IEEE Transactions on Neural Systems and Rehabilitation Engineering

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.

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.

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.

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.

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.

Breast cancer survivors frequently experience upper-limb impairments, making continuous monitoring essential for effective rehabilitation. We propose REINA (Recognize-Then-Infer Wearable-to-App AI Framework), a two-stage deep-learning approach for remote monitoring of motor function during breast cancer rehabilitation using wearable-device data. Inertial measurement unit (IMU) signals from wearable devices are first used to recognize physical activities via supervised learning, followed by an activity-specific recurrent neural network (RNN) to infer corresponding electromyography (EMG) signals. REINA establishes reliable inference of neuromuscular activity from wearable IMU data, enabling real-time, cost-effective assessment of motor function recovery in real-world settings.

Surface electromyography (sEMG) offers a silent and wearable input modality, but its practical use is limited by variability across users and recording sessions. This study presents a compact CNN- Transformer model for decoding isolated handwritten digits from eight-channel sEMG signals. The model combines trainable signal preprocessing, convolutional feature extraction, and Transformerbased temporal modeling. It was evaluated on ten recordings from five participants using recordingseen classification, leave-one-recording-out (LORO) generalization, and few-shot adaptation. The model achieved a mean macro F1 score of 0.924 {+/-} 0.059 in the recording-seen setting and 0.619 {+/-} 0.252 under zero-shot LORO evaluation. Adaptation using two labeled trials per digit increased macro F1 to 0.828 {+/-} 0.112, while ten trials per digit achieved 0.925 {+/-} 0.053. The proposed architecture also outperformed classical and neural baselines in the controlled LORO benchmark. These results indicate that compact CNN-Transformer models, combined with lightweight target-recording calibration, provide a promising basis for adaptive sEMG-based input systems.

Pathological tremor is a neurological condition that impairs fine motor tasks, affecting 1% of the general population and 4% of the elderly. Tremors arise when muscles micro-oscillations synchronize and phase lock, typically within a 4-12 Hz frequency range. Administering beta-blockers can reduce tremor severity, but doses are hard to personalize, with heavy doses of propranolol correlating with low blood pressure, dizziness, and nausea. In this project, we aimed to model tremor and create a closed-loop control framework to suppress tremor amplitude while minimizing pharmacological dependence. Because side effects constrain the use of pharmacological suppression alone, we investigated noninvasive neuromodulation. We used vibrotactile stimulation (VTS) to disrupt pathological tremor synchronization and reduce oscillatory amplitude. We hypothesized that tremor suppression involving VTS followed a nonmonotonic relationship, tested by determining whether maximum relief requires an adaptable framework. The procedure consisted of constructing a propranolol-reduction simulation by implementing a Hill curve, where we calculated and utilized tremor reduction, heart rate (HR) drop, and blood pressure (BP) drop. We then built a device to capture tremor-related data and create vibration using two linear resonant actuator (LRA) coin motors. We connected it to a microcontroller, where we determined optimal vibration frequencies through a feedback loop. Across 50 trials, VTS alone reduced tremor amplitude by an average of 37.3%, reducing the propranolol dose needed to reach 50% total tremor reduction by 71.9%, lowering the modeled blood pressure drop from 38.1 to 18.9 mmHg. This device demonstrates proof-of-concept for a nonmonotonic tremor-vibration relationship to reduce dependency on propranolol in the treatment of pathological tremor. These propranolol dose-reduction estimates are derived from computational simulation and have not been clinically validated; they are not intended as a recommendation to alter prescribed medication.

Beta oscillations (13-30 Hz) are a prominent sensorimotor neural rhythm and an important biomarker in neurorehabilitation. These oscillations propagate along the corticospinal pathway and are expressed in the discharge patterns of spinal motor neurons, enabling the assessment of corticomuscular coupling. Moreover, peripheral beta band oscillations have recently emerged as a potential control signal for motor augmentation interfaces. However, it remains unclear whether peripheral beta activity simply reflects cortical oscillations or is partly shaped by peripheral mechanisms, and to what extent it can be voluntarily controlled. To address these questions, we developed a 10-day neurofeedback protocol in which participants learned to up-regulate peripheral beta band activity. Subjects were trained to exploit movement cancellation, a behaviour naturally associated with increased cortical and muscle beta band activity, as a two-state strategy to voluntarily modulate peripheral beta band power. Each session included a guided familiarization phase based on a GO/NO-GO task, in which participants familiarized with movement cancellation through guided visual cues, followed by an asynchronous control phase in which they self-initiated the same strategy without external guidance to increase peripheral beta band activity in a target window. Participants progressively improved their ability to voluntarily modulate peripheral beta band activity. Peripheral beta band power during movement cancellation increased significantly across training days in both the familiarization and asynchronous control phases. Intramuscular coherence in the beta band also increased, indicating enhanced common synaptic input to the motor neuron pool in this band. In contrast, cortical beta power and corticomuscular coherence remained unchanged. Together, these findings demonstrate that peripheral beta band activity is a dynamic neural feature that can be voluntarily shaped through training, supporting its potential as a non-invasive control signal for future neurorehabilitation and motor augmentation technologies.

