IEEE Transactions on Neural Systems and Rehabilitation Engineering

Wearable inertial measurement units (IMUs) provide a practical and objective approach for gait assessment in clinical populations. Although several handcrafted gait features have been proposed, these features may not fully capture the multidimensional signal characteristics associated with different pathological gait patterns. This study proposes a digital biomarker called Embedding-Distance Gait Biomarker (EDGB) based on supervised contrastive representation learning of wearable IMU signals. A compact multi-input convolutional neural network is developed to encode raw acceleration, angular velocity, and their temporal derivatives into a 32-dimensional latent representation. Class-specific prototypes are computed from the training embeddings of healthy, neurological, and orthopedic participants. The proposed EDGB is then derived from the distances between each trial embedding and the learned group prototypes. The proposed architecture is evaluated on the publicly available Voisard clinical gait dataset using a subject-level split, with 20% of participants held out for testing to prevent leakage across repeated trials. On unseen test subjects, the proposed biomarker distinguished healthy from neurological, healthy from orthopedic, and neurological from orthopedic gait patterns with AUCs of 90.59%, 88.47%, and 99.50%, respectively. The biomarker also demonstrated a large group effect, with clinical category explaining 71% of its variance. Reliability analysis showed significant consistency across repeated trials, with an ICC (2,1) of 0.82, indicating that most variability reflected between-subject differences rather than within-subject trial-to-trial fluctuations.

Older adults face an increased risk of falls that may have severe consequences for their well-being. Routine, accessible clinical screening may help mitigate fall risk through early detection of balance impairments. Portable force plates offer a convenient and practical solution for balance assessment in clinical settings. A new force-plate-based balance measure, the intersection-point-height, has shown particularly promising results in its ability to distinguish between healthy and impaired balance behaviors. However, the intersection-point-height measure requires measurement of shear force during standing, which exhibits magnitudes of less than 0.2% of normal forces (body weight), taxing the dynamic range of most sensor technologies. The ability of existing force plates to measure such low-magnitude shear forces observed during quiet standing is currently unknown. This study presents a force-plate performance assessment method to evaluate shear-force measurement errors and quantify the uncertainty of the intersection-point-height measure. The method was applied to test a commonly used laboratory-grade portable force plate. While the device successfully captured sagittal-plane intersection-point-height at the lowest frequencies, low signal strength prevented precise readings in the frontal plane. Thus, the tested device only marginally met the precision required for quiet-standing analysis, underscoring the critical need for systematic performance validation of portable force plates prior to clinical use. Future efforts should focus on evaluating alternative portable force plates and exploring economical design improvements to enhance shear-force measurement precision.

