Search Results (237)

Background: Exercise rehabilitation (ER) has become an important treatment regimen during the recovery phase of acute ischemic stroke (AIS). Optimal exercise prescriptions for post-stroke motor recovery remain controversial. Methods: PubMed, Embase, the Cochrane Library, and Web of Science were systematically searched through November 2025. The Cochrane risk-of-bias (RoB 1) assessment tool was utilized to assess the risk of bias in the original studies. Results: This study included 66 trials comprising 3675 patients. For balance, whole-body tilting postural training (WTPT), unilateral strength training (UST) and robot-assisted unilateral gait training (RAUGT) showed high SUCRA rankings. Activities of daily living likely improved most with robot-assisted Tai Chi training (RATCT), home rehabilitation guidance (HRG) and robot-assisted gait training (RAGT). Fugl-Meyer Assessment (FMA) gains appeared greatest following robot-assisted hand training with electrical stimulation (RAHT + ES), neural inhibition therapy (NIT) and graded motor imagery with electrical stimulation (GMI + ES). Walking endurance seemed most responsive to visual feedback training (VFT), UST and visual aids training (VAT), while functional mobility showed greatest improvement with tilt sensor-assisted gait training with electrical stimulation (TAGT + ES), gait training (GT) and virtual reality therapy with motor imagery (VRT + MI). Conclusions: This study provides the first comprehensive evidence synthesis evaluating the comparative effectiveness of specific exercise modalities across distinct recovery domains after AIS. Rather than supporting generic interventions, the findings highlight the distinct comparative advantages of various emerging technologies and traditional practices. These results offer an evidence-based framework for clinicians to optimize post-stroke rehabilitation protocols and highlight priority areas for future research on dose–response and long-term outcomes. Full article

Background: Falls in older adults are a major public health and rehabilitation concern and may reflect interacting chronic disease-related mechanisms rather than diagnostic labels alone. Type 2 diabetes mellitus (T2DM), hypertension (HTN), and osteoarthritis (OA) are common in later life and may influence fall vulnerability through sensory, vascular, musculoskeletal, cognitive, medication-related, and functional pathways. This scoping review aimed to map mechanism-oriented evidence linking T2DM, HTN, and OA to falls and fall-related outcomes in older adults. Methods: A scoping review was conducted in accordance with established scoping review methodology and PRISMA-ScR guidance. PubMed, Web of Science, and Embase were searched for English-language studies published from 2016 to 2026; the final search was conducted on 15 April 2026. Evidence was charted in a structured table and synthesised descriptively and thematically across disease-specific and shared mechanism domains. Results: A total of 151 studies were included. The evidence was organised into T2DM-related sensory-neural, gait and balance, vestibular/visual-cognitive, strength-related, and glycaemic or medication-related mechanisms; HTN-related haemodynamic, cerebral perfusion, dizziness/syncope, and medication-related mechanisms; and OA-related pain, proprioceptive, strength-related, gait and balance, psychological, and compensatory-control mechanisms. Shared pathways included frailty, multimorbidity, polypharmacy, pain burden, cognitive impairment, sleep disturbance, ADL decline, dizziness, and reduced physical function. Conclusions: Falls in older adults with T2DM, HTN, OA, or multimorbidity may be better understood through interacting disease-specific and shared mechanisms than through diagnostic labels alone. Future longitudinal and multimorbidity-specific studies using standardised fall ascertainment are needed to test these pathways more directly. Full article

Human gait datasets play a central role in the development and evaluation of computer vision models. However, the current dataset landscape remains highly heterogeneous, with inconsistent reporting of acquisition conditions, user variability, and sensing configurations, which limits reproducibility and hinders principled cross-dataset comparability. In this work, we propose a covariate-centered, modality-agnostic taxonomy for gait datasets, explicitly structuring variability across scene-level, user-level, and sensor-level factors. The proposed framework enables consistent characterization of datasets through a standardized set of covariates (A–R), bridging differences across application domains and sensing modalities. Following a systematic review protocol aligned with PRISMA 2020, we analyze 47 publicly available image- and depth-based human gait datasets spanning healthcare, biometric, and attribute-recognition application domains. Using the proposed taxonomy, we derive a quantitative analysis of covariate coverage, revealing systematic biases in current dataset design. Full article

