Search Results (3,348)

Rationale and objectives: Parkinson’s disease (PD) significantly affects visual function, especially convergence and eye movements, impacting tasks such as reading. The objective was to investigate preliminarily the impact of the use of digital visual training in PD patients with associated convergence insufficiency (CI). Materials and methods: Pre–post pseudo-experimental pilot study to evaluate the impact of a novel digital therapy system (video game for use on a mobile phone or tablet) in 13 patients with PD and CI, with a mean age of 67 years. A comprehensive visual assessment was performed before and after a 6-week home-based visual rehabilitation, including measurement of near point of convergence (NPC), near positive fusional vergence (PFV), oculomotor tests (NSUCO and King-Devick tests), and symptom assessments with two validated questionnaires (CISS and SQVD). Results: Treatment adherence was variable, ranging from 0.8% to 124.7%. Despite this, significant improvements were found after therapy in break (p = 0.022) and recovery points of the NPC (p = 0.007), as well as break (p = 0.003) and recovery points in near PFV (p < 0.001). In the NSUCO test, the total score improved significantly from 23.9 ± 4.2 to 26.2 ± 3.7 after therapy (p = 0.003). Furthermore, a significant reduction in the total King-Devick test time was observed, decreasing from 79.4 ± 28.8 s to 69.0 ± 21.5 s with therapy (p = 0.034). Finally, symptom questionnaire scores also decreased significantly with therapy (CISS p = 0.037, SQVD p < 0.001). Conclusions: The digital vision therapy system evaluated seems to improve oculomotor control and reduce visual symptoms associated with CI in PD patients. Studies with larger sample sizes and a control group are needed to fully validate the therapeutic effectiveness of this tool. Full article

The growing integration of rehabilitation robotics and artificial intelligence has created new opportunities for developing control strategies that better support clinicians during patient therapy. This study investigates machine learning and deep learning approaches for classifying upper limb motion using encoder-based biomechanical data, with the goal of identifying a model suitable for implementation in a rehabilitation exoskeleton. Several classical algorithms such as k-Nearest Neighbors, Random Forest, multiclass logistic regression, XGBoost, and an SVM classifier were evaluated alongside three deep learning architectures: convolutional layers, GRU and LSTM units. Models were trained and tested on two types of datasets using both standard cross-validation and leave-one-subject-out validation. The analysis included assessments of class separability, signal features’ importance, and comparative performance based on F1-score, accuracy, and confusion matrices. Results showed notable differences between validation strategies, with LOSO evaluation revealing limitations of the available dataset and emphasising the need for broader data collection. Overall, the findings indicate that, in the LOSO evaluation of the five-class multi-subject dataset—the most clinically realistic validation scenario—the LSTM-based model achieved the highest generalisation performance (accuracy 92.8%, macro-F1 0.927), supporting its suitability for integration into exoskeleton control systems aimed at detecting and mitigating compensatory movements. Full article

Accurately classifying gait abnormalities is crucial for the effective monitoring and rehabilitation of stroke patients. This study proposed a lightweight machine learning framework for distinguishing healthy from abnormal gait patterns using statistical features extracted from wearable gyroscope data. Statistical z-axis angular velocity values from both limbs were derived and used to evaluate the performance of multiple classifiers, including logistic regression, support vector machines, and ensemble methods. A leave-one-out cross-validation strategy was employed to enhance generalizability across subjects. The results indicated that several classifiers achieve accuracy and area under the curve (AUC) values exceeding 0.95, with random forest and support vector machine-based models demonstrating near-perfect class separability, with an AUC of 0.98. These findings highlighted the effectiveness of using minimal set of biomechanically relevant gyroscope features for gait classification in real-world healthcare applications. The proposed pipeline is computationally efficient, making it well suited for implementing in wearable and remote monitoring systems. Full article

