Search Results (506)

Introduction: Stroke remains one of the leading causes of long-term disability worldwide. Post-stroke motor recovery depends on neuroplasticity, which is stimulated by intensive, repetitive, and task-specific training. Modern technologies such as robotic rehabilitation (RR), virtual reality (VR), functional electrical stimulation (FES), brain–computer interfaces (BCIs), and non-invasive brain stimulation (NIBS) offer novel opportunities to enhance rehabilitation. They operate through sensory feedback, neuromodulation, and robotic assistance which promote neural reorganization and motor relearning. Neurobiological Basis of Motor Recovery: Mechanisms such as long-term potentiation, mirror neuron activation, and cerebellar modulation underpin functional reorganization after stroke. Literature Review Methodology: A narrative review was conducted of studies published between 2005 and 2025 using PubMed, Scopus, Web of Science, Cochrane Library, and Google Scholar. Randomized controlled trials, cohort studies, and systematic reviews assessing the efficacy of these modern technologies were analyzed. Literature Review: Evidence indicates that RR, VR, FES, BCIs, and NIBS improve upper and lower limb motor function and strength, and enhance activities of daily living, particularly when combined with conventional physiotherapy (CP). Furthermore, integrated rehabilitation technologies (IRT) demonstrate synergistic neuroplastic effects. Discussion: Modern technologies enhance therapy precision, intensity, and motivation but face challenges related to cost, standardization, and methodological heterogeneity. Conclusions: RR, VR, FES, BCIs, NIBS, and IRT are effective complements to CP. Early, individualized, and standardized implementation can optimize neuroplasticity and functional recovery. Full article

Background/Objectives: Reports detailing rehabilitative interventions for infants with severe dermatologic disorders are scarce. Epidermolysis Bullosa (EB) is a genetic disorder characterized by skin fragility, which causes blistering after minor trauma. Since there is still no cure in general clinics, symptomatic treatment and developmental support are essential for managing the condition. While physiotherapy and occupational therapy guidelines for EB exist, descriptions of neonatal habilitation/rehabilitation are insufficient. Case: This case report describes the longitudinal habilitation/rehabilitation intervention process for a newborn with Dowling–Meara EB, the most severe form, from admission to the Neonatal Intensive Care Unit (NICU) until discharge. Since maneuvers requiring contact were strictly limited due to skin vulnerability, rehabilitation interventions were implemented utilizing the opportunity afforded by necessary care. Intervention strategies were modified according to developmental stages and skin stability, with a particular emphasis on sensory development, postural control training, and fostering the mother–child relationship. This report is the first to describe the applicability of sensory rehabilitation and the use of behavioral cues to facilitate voluntary movements. In addition, careful respiratory rehabilitation was implemented for comorbid tracheomalacia with specific attention to skin vulnerability. The child achieved stable head/neck control, symmetrical limb movements, reaching, guided rolling, and stable oxygenation by the time of discharge. Conclusions: Balancing skin disorder prevention and motor–neural development requires flexible approaches that minimize contact while utilizing routine care as a training opportunity. Our experience will contribute to the progress in the habilitation, wound rehabilitation and respiratory rehabilitation of infants with severe dermatologic disorders. Full article

Rehabilitation after anterior cruciate ligament reconstruction cannot be reduced to a linear, time-based sequence of protection, strength, and return to sport. Persistent asymmetries, quadriceps inhibition, and variable re-injury rates highlight that recovery is a complex adaptive process in which outcomes emerge from dynamic interactions between biological, neural, and psychological subsystems. Grounded in complexity science and chaos theory, this editorial reframes rehabilitation as the regulation of variability rather than its suppression. The Control–Chaos Continuum provides a practical structure to translate this concept into progressive exposure, where clinicians dose uncertainty as a therapeutic stimulus. Adaptive periodization replaces rigid stages with overlapping macro-blocks that respond to readiness, feedback, and context. Neuroplastic mechanisms and ecological dynamics justify the deliberate introduction of controlled “noise” to foster coordination, confidence, and resilience. Ultimately, the goal is not perfect control but stable performance under variability—the ability to function “at the edge of chaos.” This conceptual perspective articulates a clinically actionable framework—linking the Control–Chaos Continuum with adaptive periodization—to guide non-linear decision-making and safe return-to-sport. Full article

