IEEE Transactions on Neural Systems and Rehabilitation Engineering

Human-in-the-loop optimization (HILO) is an established method for identifying subject-specific optimal controllers for performance augmentation. For HILO algorithms to be useful in rehabilitation, however, the optimization algorithm may need to account for how the human response changes over time in response to assistance. In this study, we tested a modified version of Bayesian optimization (BO), dynamic Bayesian optimization (DBO), in a three-parameter optimization problem that sought to identify participant-specific optimal solutions for increasing walking speed. As opposed to BO, DBO accounts for the non-stationarity of human responses. Sixteen healthy participants received bilateral hip torque pulses delivered by a hip exoskeleton. The exoskeleton torque parameters were determined using HILO with either DBO or BO. Validation iterations were introduced to objectively compare performance across optimizers at different time points of HILO. The results showed that both DBO and BO significantly increased walking speed compared to baseline. When comparing performance between DBO and BO, DBO emerged as an improvement over BO both in terms of efficacy, modeling accuracy, and personalization. DBO induced changes in walking speed relative to baseline that exceeded those induced by BO at three of the four validation iterations. DBO outperformed BO in modeling accuracy in later validation iterations. DBO personalization induced changes in walking speed that were significantly greater than those induced by previously identified assistive solutions, while this was not the case of BO. Overall, our results indicate that DBO outperformed BO due to its greater ability to account for non-stationary aspects of the human response.

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.

Ground reaction force (GRF) measurement remains largely confined to instrumented laboratories, limiting longitudinal monitoring in daily life. This article presents an edge-first wearable system for estimating vertical GRF from consumer smartwatches. Two Apple Watch Series 6 devices worn at the wrist and waist stream 12-channel inertial data at 100 Hz to an iPhone, where preprocessing, storage, and inference occur locally without cloud dependence. The proposed GRFNet-MultiScale model is a compact temporal convolutional network with four dilated residual blocks and a global context branch. Under leave-one-subject-out evaluation on 539 stance windows from 10 healthy participants, the dual-sensor system achieved a mean Pearson correlation of 0.798 with an RMSE of 257 N, while a wrist-only configuration retained 82.5% of dual-sensor correlation. Temporal attribution remained stable across validation folds and identified early-stance wrist acceleration as the dominant reproducible signal. The system is strongest for cyclic locomotion.

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

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.

People with lower limb amputation lack somatosensory feedback from their prosthesis, and this loss contributes to functional deficits, including balance and gait impairments. Recent advances in neuroprostheses have demonstrated that electrical stimulation of sensory nerves in the residual limb and spinal cord can restore lost sensations in the lower limb. To maximize the efficacy of these somatosensory neuroprostheses, the restored sensations should be intuitive, seamlessly integrating into the sensorimotor network. However, it is challenging to quantify the intuitiveness of these evoked sensations. Recent studies have proposed using crossmodal congruency effect (CCE) tasks for this purpose in people with upper-limb amputation. The current study tests the feasibility of the CCE task for assessing the intuitiveness of sensory feedback in the lower limb. We hypothesized that CCE score would reliably differentiate between a more natural (pneumatic) sensation and a less natural (electric) sensation at two locations: the knee and the foot. Across fifteen able-bodied individuals, we observed that the CCE task differentiates sensory modalities at the knee, but not at the foot. Identification of external factors affecting the CCE is needed before it can be implemented to measure intuitiveness of sensory feedback in lower-limb amputees.

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.

Many people which are affected by drop foot syndrome, have to face difficulty while walking which leads to pathological gait. This type of syndrome is treated by means of an external artificial stimulation known as functional electric stimulator (FES). In this paper we are designing an online feedback control system which optimize the strength of a FES given to paretic muscle which results in correction of pathological gait of the patient in a tolerable domain. Different phases of gait are identified using inertial measurement unit (IMU) as a feedback sensor mounted on the foot. Data is collected form 8 different healthy subjects and average of collected data is used as a reference template. Different trajectories of drop foot patients are simulated (due to unavailability of patients) and corrected according to the reference template.