EEG microstates are a distinct number of quasi-stable spatial distributions of brain activity. Microstate trajectories are strongly suspected to reflect the underlying neural mechanisms during information processing and are therefore also called the "building blocks" of human thought. In this study, we examined, if EEG microstate sequences can serve as potential triggers for a Brain-Computer Interface (BCI). To this end, a semi-supervised deep learning model architecture consisting of an LSTM-based autoencoder and a dense neural network was utilized to classify between left- and right-hand motor imagery EEG data, with the resulting classification output serving as the BCI trigger. On the one hand, this was done in a 2-step approach, in which the autoencoder and classifer have been trained separately. On the other hand, an end-to-end approach was employed, where training was performed by combining reconstruction and classification losses. Results show that the proposed model architecture was able to extract relevant features from microstate sequences and exploit them for within subjects and sessions classification. Applying transfer learning to session-to-session or across-subject transfer resulted in peak classification accuracies around 89%. We also investigated to what extent transfer learning has to be applied to reach considerable classification accuracies serving as the calibration time representative. We found that on average around 400s are needed for BCI calibration when emplyoing our approach to reach 80% classification accuracy. The present study signifies that the investigation of EEG microstate trajectories can be a promising approach for extracting BCI triggers, as it reduces the dimensionality of multi-channel recorded EEG signals to a distinct number of brain states over time. Deep learning methods, especially transfer learning, applied to EEG microstate trajectories seem promising regarding user-convenient and calibration-free BCIs in real-world applications.

Proprioception can be assessed in several ways, including movement detection, joint position matching, and matching across sensory frames of reference. These task types make different demands, yet they are rarely compared within the same participants on the same device, and psychometric data for wrist-focused batteries are limited. This work had two aims: to compare performance across different levels of proprioceptive judgment, and to establish the within-day test-retest reliability of each. We evaluated three robotic wrist tasks spanning judgments within a single reference frame and across reference frames: joint detection threshold (JDT), same-frame joint-to-joint matching (J-to-J), and cross-frame joint-to-visual matching (J-to-V). Methods. Twenty neurotypical adults completed two identical sessions on the same day, separated by at least two hours, using a single-degree-of-freedom wrist robot. Outcomes were the kinematic detection threshold (degrees) for JDT and the mean absolute matching error (degrees) for J-to-J and J-to-V. Relative reliability was quantified with ICC (2,1) and 95% confidence intervals. Absolute reliability was quantified with the standard error of measurement (SEM) and the smallest detectable change at 95% confidence (SDC 95). Learning effects and differences across task levels were evaluated with paired t-tests or Wilcoxon signed-rank tests. Results. ICC (2,1) was 0.959 [95% CI: 0.900 to 0.980] for JDT, 0.837 [0.640 to 0.930] for J-to-J, and 0.769 [0.500 to 0.900] for J-to-V. The %SEM ranged from 11.9% (J-to-J) to 15.6% (J-to-V). SDC95 was 0.85, 1.71, and 3.54 degrees for JDT, J-to-J, and J-to-V, respectively. A small but significant practice effect was observed for JDT, but this was below the SDC95, and no learning effect was observed for J-to-J or J-to-V. We also observed that the absolute error increased monotonically across task levels, with all pairwise comparisons (JDT < J-to-J < J-to-V; all p < 0.01). Conclusions. All three tasks demonstrated good-to-excellent within-day relative reliability. Error scaled with the computational demand of each task, with the largest errors observed for the cross-frame task, which required a transformation between the visual and joint reference frames. The reported SDC95 values provide task-specific thresholds for distinguishing measurement noise from true change in future intervention studies. Inter-day reliability and validation in clinical populations are the next steps.