Background/Objectives: Commercial wearables are increasingly used after total hip and knee arthroplasty (THA/TKA) to record activity and gait outside the clinic and may help tailor recovery pathways. We reviewed the evidence on commercial devices, digital phenotyping, and data-informed rehabilitation after arthroplasty. Methods: We performed a structured narrative search of PubMed and Google Scholar. Of 916 records, 63 duplicates were removed, 853 were screened, 245 full texts were assessed, and 76 publications were included. Findings were synthesized thematically; no formal risk-of-bias assessment or evidence grading was undertaken. Results: Recovery differed by outcome and procedure. PROMs commonly improved within 1–3 months, while gait-quality measures recovered over about 7–13 weeks after THA and 13–24 weeks after TKA. Frequency-domain gait features, sample entropy, and machine-learning models provided information beyond step counts. Tracker-based interventions most consistently increased postoperative steps when introduced early and combined with behavior-change support, while digital rehabilitation was generally non-inferior to conventional rehabilitation. Wrist-worn consumer devices remained inaccurate with gait aids, compliance definitions varied, and higher step counts did not consistently correspond to better PROMs. Conclusions: Commercial wearables already support phenotyping and remote follow-up of individual recovery trajectories, but evidence for fully adaptive rehabilitation remains limited. Full article

Background: Physical activity plays a vital role in caring for people with multiple sclerosis (MS). Yet, challenges like fatigue, mobility impairment, fall risk, symptom fluctuation, and poor access to rehabilitation often make sustained participation difficult. Adhering to physical activity programmes is tough. Artificial intelligence (AI) may transform wearable, smartphone, clinical, and patient-reported data into interpretable information for monitoring, prediction and individualized support. Objective: Building on the identified need for individualized support, this scoping review mapped original AI-based studies relevant to physical activity, gait, mobility, ambulation, fatigue, fall risk, and real-world functioning in people with MS. Methods: The review followed PRISMA-ScR principles. Search covered records published from 1 January 2016 to 14 May 2026 across biomedical, rehabilitation, technology-oriented, trial registry, and citation-searching sources. Eligible studies included MS or MS-specific data, an explicit AI/ML or model-derived predictive analytics component, and an activity-related clinical domain. Results: The search identified 332 records/reports. After removing 127 duplicates, 205 records were screened, 45 full texts were assessed, and 21 original AI studies met the eligibility criteria. These addressed inertial sensor deep learning, wearable gait speed estimation, postural sway fall risk prediction, connected device prediction of fatigue and health state, smartphone-based ambulation characterization, treadmill or walkway gait classification, chair stand fall status prediction, daily life gait/turning fall prediction, fear-of-falling detection, sensor-derived fatigue prediction, mobile/wearable modelling of MS, and physical activity prediction. Conclusions: AI can extract clinically relevant patterns from gait, wearable, smartphone, connected device, clinical, and patient-reported data in MS. However, evidence does not yet show that AI-supported systems improve physical activity, adherence, fatigue burden, fall risk, quality of life, or functional independence. AI is therefore promising but early, and prospective, externally validated, human-supervised, fatigue-aware, safety-sensitive studies are needed to support clinically meaningful, patient-centred MS rehabilitation and activity planning. Full article

