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The human neuroanatomy for gait dysfunction remains unclear. We sought (1) to identify a brain circuit for gait impairment post stroke, (2) to identify a brain circuit for gait changes after subthalamic deep brain stimulation (DBS) for Parkinson's disease (PD), and (3) to test for convergence between these 2 circuits. This cross-sectional retrospective study used data from 4 independent datasets including 109 individuals with stroke and 125 PD patients who received subthalamic DBS. Gait impairment post-stroke was measured using the Combined Walking Index. Gait changes after DBS were measured using the gait subscore on the Unified Parkinson Disease Rating Scale part III. Connectivity between lesion locations or DBS sites and 2 a priori locomotor regions (pedunculopontine nucleus and cerebellar locomotor region) was computed using a large normative connectome. We repeated this analysis in a data-driven fashion to identify additional connections. Connectivity between lesion locations and a priori regions were associated with gait impairment post-stroke (p&#x2009;<&#x2009;0.05), along with connectivity to a distributed circuit of other brain regions. Connectivity between DBS sites and our a priori gait regions and connectivity between DBS sites and our lesion-based gait circuit were all associated with gait changes post DBS (p&#x2009;<&#x2009;10-3). Data-driven gait circuits derived from lesions and DBS showed similar topography and converged on a common brain circuit (spatial r&#x2009;=&#x2009;0.68, p&#x2009;=&#x2009;0.0063, 10,000 permutations). Lesion locations impairing gait and DBS sites modulating gait converge on a common brain circuit. ANN NEUROL 2026.

Parkinson's disease (PD) has a long prodromal phase characterised by non-motor symptoms and subtle motor dysfunction, including changes in gait. While early gait abnormalities have been described in high&#x2011;risk populations, less is known about how real&#x2011;world gait behaviour changes prior to diagnosis in the general population.&#xa0;To investigate whether digital gait biomarkers derived from wrist&#x2011;worn sensors are associated with future Parkinson's disease diagnosis.&#xa0;The study comprised 73,413 UK Biobank participants who wore a wrist-worn device for seven days. Seventeen digital gait biomarkers were derived using the Watch Walk algorithm. Participants were followed for up to 10 years through linked electronic health records, and associations with time to PD diagnosis were assessed using Cox regression models adjusted for age and sex.&#xa0;Of the 73,294 participants without PD at the accelerometry assessment, 314 were diagnosed with PD during follow-up. Compared with those who did not develop PD, those who did had lower daily step counts (6370 vs. 4043-5585 steps/day), with greater differences observed closer to diagnosis, as well as slower walking speeds, altered step regularity, and reduced arm swing at baseline (all p&#x2009;<&#x2009;0.001). Across groups stratified by time to PD onset, five gait measures (daily step count, maximum walking speed, step regularity, proportion of long walking bouts, and time spent walking with static arm positions) were consistently associated with subsequent PD diagnosis (all p&#x2009;<&#x2009;0.001).&#xa0;Differences in real&#x2011;world gait behaviour were observable years before PD diagnosis in this large population cohort. These findings suggest that digital gait biomarkers may help characterise early motor changes detectable up to 6.8 years before a clinical diagnosis of PD.