Dysphagia is commonly assessed with qualitative and image-based diagnostic tools, which are often costly, technically demanding, and limited in their ability to support individualized rehabilitation. Electroencephalography (EEG) has recently emerged as a quantitative, cost-effective, and accessible alternative to characterize sensorimotor activity during swallowing, though its potential in dysphagic populations has not been systematically explored. This study investigated neural dynamics in 50 post-stroke dysphagic patients, 32 post-stroke non-dysphagic controls, and 21 healthy adults performing a swallowing task. EEG recordings from primary motor regions (C3, Cz, C4) were analyzed using event-related spectral perturbation (ERSP) to quantify alpha (8–13 Hz) and beta (15–30 Hz) event-related desynchronization, alongside hemispheric lateralization indices. Group comparisons revealed significantly reduced beta desynchronization in both post-stroke groups compared to healthy participants, with additional alpha and beta deficits at C3 and Cz distinguishing dysphagic patients from non-dysphagic controls. Dysphagic patients further exhibited abnormal lateralization not observed in other groups. These findings identify distinct alterations in motor cortical dynamics and hemispheric balance in dysphagia, supporting EEG-derived biomarkers as promising tools for diagnosis and clinical follow-up. The accessibility of EEG reinforces its potential integration into routine workflows to enable objective and personalized management of post-stroke dysphagia. Full article

Olfactory dysfunction (OD) is an early symptom associated with a variety of diseases, including COVID-19, Alzheimer’s disease, and Parkinson’s disease, where patients commonly experience hyposmia or anosmia. Effective restoration of olfactory function is therefore crucial for disease diagnosis and management, and improving overall quality of life. Traditional treatment approaches have primarily relied on medication and surgical intervention. However, recent advances in bionic sensing and brain–computer interface (BCI) technologies have opened up novel avenues for olfactory rehabilitation, facilitating the reconstruction of neural circuits and the enhancement of connectivity within the central nervous system. This review provides an overview of the current research landscape on OD-related diseases and highlights emerging olfactory restoration strategies, including olfactory training (OT), electrical stimulation, neural regeneration, and BCI-based approaches. These developments lay a theoretical foundation for achieving more rapid and reliable clinical recovery of olfactory function. Full article

This study aimed to evaluate the effectiveness of a multimodal trust assessment approach that integrated electroencephalography (EEG) and self-report questionnaires compared with unimodal methods within the context of lower-limb rehabilitation training. Twenty-one mobility-impaired participants performed tasks using handrails, walkers, and stairs. Synchronized EEG, questionnaire, and behavioral data were collected. EEG trust scores were derived from the alpha-beta power ratio, while subjective trust was assessed via questionnaire. An adaptive neuro-fuzzy inference system was used to fuse these into a composite score. Analyses included variance, correlation, and classification consistency against behavioral ground. Results showed that EEG-based scores had higher dynamic sensitivity (Spearman’s ρ = 0.55) but greater dispersion (Kruskal–Wallis H-test: p = 0.001). Questionnaires were more stable but less temporally precise (ρ = 0.40). The fused method achieved stronger behavioral correlation (ρ = 0.59) and higher classification consistency (κ = 0.69). Cases with discordant unimodal results revealed complementary strengths: EEG captured real-time neural states despite motion artifacts, while questionnaires offered contextual insight prone to bias. Multimodal fusion through fuzzy logic mitigated the limitations of isolated assessment methods. These preliminary findings support integrated measures for adaptive rehabilitation monitoring, though further research with a larger cohort is needed due to the small sample size. Full article

The integration of neural interfaces with assistive robotics has transformed the field of prosthetics, rehabilitation, and brain–computer interfaces (BCIs). From brain-controlled wheelchairs to Artificial Intelligence (AI)-synchronized robotic arms, the innovations offer autonomy and improved quality of life for people with mobility disorders. This article discusses recent trends in brain–computer interfaces and their application in robotic assistive devices, such as wheelchair-mounted arms, drone control systems, and robotic limbs for activities of daily living (ADLs). It also discusses the incorporation of AI systems, including ChatGPT-4, into BCIs, with an emphasis on new innovations in shared autonomy, cognitive assistance, and ethical considerations. Full article