Accurate detection of P300 event-related potentials from electroencephalography (EEG) re-mains challenging for small numbers of trials due to low signal-to-noise ratios and substantial inter-subject variability. This study presents a systematic comparison of data aggregation strate-gies for improving P300 classification, evaluated on a 10-subject dataset using two convolutional neural network architectures (EEGNet and BaseCNN) and a support vector machine (SVM). We compared: (1) subject-specific and pooled general models for single trials; (2) epoch aver-aging with 5 and 10 stimuli repetitions; (3) multi-channel models where subjects corresponded to different input channels; (4) cross-subject averaging; (5) mixed (uncontrolled) averaging; (6) a combined approach with K trials per subject across all participants; and (7) time-shifted channels from extended single-trial epochs. Decoding performance was quantified using the Information Transfer Rate (ITR), computed for binary classification accuracy. We found that single-trial ITR was unpractical (0.15-0.64 bits/trial), whereas controlled aggregation improved the performance. The combined cross-subject approach with K = 3 trials per participant (30 channels) achieves the highest ITR with multi-channel EEGNet: 0.95 bits/aggregated decision in the no-aperture recordings and 0.97 bits/aggregated decision on Aperture data, approaching the theoretical binary-classification limit for the aggregated decision. Controlled cross-subject averaging consistently outperformed random trial mixing, and multi-channel architectures out-performed simple averaging when inter-subject structure was preserved. These findings con-tribute to improving P300 decoding and implementing multi-subject brain-computer interfaces (BCIs).

Non-invasive human-machine interfaces (HMIs) are critical in developing prosthetic systems that offer intuitive, simultaneous, and proportional control over multiple degrees of freedom (DOFs). This study introduces a novel system for intuitive concurrent control of hand and wrist movements using sonomyography based imaging of muscle activity. Our method uses a sparse set of ultrasound scanlines to reduce computational complexity while enhancing usability. We evaluated four regression techniques for wrist and hand angle prediction, focusing on performance with a reduced sonomyographic feature set. We also explored the feasibility of a sonomyography-based system by simulating various factors that could affect prediction, including feature selection and scanline count. Our findings demonstrate that Gaussian process regression excels in predicting wrist and hand angles with just eight equispaced transducers in offline settings. Real-time evaluations with 10 non-disabled participants showed a 93 % success rate for two-DOF tasks using linear regression. The system was tested with an individual with amputation, achieving a 46 % success rate for two-DOF control in a 2D space, even though the ground truth data for model training was collected from the contralateral limb. This study validates our sonomyography-based approach for accurate wrist and hand angle estimation, reducing complexity and demonstrating potential in real-world scenarios.

Age-related decline in postural control is an important factor that increases the fall risk of older adults. Fingertip vibrotactile stimulation has been developed to provide light touch-like somatosensory input. However, evidence regarding differences among older age groups is limited. This study examined the effects of fingertip vibrotactile stimulation on postural control in 348 community-dwelling older adults classified as young-old (age 65-74 years), old-old (age 75-84 years), and oldest-old (age 85 years or older). Participants stood with eyes closed and feet together under stimulation and no stimulation conditions. The center of pressure (COP) velocity and COP area were measured using a force plate. The natural log-transformed COP area was used for the analysis. Linear mixed models were used to examine the effects of age group, stimulation conditions, and measurement segments. The COP velocity under the stimulation condition was significantly lower than that under the no stimulation condition; however, the COP area did not change significantly. Significant main effects of age group were observed for both COP indices, but no interaction between age group and stimulation condition was observed. Fingertip vibrotactile stimulation may reduce the COP velocity across older age groups, thus reflecting the effects on postural adjustment frequency.

The study of human locomotion has long relied on descriptive frameworks of the gait cycle, which have provided essential insights into the functional phases of walking and their underlying biomechanical demands. While these models remain highly informative, they are largely based on observational analyses and may not fully capture the continuous, global coordination that characterizes human movement. The present study proposes an integrated framework to study whole-body coordination. This framework combines network theory with non-negative matrix factorization (NNMF) to treat gait as a dynamic system of coordinated joint interactions. Using three-dimensional kinematic data from 60 healthy subjects, we constructed a representation of whole-body coordination across time, named "dynamic kinectome". It was then decomposed using NNMF to extract spatial patterns of joint coordination and their corresponding temporal activations, allowing for an interpretable characterization of locomotor organization while preserving physiological meaning. Our analysis extracted six robust, highly consistent, and symmetrical coordination patterns across participants, effectively capturing the primary functional subtasks of locomotion. Rather than challenging classical phase descriptions, these findings enrich them by showing how coordination emerges as a continuous, often proactive process that can extend across conventional phase boundaries and systematically integrates the upper limbs for dynamic stability. Overall, this study provides a holistic, data-driven perspective on human locomotion, offering a promising basis for future investigations in motor control and may contribute to the development of sensitive biomarkers for clinical and rehabilitative applications.