PurposeEEG-based brain-machine interfaces (BMIs) may support assistive technologies for individuals with stroke-related motor impairment by translating neural activity into control commands for external devices. However, post-stroke neural reorganization and interindividual EEG variability challenge reliable decoding. This study characterized motor imagery EEG features in healthy and acute stroke participants and evaluated whether population-trained Q-learning Kernel Temporal Difference (Q-KTD) decoders could improve individual stroke decoding through transfer learning. These analyses assess the feasibility of healthy-to-stroke translation for EEG-based BMI neural decoding. Materials and MethodsPublicly available motor imagery EEG datasets from healthy participants (n = 109) and individuals with acute stroke (n = 50) were analyzed using left- and right-hand motor imagery trials. The datasets were selected because of their relatively large sample sizes and comparable motor imagery tasks. EEG characterization included baseline and motor imagery-period band power, ERD/ERS, hemispheric asymmetry, and time-frequency representations. For Q-learning Kernel Temporal Difference (Q-KTD) decoding, filtered time-domain EEG from 0- 0.5 s after motor imagery onset was used as the neural-state input. A Q-KTD model trained on the healthy population was transferred to individual stroke participants, and repeated Monte Carlo simulations compared decoding performance with and without transfer learning across multiple learning epochs. ResultsHealthy and acute stroke participants showed shared motor imagery-related EEG structure, including post-onset mu-band suppression, while the stroke group exhibited greater interparticipant variability, more diffuse time- frequency modulation, and altered hemispheric asymmetry. No channel-level healthy-stroke differences in windowed band power remained significant after false discovery rate correction. Healthy-source transfer learning improved first-epoch Q-KTD success rates in 29 of 50 stroke participants (58%). Across all participants, mean success rate increased from 49.46% without transfer learning to 51.82% with transfer learning. Among participants showing positive transfer, the mean gain was 7.34% and the maximum gain was 18.75%. However, 21 participants showed negative transfer, demonstrating substantial subject-level variability. ConclusionHealthy-source Q-KTD transfer learning improved first-epoch motor imagery BMI decoding for a majority of acute stroke participants, supporting the offline feasibility of population-informed Q-KTD decoding in stroke. These early performance gains may reduce subject-specific calibration burden, although substantial interparticipant variability and negative transfer indicate the need for individualized transfer-selection or adaptation strategies. Assistive Technology ImplicationsO_LIEEG-based brain-machine interfaces may support assistive technologies for individuals with stroke-related motor impairment by translating motor imagery-related neural activity into control commands for external devices. C_LIO_LIHealthy-to-stroke transfer learning may improve early BMI neural-decoder performance and potentially reduce the amount of subject-specific calibration required. C_LIO_LIThe findings support the offline feasibility of Q-KTD for motor imagery BMI neural decoding in individuals with acute stroke. C_LIO_LISubstantial interparticipant variability and negative transfer suggest that individualized source-model selection or adaptation strategies may be needed for reliable post-stroke BMI implementation. C_LIO_LIPhysiological EEG characteristics, including ERD/ERS and hemispheric asymmetry, may provide candidate markers for future transfer-selection strategies, although their predictive value requires direct validation. C_LI

Non-invasive brain-computer interfaces (BCIs) have substantially advanced in the field of continuous cursor control over the past decade. Yet, current methods lack key control aspects such as initiation and termination of cursor movements as well as evaluation in real-world applications. In this study, we introduce a framework for continuous, electroencephalography-based cursor control that supports both active movement and no-movement states, thereby allowing for inactive periods of the user when no control input is desired. We demonstrate its applicability in healthy participants and show its performance in real-world application through the selection of targets on a screen. This demonstrates that participants can leverage the continuous control cursor control and the intentional starting and stopping of motions to effectively select targets on a screen through dwell-time selection. On average, 7.1 out of 40 targets were correctly selected (level of significant performance: 4.5 targets), while experienced BCI users achieved an average of 12.8 targets. The proposed framework additionally demonstrates compatibility with motor-impaired people without residual hand motions since it does not rely on observable movements for model training.

Brain-computer interfaces face a fundamental trade-off between the signal fidelity and stimulation precision of noninvasive systems and the surgical burden and scalability of invasive systems. Non-invasive BCIs suffer from low signal quality and poor stimulation accuracy due to the skull barrier and the variability introduced by the scalp and skull. Existing invasive BCIs rely on traumatic surgical procedures or brain-penetrating electrodes, which limits their spatial extensibility, application, and patient acceptance. Here, we introduce a minimally invasive hybrid BCI architecture that uses the skull as a distributed interface layer rather than treating it solely as a barrier. The hybrid BCI comprises four integrated components: (1) the safe and smart micro-hole craniotomy; (2) distributed microelectrodes subcutaneously implanted in micro-holes in the skull with the distal end in contact with the dura; (3) an external bi-directional wearable headset for coupling, recording, stimulation, and channel selection; and (4) an AI-assisted planning and control agent. Animal studies have shown that micro-holes with a diameter of 300-800 m can be safely and conveniently prepared at any predefined locations across the skull without impairing the dura. In vivo experiments on rats demonstrate that the hybrid BCI with skull-implanted microelectrodes evidently increases resting-state spectral power and improves the signal-to-noise ratio of somatosensory and steady-state visual evoked responses compared to the scalp EEG; the computational modelling shows that distributed skull-dura microelectrodes can increase the intracranial electrical field strength and steer focused temporal-interference fields towards predefined deep brain targets. These findings will lay a solid foundation for future endeavors in wireless integration, safety evaluation and clinical benefits of the hybrid BCI. In summary, we propose the hybrid BCI as a distinct minimally invasive BCI paradigm with the great potential as a distributed, scalable, and upgradable neural interface that can expand the clinical application of minimally invasive BCI techniques.

