Search Results (1,922)

Background/Objectives: Adult Polyglucosan Body Disease (APBD) is a rare neurodegenerative glycogen storage disorder characterized by progressive gait disturbance, sensory impairment, and balance dysfunction. Although rehabilitation is recommended for functional maintenance, evidence regarding robot-assisted gait training (RAGT) in APBD remains extremely limited. Methods: A 58-year-old man with progressive lower extremity sensory and motor symptoms was diagnosed with APBD in 2026. Neurological examination revealed severe proprioceptive impairment in both great toes, generalized sensory deficits, gait instability, and impaired balance. Functional assessment demonstrated mild balance impairment with generally preserved muscle strength except for mild weakness in the lower extremities. RAGT was initiated and performed for 19 sessions over approximately 6 weeks in combination with conventional rehabilitation therapy, including gait and balance training with visual feedback. Results: Following the combined rehabilitation program, improvements were observed in balance function, postural stability and proprioceptive function. Conclusions: This case suggests that RAGT combined with conventional rehabilitation may improve balance and gait-related function in patients with APBD. Repetitive task-specific gait training with enhanced sensory feedback may be particularly beneficial in APBD, where proprioceptive impairment and sensory ataxia are major contributors to gait dysfunction; however, this remains a hypothesis that requires validation in future studies. This report highlights the feasibility and potential applicability of RAGT in rare neurodegenerative disorders such as APBD. Full article

Background and Objectives: Low participation rates in exercise programs among older adults highlight the need for theory-driven, biopsychosocial interventions that enhance adherence, self-efficacy, and functional outcomes. Grounded in principles of motor learning and behavioral reinforcement within physiotherapy practice, this study aimed to examine the effect of periodic assessments combined with verbal feedback on functional and psychological outcomes in community-dwelling older adults. Methods: A pilot RCT was conducted involving 54 individuals aged ≥65 years (53 women and 1 man), recruited from senior community centers. Participants were randomly allocated to an intervention group (periodic assessment and verbal feedback; n = 27) or a control group (n = 27). Both groups participated in an identical 12-week structured exercise program, delivered twice weekly, focusing on balance, gait, and lower-limb functional training. An intention-to-treat approach was applied. Data were analyzed using Linear Mixed Models, with statistical significance set at p < 0.05. Results: Significant group × time interactions were observed in favor of the intervention group for key kinesiology-related functional outcomes, including the Short Physical Performance Battery (SPPB; p < 0.001), Timed Up and Go test (TUG; p = 0.011), and Activities-specific Balance Confidence Scale (ABC; p < 0.001). No statistically significant differences were identified between groups for the Behavioral Regulation in Exercise Questionnaire–2 (BREQ-2; p = 0.164) and the Self-Efficacy for Exercise Scale (ESE; p = 0.108), indicating that the primary psychological outcome (ESE) was not confirmed. However, both ESE and BREQ-2 demonstrated significant baseline differences favoring the intervention group, and, therefore, these findings should be interpreted with caution despite statistical adjustment. Conclusions: Periodic assessments followed by verbal feedback appear to selectively improve the functional effectiveness of structured exercise programs in older women, particularly physical performance, functional mobility, and balance confidence, with no significant differential effect on the primary psychological outcome (ESE; group × time interaction: p = 0.108). These findings support assessment-informed and feedback-driven physiotherapy strategies as a promising adjunct to exercise programs in older adults, with potential implications for optimizing functional outcomes within applied kinesiology and rehabilitation contexts. Full article

Although considerable research has investigated non-motorized treadmills (NMTs), most studies have focused on healthy adults or athletes. This study aimed to compare trunk and lower limb muscle activation during walking on an NMT and flat ground (FG) at different exercise intensities in patients with stroke. Eighteen patients with stroke participated in this within-subject, repeated-measures experimental study conducted at a single hospital. Participants performed walking trials under six randomized conditions, comprising both NMT and FG walking at intensities of 20%, 40%, and 60% of heart rate reserve (HRR). Muscle activation of the affected-side erector spinae, internal oblique, gluteus medius, gluteus maximus, vastus medialis oblique, biceps femoris, and lateral gastrocnemius was assessed. Walking on the NMT resulted in significantly greater overall muscle activation than walking on FG (p < 0.05). In addition, significant differences in trunk and lower limb muscle activation were observed across HRR levels during both NMT and FG walking (p < 0.05), indicating that exercise intensity influenced neuromuscular responses. These findings suggest that NMT walking, particularly at higher intensities, acutely increases neuromuscular demands, providing preliminary evidence for its potential application as a demanding walking condition for stroke rehabilitation. Full article