ObjectiveElectroencephalography (EEG) is a key tool for studying auditory processing in cochlear implant (CI) users. In particular, EEG recordings obtained during continuous speech are becoming increasingly important for assessing speech and language processing in CI users, and may be utilized for neurofeedback. However, CIs also induce strong stimulation artifacts that are time-locked to the stimulus and mask the neural responses that have smaller magnitudes. Existing artifact reduction methods are typically based on event-related potentials (ERPs) or require manual component selection, making them unsuitable for naturalistic listening conditions or large datasets. ApproachWe develop CORSICA (CORrelation-baSed ICA artifact rejection), a reproducible, parameter-efficient method for CI artifact reduction in EEG responses to continuous speech. CORSICA operates on independent components (ICs) obtained through Infomax ICA and requires no manual component labelling, with performance governed by a single tunable threshold. It exploits the observation that CI artifacts temporally follow the audio signal without delay, whereas neural responses have an inherent lag due to auditory pathway latencies. For each IC, CORSICA computes the cross-correlation with the speech stimulus. Artifacts are identified by a high signal-to-noise ratio (SNR) of the correlation peak near zero lag, and the component is rejected if this SNR exceeds a threshold. To benchmark CORSICA, we evaluate two alternatives: a TRF-based SNR method, in which temporal response functions are fitted to each IC and artifact-driven peaks near zero lag are used for rejection, and a variant replacing ICA with second-order blind identification (SOBI) as the source separation step. Main resultsCORSICA effectively suppressed CI artifacts while preserving neural activity, enabling recovery of physiologically plausible TRFs with only 2% of ICs rejected. Both benchmark methods confirmed the validity of the SNR-based rejection framework, but CORSICA outperformed the TRF-based alternative in artifact suppression quality. Replacing ICA with SOBI as the source separation step required more ICs to be rejected, further supporting ICA as the preferred backbone for CORSICA. SignificanceCORSICA provides a fully objective, label-free approach to identifying CI artifacts in speech-evoked EEG data, with no manual intervention required. By centering artifact rejection on a single interpretable threshold, it offers a reproducible preprocessing standard for future EEG studies on speech processing in CI users. ConclusionOur findings demonstrate that objective CI artifact suppression in speech-evoked EEG data is feasible on the basis of the ICs temporal response patterns.

Background: To examine the association between ultrasound-based muscle activity detection, or sonomyography (SMG) derived metrics, and clinical measures of upper-extremity function in individuals with cervical spinal cord injury (cSCI), and to evaluate SMG-based trajectories derived directly from muscle activity as a muscle-level assessment compared to conventional kinematic approaches. Methods: Eight individuals with cSCI (n = 8; American Spinal Injury Association Impairment Scale grades A to C; injury levels C5 to C6) participated. Participants performed a wrist tenodesis based target achievement task while SMG data were collected. SMG derived metrics were correlated with performance-based upper extremity function assessed using the Jebsen Taylor Hand Function Test (JTHFT) and self-reported function assessed using the Capabilities of Upper Extremity Questionnaire (CUE-Q). Associations were quantified using distance correlation (dCorr). Results: Strong associations between SMG-derived metrics and clinical measures were observed. Movement Arrest Period Ratio (MAPR) showed the strongest association with JTHFT performance (dCorr = 0.75), while Time to Peak Velocity (TTPV) demonstrated a moderate association (dCorr = 0.62). Rate of Change of Acceleration (ROCAcc) showed a strong correlation with CUE-Q scores (dCorr {approx} 0.70), and spectral arc length (SAL) showed moderate correlations (dCorr {approx} 0.66). Conclusions: SMG-derived metrics show meaningful associations with both performance-based and self-reported measures of upper-extremity function in individuals with cSCI. These findings suggest that SMG metrics can serve as objective tools to complement clinical assessments for tracking functional status and recovery. Larger studies are needed to confirm these observations.