Motor unit (MU) decomposition using high-density surface electromyography (HD-sEMG) has been widely used to characterize MU behavior in neurophysiology and to build neural-machine interfaces for wearable robots. Recently, many open-source software tools for MU decomposition have been made available on GitHub, which could reduce the effort of researchers in the field. However, the consistency among these open-source tools has never been studied, making researchers hesitate to use them. In this study, we collected 7 open-source software tools on GitHub and applied them to decompose MUs from an open-source HD-sEMG dataset (including 11 isometric contraction trials) to investigate the consistency among these tools. To create a comprehensive MU pool for reference, we combined all unique MUs identified by seven tools, visually inspected and removed bad MUs, and manually edited all remaining MU spike trains. Across 7 tools for 11 trials, the number of identified MUs ranges from 167 to 736. The number of valid MUs after expert inspection ranges from 29 to 210, which is 10% to 72% of the reference pool. The rate of agreement between the raw MUSTs and the manually edited MUSTs ranges from 0.86 to 0.94, and the averaged number of edits per MU to correct misalignments ranges from 14 to 39. The results show inconsistency in the implementation and procedures of each tool, which results in an inconsistent number of identified MUs and valid MUs (29 vs 210). In general, a substantial amount of effort is required to process the raw MUSTs from each tool to conduct further research analysis. This study provided a guideline for using open-source software tools for MU decomposition and indicated that it would be beneficial to develop tools to automatically edit the MUSTs.

Quantitative gait analysis could enhance personalized treatment for many movement-related conditions; however, it is not routinely integrated into clinical care. Advances in mobile sensing, such as smartphone-based motion capture, enable rapid clinical gait assessment, but extracting actionable insights remains challenging. Although machine learning models can support clinical decisions from gait data, they typically require costly task- and condition-specific datasets, which limits progress across various gait-related conditions. Here we present a generative foundation model of walking kinematics that enables various downstream clinical tasks across diverse patient populations using clinically accessible smartphone video-based gait analysis. We aggregated eight gait datasets comprising 657 individuals across seven unique pathologies. Using weakly-supervised learning, we trained a variational autoencoder to distill high-dimensional gait kinematics into a 16-dimensional learned latent representation. We demonstrate generalizability across four downstream clinical tasks spanning pathologies both seen and unseen during training, with and without model fine-tuning, including: 1) classification of neuromuscular disorders unseen during training, 2) predicting clinical severity scores for individuals with Parkinson's disease, 3) tracking of subacute recovery post-stroke, and 4) generating patient-specific kinematic changes following total hip arthroplasty. Our model also computes a deviation from mean unimpaired (DMU) score, an interpretable scalar metric that captures an individual's deviation from typical unimpaired gait, providing rapid, holistic quantification of impairment. This generalizable model provides a foundation for clinically actionable tools that translate mobile sensing-derived gait data into precise biomechanical insights for clinical research and decision-making. The open-source model is deployed in the cloud for automated smartphone video-based gait analysis on our freely available OpenCap platform.

