Search Results (443)

This study aimed to investigate the effects of mental fatigue on physical and cognitive performance (lower-limb power, isometric and handgrip strength, and psychomotor vigilance). Twenty-two physically active young adults (12 males, 10 females; M_age = 20.82 ± 1.47) were randomly assigned to either a Mental Fatigue (MF) or Control group (CON). The MF group showed a statistically significant (p = 0.019) reduction in non-dominant handgrip strength, declining by approximately 2.3 kg (about 5%), while no such change was observed in the CON group or in dominant handgrip strength across groups. Reaction time (RT) was significantly impaired following the mental fatigue protocol: RT increased by 117.82 ms, representing an approximate 46% longer response time in the MF group (p < 0.001), whereas the CON group showed a smaller, non-significant increase of 32.82 ms (~12% longer). No significant differences were found in squat jump performance, indicating that lower-limb explosive power may be less affected by acute mental fatigue. These findings demonstrate that mental fatigue selectively impairs fine motor strength and cognitive processing speed, particularly reaction time, while gross motor power remains resilient. Understanding these effects is critical for optimizing performance in contexts requiring fine motor control and sustained attention under cognitive load. Full article

This study explores the potential of craft-based activities in the context of special education, focusing on a papier mâché sculpting workshop implemented at the Special Kindergarten of Komotini, Greece, as part of the Horizon Europe Craeft project. The initiative aimed to assess how such creative activities could enhance the learning experience of children with intellectual and motor impairments, foster socialization, and develop fine motor skills. With reference to literature in art therapy, craft education, and inclusive pedagogy, the study applied a mixed-methods approach combining observation, visual analysis, and a survey. The findings indicate that, despite varied levels of participation based on individual needs, all students engaged meaningfully with the materials and activities. School professionals observed increased student engagement, emotional comfort, and communication, while also identifying the activity as well adapted and replicable in similar contexts. The results highlight the value of crafts in special education, not only as a sensory and cognitive stimulus but also as a means of fostering inclusion and self-expression. The study concludes with a call for further research into the role of tactile materials and hand gestures in relation to specific impairments. Full article

Infants discriminate faces early in the first year, but research on infants’ discrimination of bodies is plagued by mixed findings. Using a familiarisation novelty preference method, we investigated 7- and 9-month-old infants’ discrimination of body postures presented in upright and inverted orientations, and with and without heads, along with relationships with gross and fine motor development. In our initial studies, 7-month-old infants discriminated upright headless postures with forward-facing and about-facing images. Eye tracking revealed that infants looked at the bodies of the upright headless postures the longest and at the heads of upright whole figures for 60–70% of the time regardless of the presence of faces, suggesting that heads detract attention from bodies. In a more stringent test, with similarly complex limb positions between test items, infants could not discriminate postures. With longer trials, the 7-month-olds demonstrated a familiarity preference for the upright whole figures, and the 9-month-olds demonstrated a novelty preference, albeit with a less robust effect. Unlike previous studies, we found that better gross motor skills were related to the 7-month-olds’ better discrimination of upright headless postures compared to inverted postures. The 9-month-old infants’ lower gross and fine motor skills were associated with a stronger preference for inverted compared to upright whole figures. This is further evidence of a configural representation of bodies in infancy, but it is constrained by an upper bias (heads in upright figures, feet in inverted), the test item similarity, and the trial duration. The measure and type of motor development reveals differential relationships with infants’ representations of bodies. Full article

Objective: Retained primitive reflexes are associated with delayed motor and behavioral development in children with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). This study examined the effects of a 12-week structured exercise intervention on reflex integration, motor coordination, and socio-behavioral outcomes in these populations. Method: Fifteen children with ASD (13 boys, 2 girls) and twelve with ADHD (8 boys, 4 girls), aged 6–12 years, participated in rhythmic, balance, and coordination-based exercises. Primitive reflexes, including the asymmetrical tonic neck reflex (ATNR), were assessed using standardized protocols, and fine motor coordination was evaluated using the Finger and Thumb Opposition Test (FOT). Behavioral outcomes were measured using the Social Responsiveness Scale-2 (SRS-2) for the ASD group and the Conners 3 for the ADHD group. Results: The ASD group showed significant reductions in left-standing ATNR retention scores (p = 0.012) and improvements in right-hand FOT scores (p = 0.023). In the ADHD group, significant improvements were also observed in right-hand FOT scores (p = 0.007). Furthermore, Conners 3 Total and Global Index scores significantly decreased in the ADHD group (p = 0.016 and 0.020, respectively). Reflex retention patterns appeared broader and more bilateral in ASD than in ADHD, suggesting distinct motor developmental profiles. Conclusions: Short-term rhythmic, balance, and whole-body coordination exercise interventions may support behavioral and motor development in children with ASD and ADHD. Tailored programs emphasizing reflex integration hold promise for clinical and educational applications. Full article