Background/Objectives: Wearable technology is increasingly used to provide biofeedback in physical rehabilitation; however, there is no consensus on which biofeedback parameter is most appropriate for clinical use, as most studies evaluate only one arbitrarily selected parameter. This study presents a wearable multimodal biofeedback system integrating multiple parameters selected based on the prior literature and evaluates its feasibility, usability, and implementation within a rehabilitation context through a longitudinal post-stroke case study. Methods: The system integrates inertial and electromyographic sensors to monitor centre of mass (CoM-B), joint angle (ANG-B), and muscle activity (EMG-B), delivering real-time sensory cues based on the monitored parameters. Feasibility was assessed in a post-stroke participant (male, 32 years, 29 months post-stroke, left hemiparesis, Fugl-Meyer Lower Extremity Score = 27) across 15 sessions involving stand-to-sit, split-stance weight shifting, and walking tasks. Each task was practiced with all three biofeedback parameters, with five sessions per parameter. Results: The motor performance varied across biofeedback parameters and tasks. CoM-B was associated with favourable trends in motor performance during stand-to-sit, showing improvements in medio-lateral displacement (0.03/session); ANG-B during walking, showing increased ankle dorsiflexion (1 deg/session); and EMG-B during split-stance weight shifting, showing increased tibialis anterior activation (5 µV/session). Conclusions: The findings generate the hypothesis that the ability of biofeedback to elicit favourable motor performance is task-dependent, suggesting that the choice of biofeedback parameters may need to be adapted to task demands. The system demonstrated high usability and feasibility, supporting its potential for post-stroke rehabilitation. Further studies are needed to test the generated hypothesis and evaluate the system efficacy. Full article

Walking is not merely locomotion but a window into the nervous system, integrating cortical, subcortical, cerebellar, spinal, and peripheral networks into a unified motor behavior. Across neurological diseases—including Parkinson’s disease, atypical parkinsonism, cerebellar ataxias, stroke, multiple sclerosis, neuropathies, neuromuscular disorders, and functional gait syndromes—gait disturbances are among the most disabling clinical features, contributing to falls, loss of independence, institutionalization, and premature mortality. Traditional bedside observation remains indispensable, but it lacks the sensitivity and reproducibility needed to capture subtle, episodic, or prodromal abnormalities. Over the past decade, advances in wearable sensors, marker-based and markerless motion capture, pressure-sensitive walkways, force plates, artificial intelligence, and machine learning have positioned digital mobility outcomes as promising, ecologically valid biomarkers of neurological function. These measures can support differential diagnosis, provide prognostic information on falls and survival, and serve as sensitive endpoints in therapeutic trials. They may also detect early abnormalities, such as increased stride-to-stride variability or prolonged double-support time, before overt clinical deterioration becomes evident. Clinical applications are increasingly evident across disorders, including distinguishing Parkinson’s disease from atypical parkinsonism, quantifying treatment response in normal-pressure hydrocephalus, tracking progression in ataxia and multiple sclerosis, predicting functional decline in motor neuron disease, and guiding rehabilitation after stroke. Integration with neuroimaging, electrophysiology, and molecular biomarkers is beginning to reveal the circuits underlying variability, instability, and freezing, positioning gait as a systems-level marker of neural integrity. Nevertheless, methodological heterogeneity, limited disease-specific validation, insufficient longitudinal data, and lack of consensus on clinically meaningful parameters continue to constrain translation. Cognitive, affective, and environmental influences also remain insufficiently represented in digital frameworks, while equity, accessibility, algorithmic bias, and privacy require careful ethical governance. Reconceptualizing gait as a “sixth vital sign” reframes mobility as a multidimensional biomarker of neural and systemic health. With harmonized protocols, robust validation, multimodal integration, and appropriate ethical frameworks, gait analysis could become a cornerstone of precision neurology. Full article