Classification of motor imagery (MI) tasks through EEG is valuable in brain-computer interfacing and rehabilitation engineering. EEG channels selection for MI task classification is well discussed problem and is challenging due to its combinatorial nature. Most of the existing methods are subject and task-dependent. This paper introduces a subject-independent EEG channel selection. The proposed approach consists of two stages. First, we rank channels based on their divergence from a reference channel Cz. We hypothesize that channels less divergent from Cz are more relevant for MI task classification. In the second stage, we employ a three-stage feature selection and classification model to evaluate the selected channels. It consists of a bandpass filter, followed by common spatial pattern (CSP) filter and three classifiers viz. SVM, 1-NN and 5-NN. Two publicly available datasets viz. PhysioNet and BCI Competition III IVa datasets have been used to assess the method. It performs 15.21\% more than 3Cs and just 2.91\% less than all-channels accuracy with as few as 20/118 channels on BCI Competition data and 19.64\% more than 3Cs on the PhysioNet dataset with 16/64 channels. Empirical comparison implies that the method performs better than classical models such as CSP Rank, fishers rank, and normalized mutual information, significantly. Results support that our hypothesis that divergence between channels and a reference channel Cz can be used as a ranking measure for channel selection.

Understanding the neuromuscular properties that allow dexterous manipulation of objects remains a major challenge in neurorehabilitation, largely due to the difficulty of characterizing intrinsic hand muscle activity. These muscles are small, densely packed, and anatomically complex, making selective recordings with intramuscular electromyography (EMG) technically demanding and impractical for comprehensive studies. In this work, we present a custom, high-density (HD) surface EMG grid designed to non-invasively capture activity from intrinsic hand muscles from both dorsal and palmar surfaces. We evaluated the quality and spatial selectivity of the recordings by directly comparing them with intramuscular EMG signals obtained from the dorsal and palmar interossei. Surface EMG signals corresponded closely to the intramuscular recordings, with high correlation values for all subjects and tasks. Double differential spatial filtering significantly improved selectivity, although some residual volume conduction remained. The dorsal grid primarily captured dorsal interossei activity, while the palmar grid was more sensitive to lumbrical activation. The palmar interossei recordings were spatially more varied, with the second palmar interosseous predominantly detected on the dorsal grid and the third and fourth on the palmar grid. Together, these results demonstrate that non-invasive HD surface EMG will allow more complete measurement of intrinsic muscle activity, to provide a better understanding of the complex relation between the intrinsic and extrinsic hand muscles during dexterous movements. This basic information will allow refinement of biomechanical hand models and prosthetic devices, and the development of biomimetic brain computer interfaces aimed at restoring natural hand function after neurological injury.

Freezing of gait (FoG) in Parkinson's disease is a brief but hazardous gait failure that often precedes falls. For wearable cueing or other closed-loop assistance, a detector that reacts only after FoG onset is usually too late; the more useful task is to recognize the pre-freezing transition from physiological signals. This study presents PreFoGNet, a dual time-frequency deep learning framework for early FoG prediction using plantar pressure signals. The temporal stream combines a multi-scale Inception encoder with a bidirectional Mamba module to capture both short contact-related transients and several-second gait deterioration without the quadratic cost of attention. In parallel, the frequency stream uses band-wise spectral modeling and attention-based gating to emphasize physiologically meaningful changes in the locomotion, freeze-related, and high-frequency bands. On the WearGait-PD dataset, with a 2 s prediction horizon and subject-wise evaluation, PreFoGNet achieved a sensitivity of 93.94%, a specificity of 89.76%, a G-Mean of 0.9183, and an AUC-ROC of 0.9607. It outperformed classical machine-learning and deep learning baselines, and retained usable performance under moderate noise and single-channel loss. Additional horizon analysis showed that plantar pressure contains a stable pre-freezing signature within 0-3 s before onset, with a practical prediction boundary of approximately 6-7 s. These findings suggest that time-frequency modeling of plantar pressure is a promising signal-processing route for wearable FoG early-warning systems.