Functional recovery after total joint arthroplasty (TJA) is multifactorial. This study aimed to characterize the one-year recovery trajectory in elderly patients undergoing total hip arthroplasty (THA) or total knee arthroplasty (TKA) by integrating objective gait analysis, muscle strength assessment, and patient-reported outcome measures (PROMs). In this prospective cohort study, 84 patients (THA: 36, TKA: 48; age &#x2265; 70 years) with Kellgren-Lawrence (K-L) grades III or IV osteoarthritis were evaluated pre-operatively and at two weeks and one, three, six, and 12 months post-surgery. Spatiotemporal gait parameters were collected using foot-mounted inertial sensors. Isokinetic dynamometry measured hip abductor (THA) or quadriceps muscle (TKA) strength. The PROMs, including the Hip Disability and Osteoarthritis Outcome Score for Joint Replacement (HOOS-JR) and the Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS-JR), were collected at all time points. Correlation and regression analyses were performed to identify predictors of recovery. The TKA patients demonstrated significant and persistent gait asymmetry up to three months, which normalized by six months. The THA recovery was more symmetrical, with only transient asymmetry at one month. Pre-operative muscle strength was the strongest predictor of 12-month gait speed (&#x3b2; = 0.42, P < 0.001), while radiographic severity (K-L grade III versus IV) did not significantly predict functional gait recovery (P = 0.38). By 12 months, both groups approached normative values for gait and strength, with 94.0% of THA and 92.0% of TKA patients achieving the patient acceptable symptom state (PASS). Muscle strength, rather than radiographic severity, is the critical determinant of functional gait recovery after TJA in the elderly. Pre-operative muscle strength assessment should be incorporated into surgical planning to guide personalized post-operative rehabilitation strategies. Future prospective randomized trials incorporating pre-operative gait analysis and intervention arms are warranted to establish causal relationships.

Previous research has indicated that human gait can be quantified using three-dimensional signals, each of which corresponds to distinct locomotor modules. However, no study has thoroughly investigated their interactions and the combined influence they exert on gait in a holistic way. Three-dimensional ground reaction force (GRF) signals were recorded from 2,295 participants, including 211 healthy individuals and 2,084 patients with lower limb disorders- specifically those affecting the calcaneus, hip joint, knee joint, and ankle joint-for illustrative purposes. First, each dimensional GRF signal was decomposed into three distinct components: high frequency, medium-frequency, and low-frequency components. Subsequently, a gait network integrating these three frequency components of GRF signals across all three spatial dimensions was constructed. Disease and walking speed both reshape the topology of gait networks. Lower-limb disorders reduce the importance of anterior-posterior nodes by 3.5% and increase that of medio-lateral nodes by 3.7%, while uniformly decreasing the gait network's edge weight, global efficiency, link strength, and modularity by approximately 4-5% (p < 0.01). In healthy adults, faster walking selectively enhances the importance of anterior posterior nodes by 4.2% and reduces that of vertical nodes by 3.2%. It leaves the gait network's edge weight, global efficiency, and link strength unchanged but increases its modularity by 8.8% (p < 0.001). Traditionally, medicine and kinesiology have studied the human body by segmenting it into three independent dimensions. However, researching these dimensions in isolation is insufficient. Instead, it is imperative to establish interdisciplinary research networks and conceptualize human movement as a cohesive, integrated system. Our research transcends conventional gait analysis: it facilitates the discovery of novel mechanisms underlying human movement and holds substantial potential for broad applications across multiple fields.

The simultaneous performance of tasks relying on the same cognitive processes might lead to gait disturbances. The study aims to explore the distinct involvement of working memory and inhibition during gait within a dual-task using 3D motion analysis and wireless electroencephalography (EEG), addressing limitations of previous research on the removal of gait-related artifacts from EEG signals and the EEG results' generalizability. 30 healthy participants performed two cognitive tasks designed to engage inhibition (Go-NoGo) and working memory (N-Back). Data acquisition was performed using a 19-channel wireless EEG device and a 3D optoelectronic system. 51 EEG features, seven spatial-temporal and nine kinematic parameters (seven spatiotemporal metrics and Gait Variable Scores for nine lower-limb joint ranges of motion) were analyzed. Riemannian geometry-based methods were employed to enhance artifact removal from EEG signals and to improve the EEG results' generalizability across subjects. Gait analysis highlighted a predominant involvement of inhibition (p &#x2264; 0.048 for the most of spatial-temporal parameters and p &#x2264; 0.005 for GPS and GVS of Hip Flex-Extension among the kinematic parameters), whereas working memory engagement emerged mainly in response to higher cognitive demands (p = 0.037 for mostly spatial-temporal parameters and p &#x2264; 0.003 for GPS and GVS of Hip Flex-Extension among the kinetic parameters). Relative alpha desynchronization at F4 and at F3 emerged as candidate sensor-level EEG biomarkers of working memory- and inhibitory-related processes (p &#x2264; 5 &#xd7; 10 - 5 ) in the earlobe-reference configuration. A cross-domain mixed-effects analysis further showed that, among the tested EEG-gait associations, relative alpha power at F3 during the Go-NoGo task was significantly associated with stride length in the earlobe-reference configuration, suggesting that this sensor-level EEG feature may represent a candidate marker for monitoring the interaction between inhibitory control and gait adaptation during dual-task walking. This study supports EEG-based monitoring of cognitive processes during walking and aids the design of tailored cognitive rehabilitation for motor-impaired individuals.

