Search Results (504)

Background: Manual dexterity (MD) impairment is a frequent and disabling feature in patients with Parkinson’s disease (PD) and paranoid schizophrenia (PS), significantly affecting functional independence and activities of daily living. However, rehabilitation strategies specifically targeting fine motor control remain insufficiently integrated into routine physiotherapy (PT). Objective: This study investigated the effects of a structured, progressive PT program incorporating targeted MD training on upper limb function in patients with PD and PS. Methods: A prospective, exploratory, interventional study was conducted in 30 patients, allocated to either an experimental group (EG, n = 20) or a control group (CG, n = 10). Participants had PD (Hoehn and Yahr stages II–III) or chronic, clinically stable PS. MD was assessed using the Purdue Pegboard Test, Coin Rotation Task, and Kapandji opposition score. The EG completed a four-phase, 40-week dexterity-oriented rehabilitation program, while the CG received standard disease-specific PT. Between-group differences in change scores were analyzed using one-way ANOVA. Results: The EG showed significantly greater improvements than the CG in thumb opposition, psychomotor processing speed, and unilateral and bilateral fine motor performance (p < 0.001 for all), with large to very large effect sizes (η² = 0.45–0.76). No significant between-group differences were observed for complex sequential assembly tasks. Conclusions: Integrating targeted MD training into structured PT programs significantly improves fine motor performance in patients with PD and PS, supporting its inclusion in rehabilitation protocols for residential and outpatient care settings. Full article

Background/Objectives: Neurodegenerative cerebellar ataxia (CA) is a movement disorder caused by progressive cell death in the cerebellum. Motor imagery represents a potential therapeutic tool to improve motor function by “exercising” brain regions associated with movement, without the need for overt activity. This study assessed the feasibility of combining motor imagery with real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) to improve motor function in CA. Methods: During finger tapping conditions, 16 participants with CA pushed a button at the same frequency in time with cross flashing at 1 Hz or 4 Hz, and this information was used to train the model. During motor imagery, participants imagined finger tapping while undergoing rt-fMRI-NF with visual feedback, steering them toward activating their motor circuit. Afterwards, they completed finger tapping again. FMRI analysis compared successful motor imagery trials versus all other imagery events. Brain activity on successful trials was covaried with pre–post rt-fMRI-NF tapping improvement scores. Results: Tapping was more accurate at 1 Hz than 4 Hz, and larger tapping error rates correlated with greater movement impairments. While not significant at the group level, 9 of the 16 participants improved tapping accuracy following rt-fMRI-NF. The size of motor improvements correlated with successful motor imagery activity at 1 Hz in the frontal lobe, insula, parietal lobe, basal ganglia, and cerebellum. Motor improvements were not associated with neurological impairment severity, mood, cognition, or imagery vividness. Conclusions: Feasibility was demonstrated for motor imagery therapy with neurofeedback to potentially improve fine motor precision in people with CA. Brain regions relevant to this process may be considered for targets of non-invasive therapeutic interventions. Full article

Electroencephalography (EEG) has attracted significant attention as an effective modality for interaction between the physical and virtual worlds, with EEG-based person identification serving as a key gateway to such applications. Despite substantial progress in EEG-based person identification, several challenges remain: (1) how to design an end-to-end EEG-based identification pipeline; (2) how to perform automatic electrode selection for each user to reduce redundancy and improve discriminative capacity; (3) how to enhance the backbone network’s feature extraction capability by suppressing irrelevant information and better leveraging informative patterns; and (4) how to leverage higher-level information in EEG signals to achieve intent recognition (i.e., EEG-based task/activity recognition under controlled paradigms) on top of person identification. To address these issues, this article proposes, for the first time, a unified deep learning framework that integrates automatic electrode selection, person identification, and intent recognition. We introduce a novel backbone network, AES-MBE, which integrates automatic electrode selection (AES) and intent recognition. The network combines a channel-attention mechanism with a multi-scale bidirectional encoder (MBE), enabling adaptive capture of fine-grained local features while modeling global temporal dependencies in both forward and backward directions. We validate our approach using the PhysioNet EEG Motor Movement/Imagery Dataset (EEGMMIDB), which contains EEG recordings from 109 subjects performing 4 tasks. Compared with state-of-the-art methods, our framework achieves superior performance. Specifically, our method attains a person identification accuracy of 98.82% using only 4 electrodes and an average intent recognition accuracy of 91.58%. In addition, our approach demonstrates strong stability and robustness as the number of users varies, offering insights for future research and practical applications. Full article