Purpose: Access to assistive technology (AT) is a fundamental human right and a critical component of Universal Health Coverage (UHC). In the Middle East and North Africa (MENA) region, the scarcity of trained professionals remains a significant barrier to AT service provision. This study evaluates the effectiveness and perceived usefulness of the Assistive Technology Training Program (ATTP), a specialized continuing education initiative designed to build workforce capacity among rehabilitation and healthcare professionals. Methods: A convergent mixed methods design was used to analyze quantitative pre/post-test scores and qualitative focus group open-ended responses. Quantitative data were gathered from 386 participants across 11 MENA countries using a pre- and post-test assessment of AT knowledge. Qualitative utility and participant satisfaction were assessed through a 5-point Likert scale survey evaluating content relevance, trainer expertise, and facilities. Association tests (ANOVA and t-tests) were conducted to identify factors influencing knowledge gain. Results: Participants demonstrated a statistically significant improvement in AT knowledge, with the overall mean score increasing from 3.67 ± 1.13 to 7.50 ± 1.25 (p < 0.001). High levels of satisfaction were reported, with 92% of participants rating the training as “Very Good” or “Excellent” regarding its relevance to clinical needs. Association tests revealed that professional background (p < 0.001), employment status (p = 0.0017), level of education (p = 0.011), and prior training experience (p = 0.026) were significant factors in the magnitude of improvement, although all subgroups achieved significant learning gains. Qualitative thematic analysis per the focus group discussions using the WHO-GATE 5 P framework identified three major themes: (1) Structural Challenges: Issues with Products and Provision point toward a need for better infrastructure and localized supply chains. (2) Human Capital: Personnel barriers emphasize that training shouldn’t just be for professionals, but should extend to caregivers as well. (3) Systemic and Social Change: Policy and People focus on the “soft” side of AT moving toward user-involved guidelines and fighting social stigma to ensure rights are upheld. Conclusions: The ATTP is an impactful educational intervention that significantly enhances the foundational competencies of healthcare professionals in the MENA region. By addressing knowledge gaps and fostering practical skills, the program serves as a preliminary model that demonstrates potential for building regional capacity and supporting the United Nations’ Sustainable Development Goal (SDG) #3 related to health and wellbeing and SDG #4 related to quality education and lifelong learning opportunities for all. Further research is required to evaluate its long-term scalability and clinical impact. Full article

The burden of disability is rising rapidly in Africa, where a severe shortage of rehabilitation professionals and limited infrastructure create a major treatment gap. Immersive virtual reality and serious games have shown promise for upper limb rehabilitation, but current extended reality (XR) solutions lack personalization, cultural adaptability, real-time feedback, and scalability. This perspective paper proposes a conceptual AI-enhanced XR framework tailored to African low- and middle-income countries. We identify how generative AI, large language models, multiagent systems, and explainable AI can address specific rehabilitation barriers. The framework integrates these four pillars into a three-layer architecture covering content creation, interaction, and decision support. We analyze implementation considerations specific to African contexts—infrastructure, capacity building, cultural adaptation, ethics, and financing—and outline a detailed research agenda with near, medium, and longer term priorities. Realizing this vision requires co-design with African communities, investment in local capacity, adaptation to infrastructure constraints, and development of ethical frameworks. AI-enhanced XR has the potential to democratize access to quality rehabilitation across Africa, but this potential must be validated through rigorous, context-sensitive research. Full article

Background and Objectives: Multiple sclerosis (MS) is characterized by progressive disability and a spectrum of motor and cognitive impairments. Exergames and virtual reality (VR) are proposed as motivating exercise tools, potentially useful for improving adherence and expanding access to rehabilitation. The objectives are to explore the feasibility and safety of a supervised rehabilitation program based on a high-intensity exercise program with immersive virtual reality (IVR) in people with MS and to describe its effects on physical, cognitive, and functional domains, as well as on the serum biomarker neurofilament light chain (sNfL). Materials and Methods: Pre–post exploratory study in five volunteers from a local MS Association [Vigo, Spain]. Intervention: 8 weeks, two sessions/week, 10 min/session of an IVR boxing-based exergame combined with usual rehabilitation, supervised by a physiotherapist. The variables studied were safety (Simulator Sickness Questionnaire [SSQ]), usability (System Usability Scale [SUS]), disability (Expanded Disability Status Scale [EDSS]), gait (25-Foot Walk Test [25FWT]), manual dexterity (9 Hole Peg Test [9HPT]), cognition (Symbol Digit Modalities Test [SDMT]), and axonal damage biomarker (sNfL). Results: The intervention could be feasible and safe (100% adherence, no adverse events (without SSQ symptoms), 95% usability [SUS]). There were positive changes in all variables studied (mean ± SD): EDSS −0.5 ± 0.9; 25FWT −4.9 ± 9.8 s; right 9HPT −3.3 ± 0.9 s; sNfL −4.4 ± 4.5 pg/mL, except for left 9HPT +0.5 ± 5.0 s and cognition (SDMT −2.4 ± 1.3 points). Conclusions: A brief, supervised exercise program combing an IVR exergame with standard rehabilitation was feasible and safe in people with MS. Although the results seem promising with the proposed design, the clinical and biological changes are merely exploratory, and it is not possible to infer their efficacy. Our findings open the door to future controlled studies including perceived high-intensity exercise programs and larger sample sizes to explore efficacy and estimate clinically relevant effect sizes. Full article