Transcutaneous auricular vagus nerve stimulation (taVNS) is a promising non-invasive neuromodulation approach for addressing gait impairments in Parkinson's disease (PD). However, evidence regarding its effects on daily-life gait remains limited. We report a case of a 67-year-old woman with PD and freezing of gait (FOG), based on the hypothesis that taVNS may enhance gait automaticity under daily-life conditions. A single-case A-B-C design was used, consisting of baseline, sham stimulation, and taVNS phases. Gait performance was assessed using wearable accelerometry in both experimental and daily-life settings. While no significant changes were observed in gait parameters during experimental assessments, daily-life walking measures-step velocity, step length, variability, and asymmetry-showed improvements during the taVNS phase. Importantly, no falls or FOG episodes were observed during the taVNS phase. These findings support the feasibility of taVNS and provide preliminary support for its potential therapeutic role in enhancing gait automaticity in daily life. Further controlled studies are warranted.

Early differentiation of progressive supranuclear palsy from Parkinson's disease is challenging due to overlapping motor features, particularly in early disease stages where clinical misclassification is common. Wearable inertial sensors provide high-resolution gait data that may reveal disease-specific and clinically relevant signatures. To evaluate whether sensor-derived gait measures can distinguish progressive supranuclear palsy from Parkinson's disease using machine learning, and to compare performance between raw time-series signals and aggregate spatiotemporal variables, with emphasis on clinically interpretable gait biomarkers. In this retrospective study, 34 participants with progressive supranuclear palsy and 410 with Parkinson's disease completed an instrumented Timed-Up-and-Go while wearing six synchronized sensors. Two input modalities were analyzed: raw accelerometer, gyroscope, and magnetometer signals sampled at 128&#x202f;Hz; and 48 curated spatiotemporal features. Eight classifiers, including logistic regression, support vector machine, k-nearest neighbors, neural network, decision tree, random forest, extreme gradient boosting, and a baseline, were trained to classify progressive supranuclear palsy versus Parkinson's disease at the participant level using cross-validation procedures designed to prevent data leakage. The primary performance metric was macro-averaged F1-score. Shapley Additive Explanations were applied to aggregate-feature models to identify key discriminative gait features. Raw time-series models performed strongly, with gradient boosting achieving the highest macro-F1 (0.87), followed by random forest (0.86) and support vector machine (0.83). Aggregate models performed comparably, with gradient boosting reaching 0.88, followed by random forest (0.87) and support vector machine (0.84). Shapley analyses identified increased double-support time and mediolateral sway, and reduced stride length, gait speed, and trunk range of motion as the most distinctive gait impairments characterizing PSP relative to PD. Ensemble models reliably differentiated progressive supranuclear palsy from Parkinson's disease. Aggregate spatiotemporal models achieved performance comparable to raw signals while yielding clinically interpretable and physiologically meaningful gait biomarkers. These findings support the use of wearable sensor-based gait analysis combined with machine learning as a practical tool for improving differential diagnosis in movement disorder clinics.

