 days. Results: Fifteen of 31 eligible participants were recruited (48%; mean age 70 years), with 80% retained to study end (2 decided against surgery, 1 unable to complete final assessment). Acceptability was high (median 5/5, IQR 0). Participants valued the personalised, supportive approach and reported improved motivation and preparation for surgery, though travel was burdensome. Small pre-operative and moderate-to-large post-operative improvements were observed in 6MWD (+49.9 m and +81.6 m) and daily step count (+868 and +1405 steps/day). Conclusions: This co-designed, physiotherapy-led, behaviour-change rehabilitation programme was acceptable to participants, with encouraging recruitment, retention, and signals of improved walking following LSS surgery. The findings support progression to a future trial.

ObjectiveComputational models and visualization toolboxes for Deep Brain Stimulation (DBS) increasingly rely on pre-computed electric field libraries to estimate the Volume of Tissue Activated (VTA). However, the boundary conditions (BCs) and source models used to generate these fields vary widely across studies, and there is currently no experimental consensus regarding which parameters most accurately reflect the physical device output. The objective of this study was to experimentally validate the electric potential distribution of directional DBS leads in order to determine the optimal Finite Element Method (FEM) configuration. ApproachThe voltage distribution surrounding a Boston Scientific Vercise Gevia directional lead was mapped in a saline phantom using a custom high-precision robotic scanning system. Experimental measurements were compared against six FEM configurations that varied in source formulation (Dirichlet vs. Neumann boundary conditions) and ground definitions. For each configuration, the resulting VTA volume was computed to assess the clinical impact of modeling assumptions. ResultsThe FEM configuration implementing a Dirichlet (voltage) boundary condition on the active contact with a grounded implantable pulse generator (IPG) surface demonstrated the highest accuracy, achieving a Symmetric Mean Absolute Percent Error (SMAPE) of less than 9% across all contact levels. In contrast, conventional current-controlled simulations employing Neumann boundary conditions with disparate ground definitions substantially overestimated electric field spread. Suboptimal boundary condition selection resulted in an approximate 67% overestimation of VTA volume (137 mm3 vs. 82 mm3) relative to the experimentally validated model. SignificanceAlthough clinical DBS systems operate as current sources, standard Neumann (current density) boundary conditions do not adequately represent the equipotential behavior of the electrode-tissue interface, resulting in nearly a two-fold error in predicted VTA volume. To improve the validity of predictive clinical models, we recommend the use of Dirichlet boundary conditions derived from the device operating impedance (V = Itarget x Zmeasured) rather than conventional current density specifications.