P300 spellers convert electroencephalographic (EEG) activity into text by presenting users with a matrix of flickering characters. While these systems can achieve high classification accuracy, communication is severely slowed by the need for many stimulus repetitions to obtain a reliable signal. Reducing repetitions accelerates spelling but introduces character-level errors: insertions, deletions, and substitutions that degrade usability and increase user fatigue. Although substantial research has focused on improving performance at the signal acquisition and decoding stages, here we investigate a complementary text post-processing approach that leverages large language models (LLMs) to restore corrupted P300 speller output. We constructed a dataset derived from cLang-8 and simulated realistic P300-style text corruption using both random and empirically derived human-like error strategies. We evaluated several instruction-tuned LLMs alongside an optical character recognition (OCR)-fine-tuned ByT5 model under zero-shot and few-shot prompting conditions. We found that LLMs effectively recovered clean text from noisy inputs. Models employing SentencePiece tokenization consistently outperformed byte-pair encoding (BPE)-based counterparts, and few-shot in-context learning further improved restoration accuracy, with Gemma 3 achieving the strongest performance across all settings. These results suggest that LLM-based post-processing could enable P300 speller systems to operate with fewer repetitions and lower latency while maintaining or improving output accuracy, offering a practical path toward more efficient daily communication for users with motor and speech disabilities.

Sleep deprivation is associated with impaired endurance performance and an increased risk of running-related injury. Previous research has identified alterations in running biomechanics following a single night of sleep deprivation under laboratory conditions. However, whether these biomechanical changes can be detected using wearable technology remains unknown. Twenty-one recreationally active runners completed submaximal treadmill running under both normal sleep and total sleep deprivation conditions in a randomized crossover design. Biomechanical features were extracted simultaneously using a full-body motion capture system and a trunk-mounted wearable sensor. Five machine learning classifiers were evaluated in two classification tasks: a within-subject task using paired recordings from the same individual, and a between-subject task performed without individual baseline data. Within-subject classification consistently exceeded chance level for both measurement systems, with best accuracies of 85% for the wearable sensor (Logistic Regression) and 83% for the motion capture system (Random Forest). These findings indicate that sleep deprivation produces a systematic and individually consistent biomechanical signature during running. In contrast, between-subject classification failed across nearly all models and systems, with accuracies remaining close to chance level ([~]50%), demonstrating that inter-individual variability obscures the sleep-deprivation signal in the absence of personalized baseline data. Both systems converged on temporal organization, loading-related variables, and stride-to-stride variability as the most discriminative feature domains. Contrary to expectations, the laboratory motion capture system did not outperform the wearable sensor. Together, these findings demonstrate that individualized, baseline-referenced monitoring is essential for detecting sleep-deprivation-related changes in running gait, and suggest that a single trunk-mounted wearable sensor may provide a practical solution for real-world monitoring when paired recordings are available.

Background. Balance training in older adults can lead to reduced centre of mass accelerations and reduced angular momenta after perturbations of unipedal stance, reflecting an enhanced ability to recover balance. It has been suggested that the co-occurring changes in muscle synergies indicated strategy-specific adaptations in feedback control. Methods. We investigated the cortical involvement in such adaptations by focusing on the interaction between muscle synergies and cortical activity after perturbations. Twenty older adults (>65 years) underwent short-term and three-week long-term balance training, and we assessed their recovery from unpredictable mediolateral perturbations during unipedal stance. We measured high-density EEG and activation of leg and trunk muscles. The representations of the balance-related muscle synergies were localised in the cortex using coherence-based beamformers in the {beta}-frequency band. Results. Balance performance was accompanied by task-specific {beta}-band activation in the somatotopic representation of the lower extremities in the primary motor cortex. The {beta}-power significantly dropped during the response to perturbations, while the coherence with the activation of muscle synergies significantly increased, especially for synergies active in the early stage of balance recovery. The task-related changes in cortico-synergy coherence, especially during the later phase of balance recovery, were significantly affected by short-term training. Conclusion. Refinements of feedback control seem to underlie balance improvements in older adults. The significant changes in the cortico-synergy interaction after balance training suggest cortical involvement in these refinements.