We present a multimodal dataset1 for automated gait analysis in patients undergoing Total Knee Arthroplasty (TKA). The dataset combines quantitative motion capture data with expert clinical gait assessments in textual form. The dataset includes recordings from 23 patients during a standardized six-minute walking test (6MWT), a clinical protocol used to assess walking endurance and functional mobility. Of these patients, 15 were recorded both pre-operatively and six weeks post-operatively, while the remaining patients were recorded only pre-operatively. Motion data were acquired using a markerless SIMI Motion system and are provided as 3D keypoint trajectories and joint angle time series. In addition, parametric body representations based on the Skinned Multi-Person Linear (SMPL) model were obtained through a separate post-processing pipeline applied to the multi-view recordings. For each recording session, three to six clinical gait assessments were obtained through structured interviews with physiotherapists. These annotations describe gait characteristics, deviations, and compensatory mechanisms. The dataset enables research on linking biomechanical motion data with clinical descriptions, supporting applications such as automated gait assessment, anomaly detection, and analysis of early post-operative recovery.

Symptomatic management of hereditary spastic paraplegia (HSP) often involves assistive devices, yet neither the timing nor the choice of device is standardized, and their immediate impact on mobility in HSP remains poorly characterized. We aimed to quantify the immediate effects of classical walking aids in HSP, and to identify individualized responses. HSP patients and healthy controls performed a standardized 4&#x2009;&#xd7;&#x2009;10 meters walking test under three conditions: unaided, with bilateral walking sticks, and with a four-wheel walker. Mobile gait parameters were recorded for each condition, and participants rated their perceived walking adaptations. In the patient cohort (n&#x2009;=&#x2009;46), assistive devices significantly improved step length, while other gait parameters were unchanged or worsened. Subgroup analyses indicated that patients with less clinical impairment, but greater fear of falling, older age, and higher body weight derived particular benefit from the use of walking aids. Subjective ratings were generally positive, with most patients assessing the devices as more helpful than baseline. Perceived improvements largely aligned with objective gait changes. Classical assistive devices may yield measurable, immediate benefits in gait performance in HSP reflecting patient perceptions. Future studies should investigate longer familiarization periods to evaluate sustained functional adaptations and optimize device recommendations. Hereditary spastic paraplegia (HSP) is a rare neurological disorder with the main symptom of increasing difficulties walking. People with HSP often use assistive devices such as walking sticks or wheeled walkers to improve walking and prevent falls. Still, evidence is sparse on when these devices should be used and which type is useful for individual patients. In this study, we investigated the immediate effects of commonly used walking aids in people with HSP. We asked 46 patients and 30&#xa0;healthy volunteers to complete short walking tests under three conditions: without any aid, with walking sticks, and with a wheeled walker. Wearable sensors attached to the shoes were used to measure walking patterns. Our results show that both walking sticks and wheeled walkers increased step length in people with HSP, indicating improved walking. However, other parameters of the gait cycle were mainly unchanged. Patients reported overall positive experiences with both types of walking aids. Some patients seemed to benefit more than others. In particular, those with older age, less severe symptoms, greater fear of falling or higher body weight showed more improvement. These findings suggest that walking aids can provide immediate benefits for a subgroup of people with HSP, with large variability between individuals. Longer-term studies are needed in the future to better understand how to best tailor recommendations.