Background/objectives: Evidence on gait and postural recovery after robotically assisted total knee arthroplasty (raTKA), particularly with the ROSA system, remains limited. This study compared early gait, postural, functional, and patient-reported outcomes (PROMs) between ROSA raTKA and manual TKA (mTKA), with PROMs also assessed at final follow-up. Methods: This comparative cohort study included primary TKA patients treated by a single senior surgeon using the same implant and alignment strategy. Patients underwent either ROSA raTKA or mTKA. At three months, a senior physiotherapist assessed quadricep and tibialis anterior maximum voluntary isometric strength (MVIS), centre-of-mass (CoM) kinematics, lower-limb weight distribution, timed-up-and-go (TUG), range of motion (ROM), KOOS Pain, activities of daily living (ADL), and quality of life (QoL). The same KOOS domains were compared at final follow-up. Results: Seventy primary TKAs were included: 46 raTKAs and 24 mTKAs. No intraoperative complications occurred. Groups were comparable for age, BMI, sex, grip strength, and preoperative KOOS. At three months, no significant differences were found in quadricep MVIS (p = 0.257), tibialis anterior MVIS (p = 0.327), CoM kinematics (p = 0.066), weight distribution (p = 0.189), TUG (p = 0.599), ROM (p = 0.165), or KOOS domains. At final follow-up, KOOS-ADL (p = 0.041) and QoL (p = 0.032) were better in the raTKA group, but after Holm–Bonferroni correction, they were no longer significant (QoL, p = 0.384; ADL, p = 0.451). Conclusions: ROSA raTKA showed comparable early gait and postural recovery to mTKA. The marginal differences in KOOS domains are exploratory, as they were no longer significant after multiple-comparisons correction. Full article

Walking becomes less economical with age, but the links between detailed gait mechanics and metabolic energy expenditure remain unclear. This study examined multidimensional associations between gait characteristics and walking energetics in community-dwelling older adults. Eighty-five participants, including 62.4% women, completed a steady-state walking task while metabolic energy expenditure was measured using portable indirect calorimetry. Four energetic outcomes were analyzed: walking cost as a percent of peak metabolic capability, steady-state walking metabolic rate, gross cost of transport, and reserve cost of transport. Gait was assessed using an instrumented walkway and summarized across spatial, temporal, and variability domains, yielding 28 gait parameters. Pearson correlations were calculated between each gait parameter and each metabolic outcome, with significance set at p < 0.05. Across 28 gait features and four metabolic outcomes, clear association patterns emerged. Spatial parameters showed the strongest and most consistent relationships with metabolic cost, suggesting that step length, stride length, and related forward-progression measures are closely tied to energetic demand during steady-state walking. Temporal parameters showed meaningful but generally weaker associations, while variability-based metrics also demonstrated moderate significant correlations. These findings provide a quantitative framework linking gait mechanics with walking energetics and may help identify mobility biomarkers and intervention targets in older adults. Full article

Background/Objectives: Spinal cord injury (SCI) causes heterogeneous motor, sensory, autonomic, and participation limitations; recovery priorities vary by injury level, completeness, time since injury and residual function. Robotic rehabilitation has expanded from assistive technology to restorative, compensatory and health-promoting interventions, but patient-tailored prescription frameworks remain underdeveloped. Methods: PubMed/MEDLINE was searched from database inception to May 2026 using predefined domain-specific strategies, and findings were synthesized narratively to integrate mechanistic, clinical, safety and implementation evidence. Results: Robotic systems can increase task-specific repetition, sensorimotor feedback, active engagement and quantitative monitoring. Upper-limb robotics are feasible in cervical SCI and may support reach, grasp and activities of daily living, although SCI-specific controlled evidence remains limited. Lower-limb exoskeletons and locomotor robots can support gait practice, upright mobility, exercise exposure and selected secondary health outcomes, but walking speed, energy expenditure, cost, supervision needs and community translation remain important barriers. Sensory and non-motor effects, including proprioceptive input, spasticity, pain, bowel routine, cardiometabolic conditioning, participation and psychological well-being, are clinically relevant but should be interpreted according to evidence strength. Robotics combined with functional electrical stimulation, virtual reality, brain–computer interfaces, non-invasive brain stimulation and artificial intelligence-driven adaptation is promising but not yet routine. Conclusions: Robotic rehabilitation in SCI should be prescribed through a severity-based process that considers lesion level, American Spinal Injury Association Impairment Scale grade, residual voluntary and sensory function, safety, patient priorities and measurable goals. The proposed framework supports transparent selection and prospective validation of individualized robotic rehabilitation and shifts decisions beyond device availability toward clinically meaningful and equitable implementation. Full article