Conventional transfemoral amputation disrupts native neuromuscular pathways, limiting prosthetic joint control, sensory feedback, and the perception of the prosthesis as part of the body. To ameliorate these pathologies, we restored the agonist-antagonist relationship of residual muscles in two individuals with above-knee amputation through an interventional surgical revision. Participants trained with a bionic knee prosthesis before and after the surgical revision while generating neuromuscular, cortical, functional, and affective data. Both individuals demonstrated improvements after the revision that could not readily be attributed to training effects, including: 1) increased proprioceptive afferents and stronger activation in cortical regions associated with sensorimotor integration of their missing joints, 2) improved control of the bionic knee during functional tasks including sit-to-stand and stair ascent, and 3) generally greater prosthesis embodiment, proprioception, and phantom limb definition as assessed through questionnaires and interviews. In contrast, training outcomes were more participant-specific and more variably correlated with amount of exposure, especially before the revision. These pilot findings suggest that revisional augmentation of residual neuromuscular tissues to restore agonist-antagonist dynamics may promote sensorimotor coherence and enhance both functional and perceptual integration with a bionic prosthesis, and remaining participant-specific heterogeneities may be attributable to inter-individual difference in residual limbs neuromuscular system, amputation history, and personal beliefs about prosthesis usage.

Physical human-device interaction is central to many emerging technologies in neurorehabilitation and assistive robotics, but simulation-based research in this area remains fragmented across musculoskeletal models, assistive-device representations, task definitions, and controller-development workflows. This fragmentation limits the accessibility, reproducibility, and extensibility of studies on prostheses, exoskeletons, wearable rehabilitation devices, and related human-device systems. Here we introduce MyoAssist 1.0, an open-source framework for neuromechanical simulation of physical human-device interaction built within the MyoSuite ecosystem. MyoAssist organizes each simulation environment as a composed human-device-task system that combines compatible musculoskeletal, assistive-device, and task-scenario components through a shared composition pipeline. The current release includes 15 assistive-device models spanning gait assistance, upper-body support, manipulation, and seated mobility and supports compatible musculoskeletal models ranging from reduced lower-limb models to a 416-muscle full-body model. These human-device systems can be simulated within the broad task scenarios provided by MyoSuite, while MyoAssist adds locomotion-specific task scenarios with configurable terrain and target-velocity conditions for gait-assistive studies. MyoAssist also provides two complementary controller-development frameworks: a reinforcement-learning framework for training adaptive policies and a controller-optimization framework for tuning structured, interpretable human and device controllers. Both frameworks operate on the same simulation environments and provide standardized evaluation outputs for inspecting, comparing, reusing, and extending learned and structured control strategies. By integrating modular human models, assistive-device models, task scenarios, and training workflows under a shared open-source interface, MyoAssist aims to lower the barrier to reproducible simulation-based research and to support collaborative development of assistive technologies for neurorehabilitation and physical human-device interaction.

Impaired motor control post-stroke results in reduced walking speeds and increased gait variability. This variability reduces reliability and makes identifying longitudinal changes via gait analysis difficult since changes may occur within the margin of measurement error. We quantified intra-class correlation coefficients (ICC) and minimal detectable change (MDC) in post-stroke individuals and neurotypical individuals walking at matched speeds, to isolate the impact of gait speed and post-stroke impairments on gait-analysis reliability. We collected gait data over two days from N=15 post-stroke individuals walking on a treadmill at their self-selected speed, and from N=13 age- and sex-matched neurotypical controls walking at both their self-selected speed and a speed matched to a post-stroke participant. We calculated ICC and MDC values for spatiotemporal variables, bilateral joint ranges of motion (ROM), and bilateral peak propulsive and peak vertical ground reaction forces (GRF). Spatiotemporal ICCs showed excellent reliability across groups (range [0.813-0.988]), yet MDC values were greater post-stroke than in speed-matched controls. ICCs for joint ROM ranged from poor to excellent reliability across groups ([0.362-0.960]). Post-stroke joint ROM MDCs were 27%-53% of the gait ROM compared to 11%-42% in neurotypical controls. ROM MDCs were greater in the non-paretic compared to the paretic extremity. ICC for peak GRFs showed good to excellent reliability across groups (range [0.778-0.980]), with post-stroke peak GRF MDCs greater than in speed-matched controls. Our results suggest that stroke related neuromotor impairments influence reliability beyond the effects of walking speed alone, and we provide quantitative MDC benchmarks for interpreting gait changes post stroke following clinical interventions.