The prevalence of Alzheimer's disease and related dementias are increasing at an alarming rate, with projections estimating that by 2060, approximately 13.8 million adults aged 65 years and older in the U.S. will be affected by one or both. Among the many symptoms associated with cognitive decline, gait impairment is one that significantly affects functional independence and mobility. A systematic review was conducted to analyze 49 peer-reviewed studies using the Covidence systematic review software and adhering to PRISMA guidelines. The selected articles examined variables related to gait speed, and cognition. Participants were assessed through validated neurocognitive and mobility measures, including the MoCA and Dynamic Gait Index. A significant negative correlation was identified between usual walking speed and age. This trend was particularly pronounced in women, in whom a significant negative association between MoCA scores and age (P = -0.019) was observed, suggesting an increased susceptibility to cognitive deterioration with advancing age. These findings underline the sex-specific nature of the relationship between gait speed and cognitive function, highlighting increased vulnerability in aging women. The decline in mobility and cognition observed in this population underscores the urgency of developing targeted interventions that integrate physical and cognitive rehabilitation strategies.

Self-paced treadmills allow individuals to continuously regulate belt speed and may more closely replicate overground locomotion than fixed-speed conditions. However, their effects on gait variability and stability, particularly during running, remain incompletely understood. This study compared self-paced (SFP) and constant-speed (CON) treadmill locomotion during walking and running at matched average speeds. Twenty-eight healthy adults completed SFP and CON walking and running trials. Spatiotemporal characteristics were assessed using mean values and coefficients of variation (CV) for stride length, step frequency, step width, and stance ratio (duty factor in running). Global stability was evaluated using detrended fluctuation analysis (DFA), and local dynamic stability using the maximum Lyapunov exponent. During walking, SFP slightly increased stride length (p&#xa0;=&#xa0;0.004) and reduced stance ratio (p&#xa0;=&#xa0;0.025), while step width and step frequency were unchanged. CV increased for stride length (p&#xa0;<&#xa0;0.001), step frequency (p&#xa0;<&#xa0;0.001), and stance ratio (p&#xa0;<&#xa0;0.001), whereas step width variability remained unchanged. DFA scaling exponent was increased during SFP walking (p&#xa0;<&#xa0;0.001). Local dynamic stability decreased in the thigh (p&#xa0;<&#xa0;0.001) and foot (p&#xa0;<&#xa0;0.001). In running, no significant differences were observed between SFP and CON for mean spatiotemporal variables, their variability, or global stability. Local dynamic stability improved in the trunk (p&#xa0;=&#xa0;0.036), shank (p&#xa0;<&#xa0;0.001), and foot (p&#xa0;=&#xa0;0.002). These findings demonstrate gait-mode-specific responses to self-paced treadmill speed regulation. Walking appears more sensitive to externally imposed speed constraints, whereas running preserves its spatiotemporal characteristics and dynamical organization.

Accurate gait recognition using wearable sensors is of significant clinical value for adaptive prosthetic control, lower-limb exoskeleton assistance, and objective rehabilitation assessment. However, subject-independent recognition remains a major challenge, as unseen individuals can exhibit highly variable limb kinematics and muscle activation patterns, and existing approaches often rely on a single sensor modality or naive fusion strategies that fail to leverage the complementary information between inertial and electromyographic signals. To address these gaps, this study proposes DSAF, a dual-stream attention fusion network that separately encodes kinematic (IMU) and neuromuscular (EMG) information, and adaptively integrates them through a physiological complementarity weighting mechanism designed for window-level modality adaptation. The framework is evaluated on the public HuGaDB dataset for eight common locomotion activities (e.g., walking, running, stair negotiation, and sit-to-stand transitions), using a leave-one-subject-out protocol to rigorously assess generalization to new users. DSAF achieves 96.41% accuracy, 96.08% macro-precision, 95.62% macro-recall, and 95.81% macro-F1, consistently outperforming recent sequence-learning baselines across all 18 held-out subjects. Ablation studies further confirm that both the modality-specific encoding and the adaptive fusion mechanism contribute positively to the performance. These findings indicate that adaptive IMU-EMG fusion can effectively strengthen wearable gait recognition, providing a promising solution for real-world rehabilitation monitoring and assistive human-machine interfaces.