Background: Artificial intelligence (AI)-driven markerless motion capture (MMC) technologies are increasingly being integrated into pediatric healthcare to improve the assessment and management of movement disorders. These video-based systems enable non-invasive motion analysis without wearable sensors, facilitating more natural movement assessment in children, particularly those with neurological or developmental conditions. Objectives: We evaluated the clinical applicability of AI-based MMC tools in pediatric settings for diagnosis, monitoring of motor development, and rehabilitation. Methods: This systematic review was registered in PROSPERO (CRD42024511787) and conducted by two independent reviewers, with a third reviewer resolving disagreements. The literature published between 2018 and 2025 was systematically searched. Studies involving pediatric populations or clinically relevant pediatric applications of MMC were included. Results: Of 1521 identified studies, 52 were finally selected. The included studies evaluated populations across a wide age range. However, seven of the included articles were specifically focused on underage populations. Infant studies primarily analyzed whole-body movements, emphasizing the relevance of global motor patterns in early development. OpenPose and AlphaPose were the most frequently used frameworks in pediatric research because of their automatic full-body key point detection, whereas DeepLabCut was commonly selected for its customizable labeling capabilities. Theia3D emerged as a promising clinically applicable solution with high accuracy. Most studies evaluated kinematic parameters as objective markers of motor performance and development. However, methodological heterogeneity and limited pediatric-specific validation remain important limitations. Conclusions: AI-driven MMC technologies show considerable potential to support objective, accessible, and child-friendly movement assessment in pediatric clinical practice. Full article

Integrating neural and muscular signals into wearable robotics enables adaptive assistance during real-world tasks. This study proposes a multimodal neural interface for passive exoskeletons that combines electroencephalography (EEG) and electromyography (EMG) signals to classify motor gestures and estimate real-time cognitive and muscular effort, supported by finite-element-based biomechanical modeling. The system was implemented on the Ottobock Shoulder X passive exoskeleton© and validated using synchronous EEG–EMG acquisition via the LiveAmp platform©, a commercially available platform that was not developed specifically for this study. A hybrid CNN–LSTM architecture with deep fusion was employed to enhance robustness and responsiveness under realistic operating conditions. This study proposes a multimodal neural interface for the software-level adaptive assistance of passive upper-limb exoskeletons. While the physical device maintains a static mechanical profile, the proposed digital framework achieves adaptation by interpreting the user’s physiological and motor states. Ten healthy participants performed three functional tasks (screwing, moving the box, and lifting the box) under five assistive conditions. Finite element modeling (FEM) was used to characterize the torque–angle relationship of the passive exoskeleton and to support the interpretation of experimentally observed assistive torque profiles. The FEM model, used as an offline biomechanical analysis tool to aid in the interpretation of experimental results, has not been integrated into the real-time control loop. Results showed an average classification accuracy of 90%, an F1-score of 0.85, and inference latency below 180 ms, confirming real-time applicability. Cognitive indices such as the Cognitive Load Index (CLI) and Frontal Asymmetry Index (FAI) enabled adaptive modulation of assistance strategies without requiring active actuation, thereby preserving the device’s intrinsic passive nature. Comparative torque analysis highlighted the ergonomic benefits of passive systems in mid-range postures, while Finite Element Method (FEM) supported analysis clarified their limitations under highly dynamic loads compared to active solutions. These findings advance multimodal brain–machine interfaces for wearable robotics by integrating physiological sensing, deep learning, and biomechanical modeling, offering a safe, energy-efficient, and adaptive approach with potential rehabilitation, occupational ergonomics, and human–robot applications. Full article

An electroencephalogram (EEG) signals provide vital insights for stroke rehabilitation, yet analyzing these complex, high-dimensional data to detect gait anomalies remains challenging. Artificial intelligence offers a promising solution to precisely identify abnormal movements, assisting physicians in optimizing personalized treatments. This exploratory pilot study aims to evaluate multi-class deep learning frameworks for classifying eight distinct normal and abnormal motor activities in stroke patients using EEG data. EEG signals from eight stroke patients were utilized to train and evaluate a customized Convolutional Neural Network (CNN), DeepConvNet, and EEGNet. Furthermore, channel reduction configurations (32, 22, and 15 channels) were investigated to determine optimal clinical setups. In the Leave-One-Out Cross-Validation (LOOCV) evaluation involving seven patients, EEGNet attained the highest descriptive average F1-score of 0.810. Moreover, when assessed independently on an unseen patient, it achieved an F1-score of 0.915, indicating its potential in accommodating individual differences within this limited cohort. Moreover, EEGNet exhibited a low false positive rate of 0.175, minimizing false alarms. While the 32-channel setup yielded the highest consistency, reduced configurations served as hypothesis-generating for specific tasks. In conclusion, EEGNet demonstrated superior average performance in differentiating complicated gait patterns in this exploratory pilot study, underscoring its promise for real-time, non-invasive monitoring in stroke neurorehabilitation. Full article