Coal mines constitute a vital component of the national security system, where the extraction and utilisation of coal resources directly impact mine stability and engineering safety. Therefore, addressing the surface deformation issues caused by repeated mining activities across multiple coal seams at the Daliuta Mine, this study proposes a multi-source remote sensing monitoring technology system, which aims to improve the accuracy of surface deformation in the mining area. At the mining area scale, optimised Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology utilised 168 Sentinel-1A image scenes to generate 789 interferometric image pairs. This extracted the long-term surface deformation field of the Daliuta mining area, revealing the spatiotemporal evolution patterns of surface subsidence under repeated mining activities. To further enhance monitoring accuracy and reliability, this study proposed a Satellite Aerial-Prior Weighting (SA-PW) method. This approach integrated satellite-based time-series InSAR, aerial Pixel Offset Tracking (POT), and unmanned aerial vehicle light detection and ranging (UAV LiDAR) data through a dynamic priority weighting model. This enabled the synergistic inversion of high-precision surface deformation parameters for repeatedly mined areas. Results demonstrated that compared to SBAS-InSAR alone, the SA-PW method achieved a 10% improvement in surface deformation parameter accuracy. By constructing a dynamic priority-weighted model, this approach systematically integrated multi-source data to achieve collaborative inversion of high-precision surface deformation parameters in repeatedly mined areas. Results demonstrated that compared to SBAS-InSAR and UAV LiDAR methods, SA-PW data fusion yielded higher accuracy, with MAE and RMSE values of 60 mm and 90 mm on the A line, and 57 mm and 83 mm on the H line, respectively. This method facilitates the establishment of integrated air–space–ground real-time monitoring networks for mining areas, enables subsidence hazard early warning and management, identifies key zones for ecological restoration, explores synergistic mechanisms between repeated mining and ecological rehabilitation, and promotes safe and green mining operations and development. Full article

Biomechanics sits at the interface of engineering and medical sciences, offering essential insight into how tissues, organs, and biological systems respond to mechanical loading. This review brings together recent advances in musculoskeletal and cardiovascular biomechanics, illustrating how experimental techniques, computational modeling, and multiscale analysis are used to characterize load transfer, tissue deformation, fatigue, and injury mechanisms. In musculoskeletal applications, predictive simulations, wearable sensing technologies, and neuromechanical assessment tools support improved injury prevention, rehabilitation planning, and assistive device development. In the cardiovascular domain, patient-specific modeling, fluid–structure interaction analyses, and advanced imaging approaches clarify how hemodynamics, vessel wall mechanics, and device–tissue interactions influence disease progression, implant performance, and therapeutic outcomes. Emerging technologies including artificial intelligence, machine learning, digital twin frameworks, biofabrication, soft robotics, and self-powered sensing are enabling data-driven, real-time, and personalized interventions that connect mechanistic understanding with clinical practice. Despite these advances, challenges remain in accounting for individual variability, integrating multiscale data, and translating computational predictions into clinically validated solutions. By emphasizing interdisciplinary strategies that unite biomechanics, computational analytics, and innovative device engineering, this review outlines a pathway toward predictive, patient-centered healthcare and next-generation therapeutic and rehabilitation solutions. Full article

Monitoring center-of-mass is crucial for assessing postural control, but field measurements are often impractical or cost-prohibitive. This study investigates the feasibility of predicting center-of-mass kinematics from the motion of an unstable base—the Sensopro Luna—using deep learning, eliminating the need for wearable sensors. We conducted a cross-sectional study in which 64 participants were recorded performing three coordination exercises (Single-Leg Stance, Stepping, and Waves). Marker-based motion capture and auxiliary inertial sensors were used to record reference and tape kinematics. The model inputs consisted of IMU- and motion-capture-derived tape segment orientations, IMU accelerations and angular velocities, and algorithmic estimates of the lowest tape positions. Nine axis-specific exercise models were developed using a hybrid Encoder–LSTM–Decoder architecture and compared against linear regression baselines. Our results indicate that the deep learning models successfully predicted horizontal center-of-mass displacements (DNN Mean Absolute Errors of 16.1–23.7 mm for X-axis and 4.4–31.3 mm for Y-axis) and exhibited descriptively lower errors than linear models in mean absolute error and signal morphology. However, vertical predictions were less reliable, likely due to the physical constraints inherent to the kinematics of the unstable base. Error analysis revealed that prediction accuracy was highest within common postural ranges, but decreased for extreme displacements. These findings provide a proof-of-concept for wearable-free postural monitoring, particularly for movement along the mediolateral and sagittal axes. Such a system could facilitate automated, cost-effective postural feedback and performance tracking in rehabilitation and fitness environments, supporting autonomous coordination training without the practical constraints of traditional measurement systems. Full article