Population ageing has increased the need for safe, accessible, and sustainable exercise strategies that support functional capacity, independence, and healthy ageing. Nordic walking has been examined across physiological, functional, clinical, and health-related contexts; however, previous research syntheses have generally focused on specific intervention effects, populations, or outcome categories, leaving the relationships among these research strands insufficiently understood. This study combined CiteSpace-based bibliometric analysis with Latent Dirichlet Allocation (LDA) topic modeling to examine the knowledge structure and thematic development of Nordic walking research in ageing-related populations from 2006 to 2025. A total of 263 publications indexed in the Web of Science Core Collection were included in the final analysis. The results showed that research activity was geographically concentrated, whereas institutional connections were relatively dispersed. Co-citation patterns linked the field with exercise science, rehabilitation, gerontology, gait analysis, and clinical health research. Keyword and temporal analyses indicated that research attention expanded from the physiological and biomechanical characterization of Nordic walking toward functional rehabilitation, chronic disease-related applications, and broader health outcomes. The seven LDA-derived topics reflected health-outcome evaluation, metabolic and cardiovascular indicators, functional rehabilitation, exercise physiology, cognitive health, body composition and muscle function, and gait-related rehabilitation. Rather than representing isolated research domains, these topics reveal an interconnected knowledge structure linking exercise-related mechanisms, functional and clinical outcomes, and broader ageing-related health concerns. Future research should improve intervention reporting, adopt population-specific outcome selection, and strengthen the linkage between exercise mechanisms and functional or health-related outcomes to support more comparable and cumulative evidence. Full article

Dinosaur footprints are among the most abundant trace fossils, but they are not direct records of anatomy, behaviour or faunal composition. They preserve locomotion, substrate interaction and occurrence data only after those signals have been filtered by foot anatomy, movement, sediment properties and preservation. Classical dinosaur ichnology has relied on two-dimensional outlines, linear and angular measurements, qualitative ichnotaxonomy and influential hypotheses about trackmaker identity, speed, social behaviour and evolutionary timing. Here we review how these hypotheses are being reassessed with three-dimensional digitisation, geometric morphometrics, supervised and unsupervised machine learning, and biomechanical simulation. We first consider how different footprint representations, including interpretive outlines, landmarks, silhouettes, depth maps and three-dimensional models, shape the questions that track data can answer. We then assess analytical approaches ranging from multivariate statistics and landmark-based classifiers to convolutional neural networks and β-variational autoencoders. Against this methodological background, we revisit four linked problem domains: ornithopod–theropod discrimination and the Grallator–Anchisauripus–Eubrontes plexus; speed and gait reconstruction; ecological and behavioural interpretations of track abundance, sauropod gauge and trackway arrangement; and macroevolutionary claims about body-size trends, functional morphotypes and avian-like pedal morphologies. Across these cases, newer methods rarely remove ambiguity. They more often show where classical interpretations are robust, where they depend on representation or prior labels, and where competing explanations remain hard to separate. We argue that footprint-based inference is strongest when tracks are treated as preservationally filtered products of anatomy, motion and substrate mechanics, and when they are integrated with skeletal data, experimental analogues and forward models in explicit, uncertainty-aware frameworks. Full article