AO_SCPLOWBSTRACTC_SCPLOWElectroencephalography (EEG) is a promising tool for automated detection of mild cognitive impairment (MCI) and dementia, but comparisons across studies are limited by inconsistent datasets and evaluation protocols. This study benchmarks ten deep learning models across four resting-state EEG datasets and eight binary classification tasks using a unified preprocessing pipeline and five-fold subject-wise cross-validation. Each experiment was repeated ten times. SCCNet obtained the highest mean subject-level accuracy, sensitivity, and F1 score, while ShallowConvNet achieved the highest mean segment-level accuracy, specificity, and precision. Subject-level aggregation improved mean accuracy for all evaluated models, and performance varied substantially across datasets and diagnostic tasks. Higher computational cost did not consistently correspond to better classification performance, with several compact architectures remaining competitive with substantially larger models. The results provide a reproducible reference for comparing EEG-based dementia classification models under consistent subject-independent evaluation conditions.

Importance: There are very few interventions designed for individuals with the most severe mobility impairments. Robotic walking may be an effective way to facilitate exercise in this population. Objective: To examine how robot-assisted walkers physical parameters and user characteristics moderate the exercise intensity achieved by individuals with neuromotor disorders causing mobility impairments. Design: A prospective study. Intervention: A single-session intervention involving an overground robot-assisted walker that can be used in an endurance mode requiring no voluntary movement or a strength mode during which voluntary movement could impact the gait pattern. Participants: Individuals with pediatric-onset mobility impairments Main Outcome Measures: Participants were characterized based on their age, sex, diagnosis, and Gilette Functional Assessment Questionnaire (FAQ) levels. Heart rate during the final minute of four 5-minute walking conditions: strength mode at fast speed, strength mode at slow speed, endurance mode at fast speed and endurance mode at slow speed was expressed as a percentage of each participant heart rate reserve (%HRR). Linear mixed-effects models were used to evaluate the impact of speed, device mode and user characteristics on the level of exercise achieved. Results: 29 individuals (aged 2-26 years) with mobility impairments (FAQ levels 1-6) completed this study. Fast speeds were associated with a higher %HRR (beta= 2.11, SE = 1.03, p = 0.044). Participants in FAQ class 1 exhibited significantly higher %HRR compared with those in FAQ classes 2 and 3 (beta=18.6, SE=7.31, p=0.017; beta= 16.9, SE = 8.13, p = 0.047, respectively). No other device or participant characteristics were associated with exercise intensity. Conclusions: To facilitate higher exercise levels, users of robot-assisted walkers can increase their speed. Individuals who cannot take steps due to their neuromotor impairments experience the highest levels of exercise. Relevance: The findings in this study highlight the promise of robot-assisted walkers to improve health, particularly in those who often face the greatest barriers to exercise.

Generalizable movement decoding remains a central challenge for invasive brain--computer interfaces (BCIs), as decoders trained under limited calibration conditions often fail to generalize to unseen movement speeds, limbs, and subjects. Existing decoding methods are typically trained on paired data collected under restricted conditions. How to incorporate behavioral structure from unpaired data for robust out-of-distribution (OOD) decoding therefore remains unresolved. To address this, we propose LAND (Latent Aligned Neural-behavioral Dynamics), a framework that aligns latent neural and behavioral dynamics through flow matching. By learning a neural-to-behavioral transport map and using behavioral-dynamics priors from unpaired data to encourage structured neural manifolds, LAND regularizes representation geometry to promote cross-domain generalization. We evaluate LAND on synthetic neural data, epidural BCI recordings from a tetraplegia participant, and multi-electrode array (MEA) recordings from nonhuman primates (NHPs). Across these settings, LAND improves zero-shot generalization to OOD movement speeds and yields speed-modulated manifolds. With limited target-domain fine-tuning, it further improves transfer across limbs and subjects. These results support flow-based neural--behavioral alignment with unpaired kinematic priors as an approach for learning transferable neural representations and robust movement decoding across behavioral and recording domains.