Surface electromyography (sEMG) is a robust non-invasive modality for human-machine interaction, yet its application remains largely limited to coarse motor tasks such as grasping or rotation. The decoding of fine motor skills, specifically handwriting, remains a challenging problem with potential relevance for prosthetic control and natural communication interfaces. In this work, we explore a Transformer-based alternative to classical signal-processing pipelines that treats multi-channel sEMG signals as complex time series. We introduce DualMyo, a specialized model integrating Patch Embeddings and Rotary Positional Embeddings (RoPE) to capture the intricate spatio-temporal dynamics of myoelectric activity. Our experimental results show strong intra-session performance. Furthermore, we address the inherent challenges of signal drift and sensor displacement in cross-session applications. We show that a lightweight fine-tuning strategy of 10 epochs enables DualMyo to effectively adapt to session variability, achieving approximately 91\% accuracy with two examples per digit. These findings provide a promising step toward adaptive sEMG-based handwriting interfaces, although further validation is required for real-time and multi-subject deployment and neuromuscular control.

High performance intracortical brain-computer interface (iBCI) control has been demonstrated in research settings, but performance can still vary within and between sessions. One potential source of this variability is the change in attentional load that comes from processing naturally occurring distractors such as thoughts, sounds, fatigue, or pain. To improve the consistency of iBCI performance in real-world environments where this sort of multi-tasking is inevitable, we must understand how shifts in attention can impact performance. Here we examined the effect of attentional load on iBCI performance and movement-related neural activity using a 2D cursor translation + click iBCI task paired with an N-Back working memory task to increase attentional load during dual-task performance. Two participants (P2 and P4) with tetraplegia completed the study while enrolled in a long-term clinical trial of an iBCI device (NCT1894802). Common neural correlates of attention (theta and alpha band power) were measured with simultaneously recorded scalp electroencephalography (EEG). While the EEG recordings and difficulty ratings suggested increased attentional load during dual tasking, iBCI performance was quite robust across the various dual tasking conditions. One participant, P2, experienced a small but significant increase in trial completion time and normalized path length during the mild attentional load condition. Signal quality differences between the two participants may have impacted the results, as P2 had lower signal quality and was therefore likely more vulnerable to attentional load. P4s higher signal quality likely allowed him to accommodate increased attentional load without a drop in performance. Overall, iBCI performance appears to be robust to attentional load, but the complex trends observed here reflect a need for continued investigation of BCI use under different cognitive states to elucidate potential challenges and compensatory mechanisms across participants.

Physiological mirror activity (pMA) is the increase in involuntary muscle activity observed on the contralateral side during unilateral voluntary movement in neurologically healthy participants. This cross-sectional study aimed to explore the relationship between pMA and corticomuscular coherence (CMC) during finger dexterity tasks in young and older adults. Thirty-one right-handed young adults and 24 older adults performed a left-hand finger dexterity task. Electroencephalogram (EEG) signals were recorded from C3 and C4, and electromyogram (EMG) signals were collected from bilateral finger flexors and extensors. pMA was quantified as the change in right-hand EMG from rest to task. Gamma-band CMC was calculated from task-related EEG-EMG pairs, and its association with pMA was analyzed. In young adults, greater pMA was associated with lower CMC (C3- and C4-right flexors), whereas in older adults, greater pMA was associated with higher CMC (C3-left flexor). Young adults may suppress pMA emergence by appropriately monitoring and inhibiting activity, in the hand not performing the task. Conversely, in older adults, the mobilization of the ipsilateral motor cortex may have contributed to pMA emergence. This study suggests that the neuromuscular mechanisms involved in pMA during finger dexterity tasks differ between young and older adults.

PurposeAnkle stiffness can be altered by normal aging, bone and joint pathology, and treatments such as orthoses or surgical joint fusion. The effects of ankle stiffness on gait are not yet well understood but may be crucial for understanding how these pathologies and treatments influence body mechanics. The objective of this work was to investigate how isolated changes in stiffness applied in parallel with the ankle impact lower-limb kinematics, kinetics, joint work, and muscle activation during walking in individuals without lower-limb pathology. MethodsNine young adults without lower-limb pathology wore an adjustable-stiffness ankle exoskeleton and walked at 31 different conditions of ankle spring stiffness, neutral angle, and treadmill incline. We recorded motion capture data, ground reaction forces, and muscle activation, and analyzed the resultant data for trends as a function of ankle stiffness. ResultsExoskeleton-side ankle range of motion decreased and asymmetry increased across all joints as ankle stiffness increased, primarily due to decreased plantarflexion at toe-off. The 30 Nm/rad spring stiffness condition led to a minimum in mean exoskeleton-side muscle activation and hip joint work, but increased kinematic asymmetry. ConclusionOur results suggest that there may exist a range of stiffnesses at the lower end of typically-studied values that can reduce muscle activation and joint work during walking, though at the cost of kinematic symmetry. These findings provide a deeper understanding of how ankle stiffness influences gait mechanics, with potential applications in wearable devices, clinical rehabilitation, and assistive technology.