Electric motors are the workhorse of industries owing to their precise speed and torque control technologies. Despite their ruggedness, faults are inevitable due to wear and tear, their prolonged usage and multiple factors. Bearing faults are among the most frequently occurring faults in electric motors. Detecting faults at an early stage is crucial for avoiding complete shutdown. Deep learning has gained significant attention in the fault detection domain owing to its inherent advantages. This paper proposes a hybrid multi-scale convolutional neural network and Transformer model for bearing fault detection. The model combines the strengths of multi-scale convolutional front-ends for fine-grained feature extraction with Transformer encoder blocks for capturing long-range temporal dependencies. This approach combines the advantages of both models for effective bearing fault detection. The proposed method was tested on a bearing dataset to show its performance and efficacy. This method achieved high-performance accuracy in bearing fault detection. Full article

Accurate parameter identification in nonlinear and chaotic dynamic systems requires optimization algorithms that can reliably balance global exploration and local refinement in complex, multimodal search landscapes. To address this challenge, a modified artificial protozoa optimizer (mAPO) is developed in this study by embedding two complementary mechanisms into the original artificial protozoa optimizer: a probabilistic random learning strategy to enhance population diversity and global search capability, and a Nelder–Mead simplex-based local refinement stage to improve exploitation and fine-scale solution adjustment. The general optimization performance and scalability of the proposed framework are first evaluated using the CEC2017 benchmark suite. Statistical analyses conducted over shifted and rotated, hybrid, and composition functions demonstrate that mAPO achieves improved mean performance and reduced variability compared with the original APO, indicating enhanced robustness in high-dimensional and complex optimization problems. The effectiveness of mAPO is then examined in nonlinear system identification applications involving chaotic dynamics. Offline and online parameter identification experiments are performed on the Rössler chaotic system and a permanent magnet synchronous motor, including scenarios with abrupt parameter variations. Comparative simulations against APO and several state-of-the-art optimizers show that mAPO consistently yields smaller objective function values, more accurate parameter estimates, and superior statistical stability. In the PMSM case, exact parameter reconstruction with zero error is achieved across all independent runs, while rapid and smooth convergence is observed under both static and time-varying conditions. Full article

Down Syndrome Regression Disorder (DSRD) is a rare but severe neuropsychiatric condition characterized by abrupt loss of speech, autonomy, and cognitive abilities in individuals with Down syndrome, often associated with immune dysregulation and gut–brain axis dysfunction. We report the case of an 11-year-old girl with Down syndrome who developed developmental regression at age five, in temporal proximity to a family transition (the birth of a younger sibling), with loss of continence, language, and comprehension, alongside persistent behavioral agitation and gastrointestinal symptoms. Laboratory assessment revealed Giardia duodenalis infection, elevated fecal calprotectin and secretory IgA, and microbial imbalance with overgrowth of Streptococcus anginosus and S. sobrinus. The patient received a single oral dose of tinidazole (2 g), daily folinic acid (1 mg/kg), and a 90-day course of transcranial and abdominal photobiomodulation (PBM) (1064 nm, 10 min per site). Post-treatment, stool analysis showed normalized inflammation markers and restoration of beneficial bacterial genera (Bacteroides, Bifidobacterium, Lactobacillus) with absence of Enterococcus growth. Behaviorally, she exhibited marked recovery: CARS-2-QPC decreased from 106 to 91, ABC from 63 to 31, and ATEC from 62 to 57, alongside regained continence, speech, and fine-motor coordination. These outcomes suggest that abdominal and transcranial PBM, by modulating mitochondrial metabolism, mucosal immunity, and microbiota composition, may facilitate systemic and neurobehavioral recovery in DSRD. This translational case supports further investigation of PBM as a non-invasive, multimodal therapy for neuroimmune regression in genetic and developmental disorders including validation through future randomized controlled clinical trials. Full article