Gait speed is a key clinical indicator in neurological and orthopaedic conditions, yet waveform-level adaptations in ground reaction forces (GRF) and multi-muscle electromyography (EMG) remain poorly characterised. Existing approaches often analyse discrete outcomes or individual modalities, leaving limited integration of continuous waveform inference, dimensionality reduction, explainable machine learning, and equivalence testing within a unified multimodal framework. To compare three-axis GRF and six-muscle EMG between slow (0.5&#xa0;m/s) and fast (1.0&#xa0;m/s) treadmill walking using statistical parametric mapping (SPM), functional principal component analysis (fPCA), explainable machine learning, and equivalence testing. Fifty-eight healthy adults were analysed (55 with complete EMG). Paired SPM with cluster-based permutation assessed waveform differences. fPCA-derived features entered a Random Forest with leave-one-subject-out cross-validation and SHAP interpretability. Two one-sided tests (TOST) assessed equivalence of the vertical GRF. No significant cluster-level SPM differences were found for any GRF component. In contrast, significant EMG clusters were detected in tibialis anterior (ten clusters), gastrocnemius medial and lateral, vastus lateralis, rectus femoris, and semitendinosus. The Random Forest achieved 87.2% accuracy (95% CI: 79.3-92.3%), improving 14.7% points over simple amplitude features, with tibialis anterior PC1 the most important predictor. TOST did not confirm equivalence within &#xb1;&#x2009;0.2&#xa0;N/kg, though no GRF clusters appeared. Moderate speed increases elicited distributed multi-muscle activation changes, whereas GRF waveform differences did not reach cluster-level significance within the present analytical framework. The integrated SPM-fPCA-SHAP-TOST pipeline provides an interpretable signal-processing framework for multimodal gait analysis and could serve as a foundation for future investigations in rehabilitation engineering, digital biomarkers, and wearable sensing, pending validation in clinical populations.

Stroke rehabilitation aims to improve functional independence, ambulation, motor recovery, and participation in daily life. Robot-assisted gait therapy (RAGT) is increasingly used as an adjunct to conventional rehabilitation; however, its additional contribution to functional outcomes and kinesiophobia remains uncertain. This study evaluated changes in activities of daily living, ambulation, motor recovery, and kinesiophobia in patients undergoing inpatient post-stroke rehabilitation and compared routine multidisciplinary inpatient rehabilitation with the same rehabilitation approach plus additional RAGT. In this non-randomized observational comparative study, 99 post-stroke patients completed a 4-week inpatient rehabilitation program. Patients receiving routine multidisciplinary inpatient rehabilitation, including a standardized physiotherapy program, were classified as the CRTx group, whereas those receiving the same rehabilitation approach with additional RAGT were classified as the CRTx+RAGT group. Functional independence was assessed using the Barthel Index, which was defined as the primary outcome. Secondary outcomes included Functional Ambulation Classification (FAC), Brunnstrom stages, and Tampa Scale for Kinesiophobia (TSK). Assessments were performed at baseline and post-treatment. Within-group changes, between-group change scores, and adjusted linear regression models were analyzed. Both groups showed significant within-group improvements in Brunnstrom stages, Barthel Index, FAC, and TSK scores after rehabilitation. In unadjusted change-score comparisons, improvements in the Barthel Index and FAC were greater in the CRTx+RAGT group, whereas changes in Brunnstrom stages and TSK scores did not differ significantly between groups. However, after adjustment for baseline score, stroke duration, education level, and baseline Brunnstrom lower-extremity stage, rehabilitation group was not independently associated with post-treatment Barthel Index, FAC, or TSK scores. A 4-week inpatient rehabilitation program was associated with improvements in motor recovery, functional independence, ambulation, and kinesiophobia in post-stroke patients. Although greater unadjusted improvements in Barthel Index and FAC were observed in the CRTx+RAGT group, these differences were not confirmed in adjusted analyses. The independent contribution of RAGT should therefore be interpreted cautiously and investigated further in randomized, adequately powered, and dose-matched studies.

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