Lower limb motor dysfunction caused by stroke severely impacts patients’ quality of life. Existing rehabilitation products tend to prioritize technology over emotional considerations, leading to poor compliance—a critical issue that urgently needs to be addressed in product design. This study explores human-centered design for home-use rehabilitation devices by integrating the Three-Level Theory of Emotional Design (ED), the Analytic Hierarchy Process (AHP), and the Needs–Function–Behavior–Structure (NFBS) model. The study employed AHP to quantify user needs and identify core requirements. It utilized the NFBS model and Jaccard similarity clustering to construct a systematic mapping pathway from needs to structure, categorizing 44 structural components into six major modules. Based on this framework, an iterative optimization strategy encompassing functional configuration, structural optimization, and emotional interaction was proposed and implemented through two rounds of design practice. Through two rounds of iterative design, the expert-rated overall satisfaction score increased from 0.883 to 0.928, with satisfaction scores at the instinctive, behavioral, and reflective levels reaching 0.949, 0.931, and 0.892, respectively. The study demonstrates that the proposed ED-AHP-NFBS integrated model effectively bridges the gap between technical rationality and humanistic care, providing an actionable framework for the human-centered design of home rehabilitation devices. Full article

Background/Objectives: Transcranial direct current stimulation (tDCS) has been explored as a strategy for pain management, but no study has investigated its use in Greater Trochanteric Pain Syndrome (GTPS). This study evaluated the effects of the combination of resistance exercises (REs) with tDCS on pain, functionality, and quality of life in patients with GTPS. Methods: In this randomized, double-blind trial, adults with GTPS were allocated to receive tDCS with RE (intervention group, IG) or sham tDCS with RE (control group, CG). Supervised 20 min sessions occurred on four consecutive days. Anodal tDCS (2 mA) was applied over the primary motor cortex. The primary outcome was the VISA-G.BR score at day thirty. Secondary outcomes included pain, functionality, and quality of life at multiple time points, assessed by HAGOS, PQAS, McGill Pain Questionnaire, and SF-36. Results: Thirty patients were included. Both groups improved, but between-group differences were nonsignificant for the primary outcome (VISA-G.BR effect size, −0.16; 95% CI, −0.54 to 0.27; p = 0.460). Secondary outcomes followed a similar pattern. Conclusions: These findings reinforce the value of RE in GTPS while suggesting a limited role for short-term tDCS protocols. Future studies should investigate whether protocols involving a greater number of stimulation sessions may produce superior clinical effects. Full article