Droplet microarrays are increasingly used for miniaturized, high-throughput biochemical assays, yet their fabrication commonly relies on complex lithographic processes, custom masks, or specialized coatings. Here we present a simple method for generating hydrophilic arrays on hydrophobic plastic substrates by combining ultrasonic atomization with off-the-shelf perforated masks. A fine mist of poly(vinyl alcohol) (PVA) solution is directed through commercial diamond sieves onto polypropylene (PP) sheets and polystyrene (PS) sheets, forming hydrophilic spots surrounded by the native hydrophobic background. Static contact angle measurements confirm a strong local contrast in wettability (from 100.85 ± 0.91° on untreated PP to 39.96 ± 0.71° on patterned spots, from 95.68 ± 3.61° on untreated PS to 52.00 ± 0.85° on patterned spots), while Image analysis shows droplet CVs of 6–8% in aqueous dye solutions for 1.2–2.0 mm masks; in complex media (LB), droplet uniformity decreases. By mounting the moving mask on a motorized stage, we generate one-dimensional reagent gradients simply by controlling the moving mask motion during atomization. We further demonstrate biological compatibility by culturing Escherichia coli in LB droplets containing resazurin, and by performing localized antibiotic screening using a moving mask-guided streptomycin gradient. The resulting droplet-wise viability data yield an on-chip dose–response curve with an IC₅₀ of 5.1 µg · mL⁻¹ (95% CI: 4.5–5.6 µg·mL⁻¹), obtained from a single array. Covering droplets with Electronic Fluorinated Fluid maintains volumes within 5% of their initial value over 24 h. Compared with conventional droplet microarray fabrication, the proposed method eliminates custom mask production and cleanroom steps, is compatible with standard plastic labware, and intrinsically supports spatial gradients. These attributes make masked ultrasonic atomization a practical platform for high-throughput microfluidic assays, especially in resource-limited settings. Full article

Background and Objectives: Evidence on motor performance in children with autism spectrum disorder (ASD) is scarce and inconsistent. The association of motor impairments with autism severity and intelligence remains insufficiently studied. We aimed to examine motor performance parameters in children with ASD compared with typically developing (TD) peers. Materials and Methods: In this cross-sectional study, a convenience sample of 26 children with ASD, aged 4–10 years, was recruited from specialized centers in KSA, alongside 27 age- and sex-matched TD children. For the ASD group, severity (Childhood Autism Rating Scale, CARS-2) and intelligence quotient (Stanford–Binet Intelligence Scale, SB5) were extracted from medical records. CARS-2 score was utilized to categorize children with ASD into two groups (mild-to-moderate and severe groups). All study children were assessed for gross and fine motor skills using the Movement Assessment Battery for Children-2 (MABC-2), balance, muscle strength, endurance, and flexibility. Results: ASD groups recorded significantly lower scores in all MABC-2 component areas when compared to the TD group (p < 0.001). Aiming and catching percentile was significantly lower in the severe ASD group compared to the mild-to-moderate group (p = 0.05). Furthermore, children with ASD exhibited increased hypermobility, predominantly at the elbow joints, reduced grip strength, shorter distance in the modified 6 min walk test, and lower standing long-jump performance (p < 0.001) when compared to TD group; however, no significant difference was recorded between the ASD groups. Spearman correlation revealed that aiming and catching was negatively correlated with autism severity (CARS-2) (r = −0.38, p = 0.05) and positively with IQ (r = 0.51, p = 0.03). Aiming and catching was positively correlated with grip strength (r = 0.55, p = 0.003), endurance (r = 0.58, p = 0.002), and jump distance (r = 0.44, p = 0.03), while balance was positively correlated with grip strength (r = 0.44, p = 0.02). Conclusions: Children with ASD exhibit significant impairments in gross and fine motor performance compared with TD peers, accompanied by hypermobility, reduced strength, and diminished endurance. Notably, aiming and catching ability correlated with both IQ and autism severity as well as specific motor parameters, suggesting its potential as a clinical marker of motor–cognitive interaction in ASD. Full article