Neurodegenerative diseases, including Alzheimer’s disease (AD) and Parkinson’s disease (PD), present a growing public health challenge globally. Recent advancements in neurotechnology and neuroengineering have significantly enhanced brain–computer interfaces, artificial intelligence, and organoid technologies, making them pivotal instruments for diagnosis, monitoring, disease modeling, treatment development, and rehabilitation of various diseases. Nonetheless, the majority of neural interface platforms focus on unidirectional control paradigms, neglecting the need for co-adaptive systems where both the human user and the interface continually learn and adapt. This selected review consolidates information from neuroscience, artificial intelligence, and organoid engineering to identify the conceptual underpinnings of co-adaptive and symbiotic human–machine interaction. We emphasize significant shortcomings in the advancement of long-term AI-facilitated co-adaptation, which permits individualized diagnostics and progression tracking in Alzheimer’s disease and Parkinson’s disease. We concentrate on incorporating deep learning for adaptive decoding, reinforcement learning for bidirectional feedback, and hybrid organoid–brain–computer interface platforms to mimic disease dynamics and expedite therapy discoveries. This study outlines the trends and limitations of the topics at hand, proposing a research framework for next-generation AI-enhanced neural interfaces targeting neurodegenerative diseases and neurological disorders that are both technologically sophisticated and clinically viable, while adhering to ethical standards. Full article

Resting hand tremor is a low-frequency, involuntary oscillation influenced by mechanical and neural factors, often manifesting as inter-limb asymmetry. Therefore, the aim of this study was to investigate whether a single complex hand proprioceptive task can acutely modulate tremor in healthy young adults and whether it can induce asymmetry between limbs. Fifty participants (age: 25.0 ± 2.5 years) completed a 40-min proprioceptive task (anteroposterior, mediolateral, clockwise, and counterclockwise), with bilateral resting tremor recorded via triaxial accelerometry before and immediately after the intervention on both dominant and non-dominant limbs. Frequency-domain analysis showed a significant (p < 0.001) increase in tremor amplitude and a small decrease in mean frequency in the 2–4 Hz band immediately after the complex hand proprioceptive task for both limbs. These findings provide novel evidence that a single, wearable-based protocol can transiently modulate tremor dynamics, supporting the use of a non-invasive tool for neuromuscular monitoring in sport, rehabilitation, and clinical practice. Full article

The air-conditioner noise in a rehabilitation room can seriously affect the mental state of patients. However, the existing single-layer active noise control (ANC) filters may fail to attenuate the complicated harmonic noise, and the deep recursive ANC method may fail to work in real time. To solve the problem, in a bending-pipe model, a new hybrid adaptive self-loading filtered-x least-mean-square (ASL-FxLMS) and convolutional neural network-gate recurrent unit (CNN-GRU) network is proposed. At first, based on the recursive GRU translation core, an improved CNN-GRU network with multi-head attention layers is proposed. Especially for complicated harmonic noises with more or fewer frequencies than harmonic models, the attenuation performance will be improved. In addition, its structure is optimized to decrease the computing load. In addition, an improved time-delay estimator is applied to improve the real-time ANC performance of CNN-GRU. Meanwhile, an adaptive self-loading FxLMS algorithm has been developed to deal with the uncertain components of complicated harmonic noise. Moreover, to achieve balance attenuation, robustness, and tracking performance, the ASL-FxLMS and CNN-GRU are connected by a convex combination structure. Furthermore, theoretical analysis and simulations are also conducted to show the effectiveness of the proposed method. Full article

Despite advances in diagnostics, the objective and repeatable assessment of patients with neurological deficits (e.g., stroke) remains a major challenge. Modern methods based on artificial intelligence (AI) are of interest to researchers and clinicians in this area. This study presents a comparative analysis of different AI approaches used to analyze gait of stroke patients using a retrospective dataset of 120 individuals. The main objective is to evaluate the effectiveness, accuracy, and clinical relevance of machine learning (ML) and deep learning (DL) models in identifying gait abnormalities and predicting rehabilitation outcomes. Multiple AI techniques—including support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), and convolutional neural networks (CNN)—were trained and tested on time-series gait data with spatiotemporal parameters. Performance metrics such as accuracy, precision, recall, and area under the curve (AUC) were used to compare model results. Initial results indicate that DL models, particularly CNNs, outperform traditional ML methods in capturing complex gait patterns and providing reliable classification. However, simpler models showed advantages in interpretability and computational efficiency. This study highlights the potential and shortcomings of AI-based gait analysis tools in supporting clinical decision-making and planning personalized stroke rehabilitation. Full article