Robot-assisted telerehabilitation (RAT) combines rehabilitation robotics with digital health workflows to extend access to upper-limb (UL) therapy after stroke. Mixed reality (MR) may support therapist–patient interaction and task visualization; however, early-stage systems require rigorous evaluation of safety and usability before deployment in the home. In a formative, mixed-methods usability study conducted in a controlled setting using a telerehabilitation workflow, six individuals post-stroke (≥3 months) and six occupational therapists (OTs) completed a single supervised session with a desktop-mounted end-effector type therapeutic robot (iTbot) integrated with Microsoft HoloLens 2. Participants performed structured passive and active UL exercises while therapists supervised and interacted with the system via the MR control interfaces. Safety was evaluated by documenting observed adverse events and safety-stop activations. Usability and user experience were assessed using the System Usability Scale (SUS), study-specific satisfaction questionnaires (reported with scale ranges), and semi-structured follow-up interviews analyzed using thematic analysis. All participants completed the session without observed adverse events or safety-stop activations. Overall usability was favorable, with a mean (SD) SUS total score of 78.3 (15.9) out of 100 (stroke: 74.2 [18.1]; occupational therapists: 82.5 [13.5]). Qualitative feedback indicated that MR was perceived as engaging and intuitive by many users, while also identifying implementation needs relevant to real-world telerehabilitation, including clearer onboarding, simplification of certain MR interactions, and improved physical interfaces (e.g., handle options). Therapists highlighted workflow considerations for remote supervision and patient independence. Together, these findings support progression to multi-session, in-home studies to quantify remote assistance needs, technical reliability, adherence, and clinical outcomes. Full article

In recent years, hand exoskeleton robots have attracted extensive attention from researchers and practitioners due to their potential to rehabilitate, assist, and enhance hand movements, particularly for stroke patients. With an ageing population increasingly affected by strokes, there is a growing demand for patient-centred interventions which place less demand on clinicians, especially wearable devices that can enhance hand function. Advances in artificial intelligence have opened new avenues for developing more reliable and adaptive assistive systems. This study presents a systematic literature review, following the PRISMA protocol on the design elements of hand exoskeleton robots, acknowledging the emerging perspectives on AI integration and ethical considerations. The study provides a comprehensive foundation for future research and development in rehabilitation technologies by systematically synthesising the current mechanical architecture, actuation, sensors, material, weight, and cost aspects of soft hand exoskeleton robots for rehabilitation. The results show important patterns and trade-offs in various design dimensions, providing useful information to direct the development of more accessible and efficient rehabilitation solutions in the future. Full article

The number of patients with lower-limb dysfunction is increasing each year due to aging, illness, accidents, and other factors. Without timely rehabilitation and rapid recovery of walking function, further physical and mental deterioration may be accelerated, potentially leading to long-term bedriddenness. This study discusses gait training in rehabilitation therapy from the perspectives of kinesiology, cognitive science, walking function, and safety, and an omnidirectional gait training robot was developed. This study proposed a control system construction method for an omnidirectional gait training robot based on both prescription-based training and autonomous training. In the prescription-based training system, the target values are derived from the training prescription, and the control objective is to guide the patient to walk along the robot’s prescribed path and speed. In the autonomous training system, the target values are automatically generated based on the patient’s walking intentions, and the control objective is for the robot to safely follow the patient’s movement. A necessary condition for robot-assisted autonomous gait training is effective fall prevention. A fall prediction strategy based on foot position information and handrail pressure data was developed. Using this strategy, the robot can predict falls immediately before they occur, similar to a physical therapist, thereby reducing the risk of falls during gait training. Experimental results demonstrate the feasibility of implementing this strategy. Full article