Background: Chronic post-stroke spasticity often limits gait despite best-practice botulinum-toxin intramuscular injections (BTIs), whose benefit is constrained by short duration, dose ceilings, and tachyphylaxis. Cryoneurolysis (CNL) induces a reversible axonotmesis with preserved endoneurium, potentially providing longer tone reduction with fewer adverse effects, but its impact on whole-gait quality and its compatibility with implanted functional electrical stimulation (FES) remain poorly documented. Case presentation: A 43-year-old man, 12 years after right middle cerebral artery stroke, walked independently with an implanted common peroneal FES system but complained of effortful gait with left-knee “locking” and drop foot without FES. Multiple BTI series to triceps surae and quadriceps yielded only transient benefit. Two ultrasound-guided CNL sessions targeted tibial (soleus, medial gastrocnemius) and femoral (rectus femoris, vastus intermedius) motor branches. Quantitative gait analysis and fine-wire electromyography (EMG) were performed at baseline, 6 weeks after each CNL, and at 6 months, with and without FES. CNL produced immediate and sustained reductions in triceps surae and quadriceps overactivity, resolution of genu recurvatum, normalization of stiff-knee gait, improved ankle dorsiflexion, and increased swing phase knee flexion (>50°). Gait Deviation Index rose from 69 to 80 and Gillette Gait Index decreased by more than 50%, with preserved strength and without adverse events. Conclusions: Targeted, sequential CNL of tibial and femoral motor branches can safely deliver durable, clinically meaningful gait improvements when BTI has reached its ceiling and can act synergistically with implanted FES. Quantitative gait analysis and EMG sharpen clinical decision-making in spasticity management. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive and motor decline. Early detection remains challenging, as traditional neuroimaging and neuropsychological assessments often fail to capture subtle, preclinical changes. Recent advances in digital health and artificial intelligence (AI) offer new opportunities to identify non-invasive biomarkers of cognitive impairment. In this study, we propose an AI-driven framework for early AD based on handwriting motion data captured using a sensor-integrated Smart Pen. The system employs an inertial measurement unit (MPU-9250) to record fine-grained kinematic and dynamic signals during handwriting and drawing tasks. Multiple machine learning (ML) algorithms—Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbors (kNN)—and deep learning (DL) architectures, including one-dimensional Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-BiLSTM network, were systematically evaluated. To address data scarcity, we implemented a Sim-to-Real Domain Adaptation strategy, augmenting the training set with physics-based synthetic samples. Results show that classical ML models achieved moderate diagnostic performance (AUC: 0.62–0.76), while the proposed hybrid DL model demonstrated superior predictive capability (accuracy: 0.91, AUC: 0.96). These findings underscore the potential of motion-based digital biomarkers for the automated, non-invasive detection of AD. The proposed framework represents a cost-effective and clinically scalable informatics solution for digital cognitive assessment. Full article

Action quality assessment automates the evaluation of human movement proficiency, which is vital for applications like sports training and rehabilitation, where objective feedback enhances patient outcomes. Action quality assessment processes motion capture data to generate quality scores for action execution. In rehabilitation exercises, joints typically work synergistically in functional groups. However, existing methods struggle to accurately model the collaborative relationships between joints. Fixed joint grouping is not flexible enough, while fully adaptive grouping lacks the guidance of prior knowledge. In this paper, based on rehabilitation theory in clinical medicine, we propose a dynamic, motion-aware grouping strategy. A two-stream architecture independently processes joint position and orientation information. Fused features are adaptively clustered into 6 functional groups by a joint motion energy-driven learnable mask generator, and intra-group temporal modeling and inter-group spatial projection are achieved through two-stage attention interaction. Our method achieves competitive results and obtains the best scores on most exercises of KIMORE, while remaining comparable on UI-PRMD. Experimental results using the KIMORE dataset show that the model outperforms current methods by reducing the mean absolute deviation by 26.5%. Ablation studies validate the necessity of dynamic grouping and the two-stream design. The core design principles of this study can be extended to fine-grained action-understanding tasks such as surgical operation assessment and motor skill quantification. Full article