Accurately predicting the muscle strength of key muscle–tendon units during human motion is vital for understanding movement mechanisms, optimizing exercise training, evaluating rehabilitation progress, and advancing prosthetic control technologies. Traditional prediction methods often suffer from low accuracy and high computational complexity. To address these challenges, this study employs independent component analysis (ICA) to predict the muscle strength of tendon units in primary moving parts of the human body. The proposed method had the highest accuracy in localization, at 98% when the sample size was 20. When the sample size was 100, the proposed method had the shortest localization time, with a localization time of 0.025 s. The accuracy of muscle strength prediction based on backpropagation neural network for key muscle–tendon units in human motion was the highest, with an accuracy of 99% when the sample size was 100. The method can effectively optimize the accuracy and efficiency of muscle strength prediction for key muscle–tendon units in human motion and reduce computational complexity. Full article

This paper introduces the Gesture-Based Physical Stability Classification and Rehabilitation System (GPSCRS), a low-cost, non-invasive solution for evaluating physical stability using an Arduino microcontroller and the DFRobot Gesture and Touch sensor. The system quantifies movement smoothness, consistency, and speed by analyzing “up” and “down” hand gestures over a fixed period, generating a Physical Stability Index (PSI) as a single metric to represent an individual’s stability. The system focuses on a temporal analysis of gesture patterns while incorporating placeholders for speed scores to demonstrate its potential for a comprehensive stability assessment. The performance of various machine learning and deep learning models for gesture-based classification is evaluated, with neural network architectures such as Transformer, CNN, and KAN achieving perfect scores in recall, accuracy, precision, and F1-score. Traditional machine learning models such as XGBoost show strong results, offering a balance between computational efficiency and accuracy. The choice of model depends on specific application requirements, including real-time constraints and available resources. The preliminary experimental results indicate that the proposed GPSCRS can effectively detect changes in stability under real-time conditions, highlighting its potential for use in remote health monitoring, fall prevention, and rehabilitation scenarios. By providing a quantitative measure of stability, the system enables early risk identification and supports tailored interventions for improved mobility and quality of life. Full article

This research presents a preliminary proposal for a rehabilitation exercise aimed at patients with muscle weakness in the distal upper limb. A virtual environment was developed, where the user engages in a rehabilitation activity focused on rehabilitating the pinch grip. The goal is to strengthen the patient’s grasp and reduce muscle weakness. The virtual environment was designed as a video game in order to generate greater interest and encourage patients to adhere to their rehabilitation activities. This virtual game utilizes the haptic device Novint Falcon for the interaction with the environment. This preliminary work implements an impedance control with neural compensation; the control strategy produces signals to adapt the force exerted by the patient, with the goal that the device can give a force of the same magnitude but in the opposite direction. Consequently, regardless of the patient’s initial strength, the device will always deliver an assistive force to guide the patient along a desired trajectory. Initial experimental results with the proposed virtual-haptic rehabilitation system are presented, indicating the feasibility of the approach; however, further studies are required to validate its clinical effectiveness. Full article

Introduction: Stroke remains a major cause of disability worldwide, and precise identification of stroke lesions is essential for prognosis and rehabilitation planning. Machine learning has emerged as a powerful tool for automating stroke lesion segmentation and outcome prediction; however, these tasks are often studied in isolation. The two strategies are inherently interdependent since segmentation provides lesion-based features that directly inform prediction models. Methods: This narrative review synthesises studies published between 2010 and 2024 on the application of machine learning in stroke lesion segmentation and recovery forecasting. A total of 23 relevant studies were reviewed, including 10 focused on lesion segmentation and 13 on recovery prediction. Results: Convolutional Neural Networks (CNNs), including architectures such as U-Net, have improved segmentation accuracy on the Anatomical Tracings of Lesions After Stroke (ATLAS) V2 dataset; however, dataset bias and inconsistent evaluation metrics limit comparability. Integrating imaging-derived lesion characteristics with clinical features improves predictive accuracy at a higher level. Furthermore, semi-supervised and self-supervised methods enhanced performance where annotated datasets are scarce. Discussion: The review highlights the interdependence between segmentation and outcome prediction. Reliable segmentation provides biologically meaningful features that underpin recovery forecasting, while prediction tasks validate the clinical relevance of segmentation outputs. This bidirectional relationship underlines the need for unified pipelines integrating lesion segmentation with outcome prediction. Future research can improve generalisability and foster clinically robust models by advancing semi-supervised and self-supervised learning, bridging the gap between automated image analysis and patient-centred prognosis. Conclusion: Accurate lesion segmentation and outcome prediction should be viewed not as separate goals but as mutually reinforcing components of a single pipeline. Progress in segmentation strengthens recovery forecasting, while predictive modelling emphasises the clinical importance of segmentation outputs. This interdependence provides a pathway for developing more effective, generalisable, and relevant AI-driven stroke care tools. Full article