Ground reaction force (GRF)-based gait analysis provides objective, non-invasive evidence for neurological and musculoskeletal assessment, but its translation into medical AI decision support is limited by heterogeneous sensing devices, variable-length recordings, acquisition noise, sensor failures, and restricted access to high-cost gait laboratories. We propose SubGaitNet, a decision-oriented and interpretable AI framework designed to address four clinically relevant challenges in GRF-based medical AI: signal-length variability, sensing noise, long-range gait-phase dependency, and pathological frame-to-frame variability. SubGaitNet integrates GRF temporal slicing, multi-scale deep residual shrinkage, masked Transformer modeling, and a Sub-LSTM branch for adjacent-frame variability modeling. In subject-independent evaluation on two public clinical gait datasets, SubGaitNet achieved an AUC of 0.979 for Parkinson's disease (PD) screening and an ACC of 0.940/F1-score of 0.910 for Hoehn & Yahr severity assessment using wearable pressure insoles. On the GaitRec force-plate dataset, SubGaitNet achieved ACC values of 0.951 and 0.918 for four-class and five-class musculoskeletal impairment assessment, respectively. Additional analyses showed stable bootstrap confidence intervals, calibrated PD screening probabilities (Brier score = 0.059; expected calibration error = 0.051), positive decision-curve net benefit across clinically relevant thresholds, and ordinally plausible H&Y errors. Robustness tests under simulated sensor failure, noise perturbation, and reduced-channel inputs supported the model's stability under clinically plausible sensing uncertainty and accessibility constraints. SHAP explanations highlighted biomechanically meaningful hindfoot and forefoot regions. Overall, SubGaitNet provides a reusable, interpretable, and decision-support-oriented AI methodology for GRF-based gait health assessment, while prospective clinician-in-the-loop validation remains necessary before clinical deployment.

Musculoskeletal models are widely used to relate muscle mechanics to movement patterns in biomechanics. Accurate estimation of muscle parameters is essential for building individualized models, yet most rely on generic parameters derived from cadaveric data that do not reflect subject-specific properties critical to force generation. Here, we introduce a hierarchical Bayesian framework that leverages surface electromyography (EMG) and torque data from isometric elbow tasks to estimate subject-specific muscle parameters, overcoming limitations of generic parameter sets. This approach accounts for both inter-individual variability and uncertainty in measurement and model structure. The model infers six key parameters per subject, including flexor and extensor muscle strength, tendon slack length, moment arm geometry, and nonlinear EMG-to-activation relationships. We estimated model parameters for 14 young, healthy adults performing isometric elbow flexion and extension at multiple joint angles and torque levels. The six-parameter hierarchical-Bayesian musculoskeletal model accurately reproduced measured net elbow torque (R2 = 0.96) and outperformed simpler configurations. Muscle strength parameters varied substantially across individuals, from approximately 1.0 to 3.5. On average, participants exhibited about twice those of the OpenSim 26 generic model. In contrast, tendon slack length estimates varied minimally across subjects. Bilateral testing revealed moderate correlations between left- and right-arm parameters, supporting the models ability to capture subject-specific anatomical features. Cross-validation confirmed robust predictive performance, and convergence diagnostics indicated reliable sampling. Compared to traditional EMG-driven or imaging-based personalization methods, our approach quantifies uncertainty, enables partial pooling across subjects, and avoids reliance on invasive or time-intensive measurements. The framework is extensible to dynamic tasks and adaptable to clinical populations, including individuals post-stroke. These results demonstrate that hierarchical Bayesian inference can robustly personalize musculoskeletal models and advance our understanding of biomechanics.