Static force plates (FP) are the gold standard for measuring ground reaction forces (GRF) and computing joint moments through inverse dynamics in gait analysis. However, they are restricted to controlled environments, and the number of steps analyzed is limited by the plates embedded in the floor. To address these limitations, portable solutions such as sensorized insoles, socks, or shoes have emerged. Yet, creating wearable systems capable of measuring three-dimensional GRF in real-world conditions remains challenging. Current sensorized shoes often incorporate thick sensors (up to 2 cm), reducing usability and limiting their application in pathological populations or dynamic tasks like running. This study evaluates the usability of ShokacShoes, a novel sensorized shoe integrating three thin, three-dimensional force sensors, and explores its potential as a Wearable Force Plate (WFP). Eight healthy participants performed slow, natural, and fast walking using two insole configurations. Force and temporal metrics were derived from WFP and FP data. Results indicate that WFP enables accurate step segmentation and detects significant effects of speed and insole type on temporal and force metrics, confirming its reliability under different walking conditions. Comparisons with FP revealed differences in force metrics and signal morphology, though temporal parameters remained consistent. These results are likely due to sensor quantity and positioning. Thereby, ShokacShoes represent a valid solution capable of measuring three-dimensional forces within commercial footwear. Future work will focus on validating the applicability of a new version of ShokacShoes against gold-standard FP in a comprehensive validation study involving diverse real-world scenarios and pathological conditions.

BackgroundBalance maintenance in humans is not only a mechanical process, but it also relies on continuous interactions between cortical activity and body dynamics. Alterations in postural sway are commonly observed in aging and stroke and are frequently used to assess balance impairment. However, similar balance deficits do not necessarily reflect similar underlying sensorimotor control mechanisms. Therefore, investigating brain-body interactions and their relationship to characteristics of postural behavior may provide deeper insights into the neural processes underlying balance dysfunction in these populations. ObjectiveTo determine whether brain-body coupling is associated with characteristics of postural behavior captured by the temporal organization of postural fluctuations beyond conventional magnitude-based measures of postural sway, and whether these relationships differ between stroke survivors, healthy older adults, and young adults. MethodsEEG and center-of-pressure (CoP) signals were recorded simultaneously in stroke survivors (n = 12), healthy older adults (n = 18), and young controls (n = 17) during quiet standing under 4 different manipulated sensory conditions. Sway-based corticokinematic coherence (CKC) as well as linear and nonlinear features (sample entropy, SE; fractal dimension, FD) of CoP were extracted. Linear mixed-effects model assessed associations between features and CKC, and model performance was compared using Akaike Information Criterion. Multidimensional state vectors were constructed from CKC, linear and nonlinear CoP features, and Euclidean distances between consecutive states in the standardized feature space were computed to quantify condition-dependent transitions in brain-body control organization. ResultsNonlinear features showed significant, group- and feature-dependent associations with CKC in the mediolateral direction, driven by significant SE and FD effects in the stroke group and an SE effect in the older group, while no significant associations were observed in the young group. Including nonlinear features in baseline models containing only linear CoP features significantly improved model fit. CKC alone showed low classification performance (AUC 50 to 65), whereas combining CKC with linear and nonlinear features improved group discrimination (AUC up to 0.86). State-space transition analysis revealed larger condition-dependent transitions in stroke participants compared with healthy older adults, particularly going from eyes open to eyes closed when standing on foam. ConclusionBrain-body coupling during standing may be understood more comprehensively by factoring in the temporal structure of fluctuations rather than their amplitude alone. These findings support the use of nonlinear dynamical features, combined with CKC, as potential markers of balance impairment.