Background/Objectives: Assisted verticalization and supported upright activity are relevant components of rehabilitation in adults with severe acquired brain injury (sABI), although patient selection and implementation remain challenging. This retrospective observational study aimed primarily to describe the implementation feasibility and documented safety of GRILLO-based training in routine inpatient multidisciplinary rehabilitation, and secondarily to report exploratory pre–post functional changes. Methods: We reviewed clinical records of 34 adults screened or considered for GRILLO-based training at Centro Cardinal Ferrari KOS, Italy, between June 2022 and December 2024. GRILLO training was delivered as part of standard care and not as an experimental intervention. Functional outcomes included the Barthel Index (BI), Trunk Control Test (TCT), Tinetti Balance Scale, and Tinetti Gait subscale, extracted from routine documentation. Non-parametric descriptive analyses were used. Results: Of 34 screened patients, 4 did not meet diagnostic criteria for ABI, 5 interrupted training because of pain or poor tolerance to prolonged upright positioning, and 3 were not included because of poor compliance/motivation or an incomplete clinical pathway. The paired functional-analysis cohort comprised 22 patients: 20 (91%) completed 15 sessions and 2 (9%) completed 10 sessions. No serious device-related adverse events were documented in available clinical records, although minor adverse events were not systematically monitored. Among patients with paired observations, median BI increased from 16 to 22.5 (median change, +3; p = 0.008; n = 20), median TCT from 72 to 74 (median change, +12; p < 0.001; n = 21), and median Tinetti Balance Scale from 1 to 2 (median change, +1; p = 0.006; n = 22). Individual responses were heterogeneous and floor effects were evident, especially for balance and gait-related measures. Conclusions: In this retrospective real-world cohort, GRILLO-based training could be implemented in selected severely impaired inpatients, but feasibility may be overestimated if interrupted and non-completing cases are not considered. The non-completion cases may suggest that feasibility depends not only on initial clinical indication, but also on the appropriate timing of introduction, tolerance to prolonged upright physical effort, pain/discomfort, motivation, and behavioral engagement. The retrospective design, survivorship bias, non-systematic adverse-event monitoring, concurrent multidisciplinary rehabilitation, and absence of a comparator group preclude conclusions regarding device-specific safety or efficacy. Nevertheless, these preliminary findings support further prospective controlled studies. Full article

Background and Objectives: Brachial plexus birth injury (BPBI) is a peripheral nerve injury occurring during birth that may result in upper-extremity weakness and functional impairment. This systematic review aimed to evaluate the effects of neuromuscular electrical stimulation (NMES) on motor function, muscle strength, range of motion, and upper-extremity function in children with BPBI. Materials and Methods: This systematic review was conducted according to PRISMA guidelines and registered in PROSPERO. PubMed, CINAHL, Scopus, Web of Science, PEDro, and the Cochrane Library were searched from inception to 5 May 2026. Only randomized controlled trials were included. Methodological quality was assessed using the PEDro scale, and risk of bias was evaluated using the RoB 2 tool. Results: Seven randomized controlled trials involving 197 participants were included. Several studies reported improvements in shoulder abduction, elbow flexion, wrist extension, muscle strength, and motor function following NMES compared with conventional therapy. The combination of NMES and constraint-induced movement therapy demonstrated favorable outcomes in functional performance. However, substantial heterogeneity was observed across studies regarding participant characteristics, NMES parameters, treatment duration, and outcome measures. The certainty of evidence ranged from low to very low. Conclusions: Current evidence suggests that NMES may serve as a potential adjunct to conventional rehabilitation in children with BPBI. However, given the low to very low certainty of the evidence, high risk of bias, and substantial clinical and methodological heterogeneity among the included studies, definitive clinical recommendations cannot currently be made. Future well-designed randomized controlled trials using standardized protocols, consistent outcome measures, and longer follow-up periods are warranted. Full article

Background: Burn injuries are increasingly being recognized as chronic conditions with long-term physical, emotional, and social consequences. As survival after acute burn trauma improves, greater attention has shifted toward health-related quality of life (QoL) in survivors, particularly in regions where data remain limited. Methods: This study included burn survivors treated between January 2022 and December 2023 in the Department of Plastic Surgery and Reconstructive Microsurgery of the Emergency Clinical Hospital “Bagdasar-Arseni,” Bucharest, Romania. Patients who survived hospitalization and follow-up were invited to complete a Romanian-adapted version of the Burn Specific Health Scale-Brief (BSHS-B). Demographic and clinical data were collected from medical records, including burn type, total body surface area (TBSA), burn depth, burn localization, and access to rehabilitation services. Statistical analysis included descriptive methods, chi-square tests, t-tests, Kendall’s tau-b, Cramer’s V, Cronbach’s alpha, and exploratory factor analysis. Results: Thirty-eight patients were included. Most burns were thermal (94.74%), while burns involving <10% TBSA were most frequent (60.53%). Functional outcomes were generally favorable, with most patients reporting no difficulty in basic daily activities such as bathing, dressing, and writing. However, fine motor activities and return to previous work were more frequently affected. Emotional recovery appeared less complete, with persistent mild-to-moderate loneliness, sadness, and emotional distress reported by many participants. Women reported higher levels of loneliness (p = 0.015), while third-degree burns were associated with more frequent depressive symptoms (p = 0.008). Depressive symptoms were also significantly associated with functional limitations (such as getting dressed, p = 0.002) and work impairment (p < 0.001). The adapted functional and emotional subscales showed excellent internal consistency. Conclusions: Post-burn recovery extends beyond physical healing. Although most patients regained functional independence, emotional distress and occupational difficulties often persisted. These findings support the need for multidisciplinary long-term burn care integrating physical rehabilitation, psychological screening, and psychosocial support. Full article