Background: Post-stroke spasticity is a frequent and disabling consequence of stroke, including when affecting the lower limbs, where it may impair stance, gait, balance, postural control, functional independence and quality of life. Botulinum toxin type A (BoNT-A) is widely used as a focal neuromodulatory treatment for post-stroke spasticity. However, the relationship between BoNT-A-induced reduction in muscle hypertonia, objective changes in spastic muscle’s biomechanical properties, and functional outcomes such as mobility and balance remains insufficiently clarified. This systematic review aimed to synthesize recent evidence regarding the non-invasive assessment of spastic muscle properties following BoNT-A administration in post-stroke patients, with emphasis on mobility and balance outcomes. Methods: A systematic literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search was performed in international electronic databases and included studies published between 1 January 2023 and 31 December 2025. The search strategy used specific keywords and keyword combinations/syntaxes, contextually, related to the topic of interest. Results: A total of 32 studies met the eligibility criteria and were included in the final data analysis and synthesis, comprising 13 primary clinical studies—6 randomized or controlled interventional studies and 7 observational studies—together with 12 reviews or evidence syntheses, 3 technical or clinical framework papers, and 4 survey, epidemiological, health-services or health-economic studies. Overall, the included articles addressed BoNT-A treatment in post-stroke spasticity, with partial focus on muscle properties, gait, mobility, and functional outcomes. However, only a limited number of studies investigated objective non-invasive assessment methods, and few directly related muscle-property changes in balance and mobility outcomes. Formal risk-of-bias assessment and quantitative synthesis were not performed because of the substantial heterogeneity of the included evidence, with only two studies being potentially suitable for pooling and these addressing different muscle groups, interventions, and outcome domains. Discussion and Conclusions: The reviewed literature confirms the clinical relevance of BoNT-A in the management of post-stroke spasticity. However, most studies assess treatment effects mainly through clinical scales, while objective evaluation of muscle stiffness, elasticity, viscoelastic properties, and their relationship with mobility and balance remains limited. Although some studies address gait, functional recovery, or muscle-related changes, the combined use of BoNT-A treatment, myotonometric assessment, and proprioceptive–stabilometric evaluation is largely absent. Therefore, current evidence highlights an important research gap and supports the need for future longitudinal studies integrating non-invasive biomechanical and balance assessment tools to better monitor treatment response and guide individualized neurorehabilitation in post-stroke patients. Full article

Introduction: Pain is a major contributor to functional impairment in neurological patients. Botulinum toxin type A (BT-A) is well-established for focal spasticity and has shown some benefits in pain reduction. Long-term data from large cohorts remain limited. Material and Methods: This retrospective study (2017–2022) analyzed 20 variables from spastic patients treated with Incobotulinumtoxin A (INCO) for pain and spasticity. Functional analysis was classified into 5 major domains: Pain, Spasticity spectrum, Mobility, Activities of daily living, and Gait. Results: A total of 1575 records from 67 patients were collected. The mean age was 57.7 years, mostly female (n = 35), and stroke was the leading etiology (n = 40). The most typical patterns were wrist flexion (80%) and ankle flexion (94.9%). It was injected with 50 to 400 units of INCO, targeting an average of 3.47 upper-limb and 2.34 lower-limb muscles per session. Functional improvement was observed across all domains (GAS change 27.4), with the greatest effects in pain (GAS change 26.9; 5.16 VAS reduction). Discussion: The findings, derived from one of the largest datasets, support that INCO, and probably BT-A, should be indicated for the pain of spastic patients, regardless of etiology. Conclusions: Promising functionality outcomes, especially in this group, warrant further, more comprehensive studies. Full article

Background: The Mercer Universal Prosthesis (MUP), designed with a default “neutral” (vertical) socket alignment, was developed to simplify transtibial prosthetic fitting, reduce labor costs, and improve access to prosthetic care in low-resource settings. Methods: This present longitudinal study evaluated biomechanical and functional outcomes at baseline, 6 months, and 12 months in 20 transtibial amputees fitted with the MUP. Results: Functional outcomes, assessed using the SF-36, showed significant improvement in overall health scores at 12 months (p < 0.001), while physical function and energy/fatigue domains remained unchanged (p = 0.686 and p = 0.211, respectively). Biomechanically, sagittal kinematics, measured using inertial motion capture, revealed significant limb × time interactions for hip flexion, knee flexion, and ankle plantarflexion. At 6 months, maximum hip flexion (−7°, p = 0.008) and knee flexion (−11°, p = 0.005) of the prosthetic limb were decreased versus baseline. At 12 months, the only observed difference was increased maximum ankle plantarflexion of the intact limb (+5° vs. baseline, p = 0.016). Muscle effort, quantified via the integral of EMG throughout the gait cycle, did not differ significantly between prosthetic and intact limbs across time points. Gait symmetry index (GSI) scores for hip, knee, and ankle range of motion trended toward gradual improvement but without statistical significance (p > 0.05). Conclusions: The MUP performance was maintained over 12 months, with stable biomechanical performance and meaningful quality-of-life gains. These findings support its potential as a cost-effective solution to expand prosthetic accessibility in low- and middle-income countries. Full article