Full-arch implant-supported rehabilitations are widely recognized as an effective treatment option for edentulous patients. Nevertheless, clinical decision-making regarding patient selection, surgical planning, prosthetic material choice, and long-term maintenance protocols remains heterogeneous and requires structured evidence-based guidance. A modified Delphi consensus process was conducted involving 29 experts during the Italian Consensus Conference. A systematic literature review covering the period 2015–2024 was performed, and the certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. Consensus was predefined as ≥90% agreement. Seven evidence-based consensus statements were developed addressing: (1) periodontal risk assessment using validated tools; (2) guided bone regeneration outcomes with technique-specific indications; (3) comparative survival of four versus six implants in mandibular full-arch rehabilitations; (4) equivalence of tilted and axial implant configurations; (5) prosthetic material selection, with monolithic zirconia showing high survival; (6) risk-stratified supportive maintenance protocols associated with a reduction in peri-implantitis incidence; and (7) systemic risk stratification, including absolute and relative contraindications, medication-related osteonecrosis of the jaw (MRONJ) risk management, and perioperative antibiotic prophylaxis. Full article

Background: Speech-language pathology (SLP) and audiology services are essential components of multidisciplinary rehabilitation, particularly for individuals with developmental, neurological, and communication-related disorders. National-level data describing the distribution and utilization of these services in Saudi Arabia remain limited. This study aimed to examine national patterns of rehabilitation service utilization, with a focus on SLP and audiology services in comparison to other rehabilitation specialties. Methods: A retrospective cross-sectional analysis was conducted using publicly available national open data released by the Saudi Ministry of Health (MOH). Aggregated rehabilitation service encounters (n = 1,872,328 to 1,930,695) from 2023–2024 were analyzed by specialty, geographic region, sector (MOH clusters versus private sector), and pediatric age groups. Descriptive statistics were used to characterize utilization patterns and regional variation. Results: Rehabilitation services were widely delivered across both public and private sectors, with physiotherapy representing the largest share of encounters. SLP and audiology services contributed a smaller proportion of total rehabilitation encounters compared to other specialties. Service distribution varied regionally, with higher volumes concentrated in major urban areas including Riyadh, Makkah, and the Eastern Region. Pediatric service encounters were highest in early childhood (ages 3–7), with SLP and audiology services forming a consistent component of rehabilitation during this period. Conclusions: This study provides a descriptive overview of rehabilitation service utilization in Saudi Arabia, highlighting the distribution of SLP and audiology services relative to other specialties and across regions. Findings emphasize the importance of addressing regional variation, supporting workforce development, and enhancing national rehabilitation data systems to inform planning and ensure comprehensive access to communication and hearing services. Full article

Background: Artificial intelligence (AI) is rapidly transforming healthcare, with increasing applications in sports medicine. Advances in machine learning, deep learning, and computer vision enable the analysis of large, heterogeneous datasets derived from imaging, wearable sensors, performance-monitoring systems, and electronic health records. While these technologies offer opportunities to enhance injury prevention, diagnostic accuracy, rehabilitation monitoring, and clinical decision-making, their integration into athlete care remains complex and context-dependent. Methods: A structured narrative review of the PubMed/MEDLINE database was conducted to identify clinically relevant AI applications in sports medicine. The search focused on key domains including injury risk prediction, musculoskeletal imaging, rehabilitation monitoring, return-to-play assessment, performance management, and clinical workflow support. Evidence from original studies, reviews, methodological reports, and regulatory documents was qualitatively synthesized to provide an overview of current applications, methodological limitations, and decision-level implications. Results: AI demonstrates growing utility across multiple domains of sports medicine. Machine learning models can identify complex, non-linear relationships among training load, physiological responses, and injury risk, though their predictive performance varies widely and is often limited by dataset heterogeneity and a lack of external validation. In musculoskeletal imaging, AI-based algorithms support automated detection and quantification of abnormalities, with performance in selected tasks approaching that of expert readers, yet remaining task-specific and context-dependent. Emerging applications include movement analysis and rehabilitation monitoring through wearable sensors and computer vision systems, as well as data-driven support for return-to-play decisions and clinical workflow optimization. However, current evidence highlights important limitations, including algorithmic bias, limited generalizability, poor interpretability, and the risk of misapplication in complex clinical decision-making contexts. Conclusions: AI is likely to become an important decision-support layer in sports medicine by enabling data integration and longitudinal monitoring. However, model performance does not necessarily translate into improved clinical outcomes, and AI-generated predictions remain probabilistic and context-sensitive. Consequently, clinical decisions—particularly high-stakes processes such as return-to-play—require structured integration of AI outputs within a broader clinical framework. The sports physician remains central as a human-in-the-loop integrator, responsible for contextualizing AI-derived information, mitigating potential errors, and ensuring safe, individualized athlete management. Full article