Brain-computer interfaces (BCIs) offer a promising solution to speech loss due to neurological injury by decoding intended speech directly from brain activity. While recent BCIs have restored high-accuracy text-based communication, they fail to provide instantaneous voice output essential for the natural flow of conversation. Brain-to-voice BCIs address this gap by decoding voice directly from neural signals. However, even the state-of-the-art (SOTA) BCI-synthesized voice is not yet intelligible enough for real-world adoption. We introduce brain2voice 2.0, a new multimodal Transformer-based BCI decoder architecture capable of synthesizing highly intelligible voice from intracortical neural signals in real-time. Brain2voice 2.0 is trained on continuous and custom-tokenized acoustic targets and phoneme targets, leveraging their complementary speech information. We use self-supervised and adversarial training objectives that enhance acoustic feature quality and improve synthesis intelligibility. At each 10 ms timestep, the model causally outputs continuous and tokenized acoustic features for real-time voice synthesis as well as time-aligned phoneme predictions (raw phoneme error rate: 7%, comparable to the latest brain-to-text models). We evaluated this new approach on our prior intracortical brain-to-voice benchmark dataset (Wairagkar et al. 2025). Naive human listeners transcribed brain2voice 2.0 synthesized voice with a word error rate of 5.24%--an 8x improvement in intelligibility over previous SOTA results (43.75%). Brain2voice 2.0 demonstrates that highly intelligible real-time voice synthesis from neural signals is achievable, for the first time crossing the intelligibility threshold necessary for clinically viable brain-to-voice BCIs for people with paralysis.

Spinal cord injury (SCI) causes rapid and severe bone loss in the paralysed lower limbs, particularly at the distal femur and proximal tibia, where fragility fracture risk is high. In vitro nanoscale vibration at 1 kHz has been shown to promote osteogenic differentiation and inhibit osteoclastogenesis, suggesting potential as a targeted mechanical intervention. This study aimed to develop and evaluate a wearable device for delivering and monitoring localised nanovibration at the distal femur in individuals with SCI. The device delivered continuous sinusoidal nanoscale stimulation at 1 kHz via a bone-conduction transducer, with an opposing accelerometer used to monitor transmitted vibration in real time. Design and target-site selection were refined through two healthy-volunteer investigations comparing the distal femur, proximal tibia, and distal tibia. Bovine femur experiments characterised vibration transmission under controlled benchtop conditions. Preliminary repeated-use feasibility was assessed in one individual with motor-complete SCI. Healthy volunteer testing showed that although the ankle initially produced the highest transmitted amplitudes, these were highly variable, and positioning was inconsistent. Within the knee region, the distal femur provided the most practical and repeatable site for a wearable application. In bovine femur experiments, scanning laser vibrometry demonstrated measurable vibration on the condylar surface opposite the transducer, and depth-resolved measurements confirmed that nanoscale vibration remained detectable within bone. A gel interface layer reduced the transmitted amplitude. In the feasibility evaluation, 61 sessions were completed over 14 weeks, with logged accelerometry confirming repeated nanoscale vibration transmission. These findings establish feasibility and support further device optimisation and translational studies.

Spinal cord injury (SCI) is a devastating neurological injury that results in the profound loss of voluntary motor function and marked reduction in quality of life. Rehabilitation remains as the standard of care for recovery after SCI; however, it often falls short in recovering meaningful motor function. Spinal cord stimulation (SCS) has emerged as a promising neurostimulation approach to fill this gap and recover lost voluntary motor function. Two main approaches of SCS have been designed and implemented for human use: epidural and transcutaneous SCS. Over the last two decades, several clinical studies have shown convincing evidence that both epidural and transcutaneous SCS can be used in conjunction with rehabilitation to improve motor function of individuals after SCI. Yet fundamental clinical questions remain unanswered: when should clinicians choose epidural or transcutaneous SCS, which technique provides the most durable outcomes, and for whom is each therapy best? Without these answers, widespread and meaningful adoption of either approach into clinical practice will remain limited. To address these questions, in this Review, we define the distinct therapeutic goals, intended use cases, clinical parameters, and responder profiles for both epidural and transcutaneous SCS to guide their eventual adoption into clinical practice. We found that indeed epidural and transcutaneous SCS serve distinct therapeutic roles. Epidural SCS is designed as an assistive therapy that can restore muscle activity and single joint movements immediately within one week of implantation, while transcutaneous SCS is designed as a long-term therapeutic device with cumulative functional gains observed over treatment periods of up to 18 weeks. Lastly, epidural SCS produced benefits for all participants (AIS A-D) despite the extent of their injury, while transcutaneous SCS only consistently benefits individuals with incomplete motor injuries (AIS C-D).