Background Clinical assessments of Amyotrophic Lateral Sclerosis (ALS) are typically collected infrequently in clinic visits and may not fully capture domain-specific functional decline in daily life. Digital Health Technologies (DHTs) can support remote monitoring, but passive free-living measures often require prolonged wear time and may be influenced by non-motor factors. This study evaluated whether short, standardized, at-home lower limb exercises recorded with ankle-worn accelerometers provide objective and interpretable measures of lower limb disease progression in ALS. Methods We analyzed data from 349 participants with ALS enrolled in the decentralized ALS Research Collaborative Study. Participants completed repeated self-entry ALS Functional Rating Scale-Revised (ALSFRS-RSE) assessments and wore bilateral ankle accelerometers during monitoring periods between September 2014 and January 2023. During each period, participants performed brief seated knee flexion-extension exercises at home. A previously developed signal processing pipeline was used to derive four exercise metrics: count, duration, intensity, and similarity. We examined baseline correlations with ALSFRS-RSE total and subdomain scores, longitudinal change using linear mixed-effects models, associations with gross motor item scores, differences by anatomical site of disease onset, and comparisons with free-living gait metrics. Results At baseline, exercise-derived metrics, particularly intensity and similarity, showed the strongest associations with the gross motor subdomain. Longitudinally, duration increased while intensity and similarity decreased, consistent with progressive slowing, reduced movement vigor, and reduced movement consistency (all p < 0.001); count did not change significantly. Worsening responses to gross motor items related to turning in bed, walking, and stair climbing were consistently associated with fewer, slower, less vigorous, and less consistent lower limb repetitions. Baseline intensity and similarity were lower in participants with lower limb disease onset on the corresponding side. Exercise-derived intensity showed model fit comparable to the strongest free-living gait metrics, while requiring substantially less observation time. Conclusions Short at-home lower limb exercises recorded using ankle-worn accelerometers provide scalable, objective, and interpretable measures of amyotrophic lateral sclerosis-related functional decline. Movement quality metrics, particularly intensity and similarity, may complement passive free-living monitoring and support remote digital clinical outcome assessment in ALS research. Trial registration NCT06885918.

Purpose: To explore how 12-weeks of robotic walking impacts physical function and sequelae of inactivity for individuals with pediatric-onset neuromotor impairments. Methods: A single-arm mixed-methods interventional study examined robotic walking for 12-weeks in home and community settings, with 12-week follow-up. Outcomes included family goals (Goal Attainment Scale (GAS)) and perspectives (Interviews), postural control (Early Clinical Assessment of Balance), physical activity (Actigraphy, Habitual Activity Estimation Scale, Patient Reported Outcome Measurement Information System (PROMIS) Physical Activity) and sequelae of inactivity (PROMIS Sleep Disturbances, Bowel Function Diary). GAS was collected pre-training, post-training, and 12-week follow-up. All other quantitative outcomes were collected every 4-weeks. Quantitative data are described with median (25th-75thpercentile) and analyzed using a Skillings-Mack test with post-hoc Wilcoxon Signed-Rank. Qualitative interviews were conducted before and after training and analyzed thematically. Results: 15 participants aged 4-23 completed this study. Participants had cerebral palsy (10/15) or rare genetic conditions (5/15), and most used a wheelchair in community settings. Postural control improved (test-statistic = 23.0, p<0.001) after 8 weeks (change=5.0(0.0-21.4), p=0.016) and was maintained through 12-week follow-up (change=13.7(3.1-23.7), p=0.008). Over half of the participants achieved goals (t-score > 50) after training. Exploratory analyses suggest improvements in sleep disturbance immediately after training (p=0.025) and 4-weeks after (p=0.047). All measures of physical activity did not improve. Parents reported improvements in walking, activities of daily living, and sequelae of inactivity (i.e., bowel function, appetite, and sleep). Conclusions: Improvements were seen across a range of measures and notably postural control improvements were maintained at the follow-up. Parents perceived improvements in physical function and activities of daily living. Future research is warranted to further understand the impacts of robotic walking for children and small adults with mobility impairments.

Transcranial magnetic stimulation (TMS) excites neurons noninvasively by electromagnetic induction and is used in neurophysiology research and in approved therapy for depression. Commercial stimulators cost tens of thousands of dollars. Existing open-source designs are either low-energy and unvalidated or rely on expensive switches and laboratory infrastructure. We present a monophasic, fixed-pulse-shape TMS device built at a parts cost of ~USD 700 which, under specific modeling assumptions, can exceed average human motor thresholds. Our design assumes access to basic, off-the-shelf equipment such as a 24 V power supply unit, an oscilloscope, and a few basic tools. The device charges a 230 F film-capacitor bank and discharges it through a self-wound figure-of-eight coil using a thyristor, producing a fixed pulse with a positive lobe lasting approximately 90 s. A Zero-Voltage Switching (ZVS) driver-based charging circuit charges the capacitor bank up to 1460 V from a 24 V bench supply. Three galvanically isolated voltage domains, redundant interlocks, and passive and active discharge paths help mitigate the safety risks involved with handling lethal energy levels. We also present a low-cost way to characterize the device by reconstructing coil di/dt from pickup-coil dB/dt maps to estimate the induced cortical E-fields. At the maximum capacitor voltage, the recovered maximal di/dt is 110.86 A/s, giving estimated 99.9th percentile cortical E-fields of 159 V/m at Oz and 196 V/m at C3 on an example anatomy. Although not yet approved for clinical trials and routine stimulation, the device demonstrated the possibility of a cost-effective TMS unit.