Background/Objectives: This study aimed to evaluate longitudinal changes in physical and cognitive function in individuals with chronic stroke over five years and to explore factors associated with long-term outcomes. Methods: This ambispective cohort study included individuals with chronic stroke who had participated in a previous cross-sectional study conducted between 2018 and 2019. Assessments were performed at baseline and five-year follow-up (2023–2024). Primary outcomes were physical function, assessed using the six-minute walk test (6MWT), comfortable and fast gait speeds measured by the ten-meter walk test (10MWT), and cognitive function, assessed using the Mini-Mental State Examination (MMSE). Results: Thirty-two individuals participated (mean age 63.5 ± 10.3 years; median time since stroke 7.0 years). The six-minute walk distance declined by 22% (263.7 to 206.8 m, p < 0.001), whereas no significant changes were observed in gait speed or cognitive function. Age, baseline National Institutes of Health Stroke Scale (NIHSS) score, baseline values of the 6MWT and 10MWT, and nutritional status (Mini Nutritional Assessment–Short Form; MNA-SF) showed associations with physical outcomes. For cognitive outcomes, baseline NIHSS score, baseline MMSE score, MNA-SF score, and education level showed associations. However, sensitivity analyses suggested that the associations involving MNA-SF and education level were not robust. Conclusions: Physical function declines over five years in individuals with chronic stroke, highlighting the importance of long-term follow-up. While global cognition (MMSE) remained stable, domain-specific declines cannot be ruled out. Baseline stroke severity, nutritional status, and initial functional and cognitive performance may be associated with long-term outcomes. Full article

Falls represent a severe public health issue and a primary cause of impairment across both geriatric and adults clinical populations, underscoring the importance of early and tailored prevention. Research evidence shows that technology-assisted rehabilitation may enhance balance and reduce fall risk; however, long-term real-world evidence remains limited. This retrospective study evaluated the impact of personalized balance training by hunova^® robotic platform on fall risk in a heterogeneous clinical cohort (n = 355; mean age 58.24 ± 19.63 years) comprising neurological and orthopedic conditions. Fall risk was assessed using the Silver Index, a sensor-based robotic posturographic score to predict 12-month fall probability at baseline (T0), after 6 weeks (T1), and at 6 months (T6). Robotic rehabilitation was provided to 162 patients (TREAT-G), while 193 served as a control group (NOTREAT-G). TREAT-G demonstrated a significant reduction in fall risk over time (p = 0.036), with improvements from T0–T1 (p < 0.001) and T0–T6 (p = 0.009). Domain-specific analysis showed significant increases in limits of stability, sit-to-stand, and gait speed (p < 0.001). Significant time × group interactions were observed for limits of stability (p = 0.004) and sit-to-stand (p < 0.001). Based on the occurrence of falls in the previous six months, the sample was then divided into ‘Fallers’ (at least one fall) and ‘No-Fallers’ (no falls). Subgroup analysis revealed significant improvements in ‘Fallers’ (p = 0.016) and ‘No-Fallers’ (p = 0.034) following treatment in contrast to untreated patients—both ‘Fallers’ and ‘No-Fallers’. These results indicate that tailored robot-assisted therapy with hunova^® significantly reduces risk of fall and improves long-term dynamic balance. This study supports the clinical integration of robotic assessment and therapy as a robust tool for proactive fall prevention in diverse clinical settings. Full article