Children with Developmental Coordination Disorder (DCD) experience motor competence challenges that hinder their participation in physical activities and affect daily functioning. While traditional motor skills training is commonly used, sport-based interventions offer the potential for greater benefits by providing dynamic, contextually rich environments for learning. This study aimed to evaluate the effectiveness of mini-handball training versus conventional motor skills training in improving coordination in children with DCD. Methods: Forty-four children aged 9–10 years from Khorezm, Uzbekistan, with coordination difficulties (scores below the 16th percentile in the MABC-2) were randomly assigned to three groups: mini-handball training (n = 15), motor skills training (n = 15), and control (n = 14). Both intervention groups participated in three 90 min sessions per week for 12 weeks. The mini-handball group engaged in sport-specific drills including passing, dribbling, shooting, and small-sided games, while the motor skills group performed balance, locomotor, and fine motor exercises. Pre- and post-intervention assessments were conducted using the MABC-2. Data were analyzed using linear mixed models with time, group, and their interaction as fixed effects. Results: Both intervention groups demonstrated significant improvements in motor coordination compared to controls. However, in general, the mini-handball group outperformed the other groups, particularly in domains requiring anticipatory control and visuomotor integration, including aiming and catching, balance, and overall coordination scores. Conclusions: Mini-handball represents a promising, ecologically valid intervention for children with DCD. By integrating motor skills practice with cognitive challenge, social interaction, and intrinsic motivation within a meaningful sport context, mini-handball appears more effective than traditional training approaches. These findings suggest that sport-based, open-skill interventions should be considered in therapeutic protocols, school curricula, and community programs for children with DCD. Future research should examine long-term retention, transfer to daily activities, and implementation across diverse populations. Full article

Background: Population ageing and the growing prevalence of chronic diseases, particularly stroke, have negative repercussions on fine motor function, compromising the independence of older adults. The Specialist Nurse in Rehabilitation Nursing plays a central role in functional recovery and in improving quality of life. This study aims to describe the process of developing and validating the design of rehabilitation nursing care for older adults with impaired fine motor function. Methods: This paper is a three-phase methodological study conducted between January and July 2025: (1) initial development of the design of rehabilitation nursing care for older adults with impaired fine motor function; (2) validation of the content of the proposed design, using the modified e-Delphi technique; and (3) development of the final model of the care design. Results: The e-Delphi study, involving a panel of 15 experts, allowed the content validation of the design of rehabilitation nursing care for older adults with impaired fine motor function after two rounds. Following the suggestions, the final care design model, in relation to fine motor function, comprises five steps: (1) collection of relevant data, (2) identification of possible nursing diagnoses, (3) definition of objectives, (4) planning and implementation of interventions, and (5) evaluation of outcomes. As part of step 4, photographic records of exercises focused on the recovery of fine motor function were included. Conclusions: The final model of the design of rehabilitation nursing care for older adults with impaired fine motor function, developed and validated in this study, may serve as a guiding framework in the delivery of specialised care to this population. Full article

Children with Down syndrome (DS) show considerable variability in social-communication and cognitive profiles, and a subset meet criteria for co-occurring autism. In the present study, we examined the associations between developmental domains and autistic trait severity in toddlers with DS. Participants included 38 toddlers (M = 4.19 years, SD = 0.99) who completed a home-based assessment, including measures of language, fine motor, and visual reception skills. Caregivers also completed standardized questionnaires on communication and executive functioning. Multiple regression analyses tested the degree of association between these developmental domains and autistic traits. Fewer words produced fewer gestures, and more impaired fine motor and visual reception scores were significantly associated with higher autism trait severity, whereas executive function domains were not significantly associated. Preliminary findings indicate that variability in language and nonverbal developmental skills contributes to the expression of autism traits in DS, underscoring the need for early, multidomain assessment approaches to support accurate identification and tailored intervention. Full article

The rapid evolution of autonomous vehicles necessitates increasingly sophisticated cognitive capabilities to handle complex, unstructured environments. This study explores the cognitive potential of Large Language Models (LLMs) in autonomous navigation and swarm control systems, addressing the limitations of traditional rule-based approaches. The research investigates whether multimodal LLMs, specifically a customized version of LLaVA 7B (Large Language and Vision Assistant), can serve as a central decision-making unit for autonomous vehicles equipped with cameras and distance sensors. The developed prototype integrates a Raspberry Pi module for data acquisition and motor control with a main computational unit running the LLM via the Ollama platform. Communication between modules combines REST API for sensory data transfer and TCP sockets for real-time command exchange. Without fine-tuning, the system relies on advanced prompt engineering and context management to ensure consistent reasoning and structured JSON-based control outputs. Experimental results demonstrate that the model can interpret real-time visual and distance data to generate reliable driving commands and descriptive situational reasoning. These findings suggest that LLMs possess emerging cognitive abilities applicable to real-world robotic navigation and lay the groundwork for future swarm systems capable of cooperative exploration and decision-making in dynamic environments. These insights are particularly valuable for researchers in swarm robotics and developers of edge-AI systems seeking efficient, multimodal navigation solutions. Full article