This paper presents a comprehensive augmented reality (AR)-based rehabilitation system for upper-limb recovery that integrates AR-assisted art therapy, automated markerless goniometry, and the interval mathematical modeling of rehabilitation dynamics. The proposed platform combines four interconnected subsystems: a Python-based markerless video analysis module utilizing three stationary IP cameras, MediaPipe Pose Landmarker, and Kalman filtering; an AR art-therapy application developed for the Magic Leap 2 headset using Unity/OpenXR; a server-side subsystem implemented in NestJS/TypeScript; and a physiotherapist-oriented web application developed in React. The primary objective of the study is the real-time automated assessment of shoulder joint kinematics during AR-assisted rehabilitation sessions, including flexion (160–180°), extension (50–60°), and abduction (up to 180°). To describe and forecast rehabilitation dynamics, interval mathematical models based on recurrent difference equations were developed, enabling the prediction of subsequent joint angle values using the previous 3–4 observations. Structural and parametric identification of the interval models was performed using the artificial bee colony optimization algorithm. Experimental validation was conducted on rehabilitation data collected from five patients with different clinical diagnoses, including bursitis, epicondylitis, capsulitis, osteoarthritis, and fracture-related impairments. Under the considered experimental conditions, the proposed approach demonstrated promising predictive performance, with an angular prediction error below 5° and a correlation exceeding 95% between predicted and measured rehabilitation trajectories. The developed system implements a unified rehabilitation cycle of “execution–measurement–prediction–adaptation”, enabling the continuous monitoring of recovery dynamics, adaptive adjustment of rehabilitation scenarios, and estimation of the rehabilitation duration required to achieve target motor outcomes. The proposed approach contributes to the development of intelligent AR-based rehabilitation systems by combining markerless motion analysis, predictive interval modeling, and adaptive art-therapy mechanisms within a single clinical framework. Full article

Background/Objectives: Pressotherapy is a method of controlled compression using special cuffs designed to improve lymphatic and blood circulation and support aesthetic treatments and therapies. The available literature lacks evidence of the effects of these treatments on skin parameters in healthy, untrained women. It was hypothesized that a series of pressotherapy treatments would positively impact skin firmness, hydration, friction resistance, and transepidermal water loss (TEWL). Methods: The study involved 15 healthy women aged 20–26 years who underwent a series of 10 pressotherapy treatments on the lower limbs (preliminary study, without a separate control group). The effects of the therapy were assessed by measuring skin hydration, transepidermal water loss (TEWL), elasticity, and resistance to friction, with measurements taken in standardized conditions before, during, and after the treatment cycle. Results: The study did not observe significant changes in skin hydration (Chi-square = 0.48; p = 0.923; Kendall’s W = 0.016) (CI 95% 6.07–16.22), transepidermal water loss (TEWL) (Chi-square = 6.24; p = 0.100; Kendall’s W = 0.208) (CI 95% 2.29–14.37) and skin friction coefficient (Chi-square = 6.27; p = 0.099; Kendall’s W = 0.209) (CI 95% 0.06–1.52), while analysis of elasticity and firmness parameters showed a significant improvement in selected biomechanical indicators (R0 (Chi-square = 13.32; p = 0.004; Kendall’s W = 0.440), R3 (Chi-square = 12.39; p = 0.006; Kendall’s W = 0.413), R8 (Chi-square = 9.00; p = 0.029; Kendall’s W = 0.300), Q1 (Chi-square = 11.64; p = 0.008; Kendall’s W = 0.388), Q2 (Chi-square = 7.54; p = 0.050; Kendall’s W = 0.251) (CI 95% 0.01–0.17)). Conclusions: A series of pressotherapy treatments on the lower limbs of young women may affect skin elasticity (selected parameters) but may not have a significant impact on skin hydration, transepidermal water loss (TEWL), or skin resistance to friction. Full article