Distal radius fractures (DRFs) can delay return to activities of daily living and social participation because of postoperative pain, temporary joint immobilization, and limited wrist and forearm range of motion. The Ghost System developed at Saitama Prefectural University, Japan, combines visual action observation with tendon vibration stimulation and has shown potential as an adjunct to conventional rehabilitation. This Study Protocol describes a modified Ghost system intended to improve clinical implementation by replacing the head-mounted virtual reality display with iPad-based action observation and by using a wristband-type vibrator. This single-center, single-arm, open-label feasibility trial will enroll 10 adults after palmar locking plate fixation for DRF. The intervention will be delivered twice weekly during outpatient rehabilitation follow-up sessions from the early postoperative period (postoperative days 2-10 after enrollment) through the approved early postoperative rehabilitation period (generally up to postoperative week 8), in parallel with standard rehabilitation practices. Primary feasibility and preliminary clinical outcomes include device fit and acceptability, pain assessed using a 100-mm Visual Analog Scale, and wrist/forearm range of motion. Secondary implementation and safety outcomes include Disabilities of the Arm, Shoulder and Hand (DASH), Patient-Rated Wrist Evaluation (PRWE), Hand20 Questionnaire (HANDS-20), EuroQol 5 Dimensions 5 Levels (EQ-5D-5L), body ownership and hand-illusion questionnaires, setup time, setup errors, adherence, adverse events, and device incidents. We hypothesize that the modified Ghost system will be feasible and acceptable for early postoperative outpatient rehabilitation and will be delivered without serious device-related adverse events. Clinical outcomes will be summarized descriptively to inform a future controlled study rather than to establish efficacy.

Background Cortical processing of the knee-joint proprioception is largely unknown. Magnetoencephalography (MEG) can be used to quantify the cortical processing of the proprioceptive afference, but MEG-compatible and well-controlled stimulation of the knee joint is technically challenging, and thus has received less attention. New method We introduced a novel MEG-compatible stimulator that delivers controlled patellar tendon stretches to activate muscle afferent of the knee extensors. The stimulus intensity is adjustable, allowing graded activation of proprioceptive input and, when required, elicitation of the patellar-tendon reflex. Results The novel stimulator elicited clear muscular and cortical responses in both intensity conditions. Cortical responses demonstrated moderate to excellent intersession reliability for peak evoked field amplitude (ICC: 0.69--0.96), beta suppression (0.89--0.90) and beta rebound (0.96--0.97). Notably, beta suppression peaked more laterally than expected in both hemispheres. Peak EMG amplitudes in VL and VM muscles were reliable for both intensity conditions (ICC: 0.66--0.89), and stimulus kinematics remained consistent throughout measurements. Comparison with existing methods Previous robotic or motor-driven devices have been used to evoke cortical responses to knee-joint proprioceptive stimulation, but mechanical coupling across adjacent joints may limit knee-specific input. The present stimulator provides mechanically simple and MEG-compatible alternative that targets knee extensor afferents more directly, reduces distal joint involvement. Conclusion The novel stimulator is a feasible and repeatable tool to study cortical processing of proprioceptive afference from the knee-joint using MEG. The spatially unexpected beta rhythm suppression suggests that knee-joint proprioceptive afference may involve more unique sensorimotor cortical neuronal network than previously recognized.