Background: Traumatic fractures of the cervical spine pose significant challenges in management, particularly in young patients, where preserving mobility is crucial. Patient Characteristics: A 30-year-old woman presented with a C3 lateral mass and pedicle fracture following a motor vehicle collision. Initial conservative management with a rigid cervical collar for three months failed to reduce the diastasis, and the debilitating neck pain worsened. Preoperative imaging confirmed fracture instability without spinal cord compression. Intervention and Outcome: Preoperative screw trajectory planning was conducted with the My Spine MC system (Medacta), and fine-tuning was achieved on a 3D-printed model of the vertebra. A posterior midline approach was employed to expose the C3 vertebra, and a Herbert screw was inserted under fluoroscopic guidance. Imaging at three months demonstrated significant fracture reduction and early bone fusion. The patient achieved substantial improvement in functional mobility without complications. Conclusion: Herbert screw fixation holds potential as a less-invasive alternative to conventional posterior stabilization for selected cervical fractures. This technical note provides the reader with the required information to support surgical planning and execution. Full article

Motor Imagery (MI) classification plays a crucial role in enhancing the performance of brain–computer interface (BCI) systems, thereby enabling advanced neurorehabilitation and the development of intuitive brain-controlled technologies. However, MI classification using electroencephalography (EEG) is hindered by spatiotemporal variability and the limited interpretability of deep learning (DL) models. To mitigate these challenges, dropout techniques are employed as regularization strategies. Nevertheless, the removal of critical EEG channels, particularly those from the sensorimotor cortex, can result in substantial spatial information loss, especially under limited training data conditions. This issue, compounded by high EEG variability in subjects with poor performance, hinders generalization and reduces the interpretability and clinical trust in MI-based BCI systems. This study proposes a novel framework integrating channel dropout—a variant of Monte Carlo dropout (MCD)—with class activation maps (CAMs) to enhance robustness and interpretability in MI classification. This integration represents a significant step forward by offering, for the first time, a dedicated solution to concurrently mitigate spatiotemporal uncertainty and provide fine-grained neurophysiologically relevant interpretability in motor imagery classification, particularly demonstrating refined spatial attention in challenging low-performing subjects. We evaluate three DL architectures (ShallowConvNet, EEGNet, TCNet Fusion) on a 52-subject MI-EEG dataset, applying channel dropout to simulate structural variability and LayerCAM to visualize spatiotemporal patterns. Results demonstrate that among the three evaluated deep learning models for MI-EEG classification, TCNet Fusion achieved the highest peak accuracy of 74.4% using 32 EEG channels. At the same time, ShallowConvNet recorded the lowest peak at 72.7%, indicating TCNet Fusion’s robustness in moderate-density montages. Incorporating MCD notably improved model consistency and classification accuracy, especially in low-performing subjects where baseline accuracies were below 70%; EEGNet and TCNet Fusion showed accuracy improvements of up to 10% compared to their non-MCD versions. Furthermore, LayerCAM visualizations enhanced with MCD transformed diffuse spatial activation patterns into more focused and interpretable topographies, aligning more closely with known motor-related brain regions and thereby boosting both interpretability and classification reliability across varying subject performance levels. Our approach offers a unified solution for uncertainty-aware, and interpretable MI classification. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that impairs motor function, including the fine motor control required for handwriting. Traditional diagnostic methods often lack sensitivity and objectivity in the early stages, limiting opportunities for timely intervention. There is a growing need for non-invasive, accessible tools capable of capturing subtle motor changes that precede overt clinical symptoms. Among early PD manifestations, handwriting impairments such as micrographia have shown potential as digital biomarkers. However, conventional handwriting analysis remains subjective and limited in scope. Recent advances in artificial intelligence (AI) and machine learning (ML) enable automated analysis of handwriting dynamics, such as pressure, velocity, and fluency, collected via digital tablets and smartpens. These tools support the detection of early-stage PD, monitoring of disease progression, and assessment of therapeutic response. This paper highlights how AI-enhanced handwriting analysis provides a scalable, non-invasive method to support diagnosis, enable remote symptom tracking, and personalize treatment strategies in PD. This approach integrates clinical neurology with computer science and rehabilitation, offering practical applications in telemedicine, digital health, and personalized medicine. By capturing dynamic features often missed by traditional assessments, AI-based handwriting analysis contributes to a paradigm shift in the early detection and long-term management of PD, with broad relevance across neurology, digital diagnostics, and public health innovation. Full article

With advancements in computational resources, artificial intelligence has gained significant attention in motor health monitoring. These sophisticated deep learning algorithms have been widely used for induction motor health monitoring due to their autonomous feature extraction abilities and end-to-end learning capabilities. However, in real-world scenarios, challenges such as limited labeled data and diverse operating conditions have led to the application of transfer learning for motor health monitoring. Transfer learning utilizes pretrained models to address new tasks with limited labeled data. Recent advancements in this domain have significantly improved fault diagnosis, condition monitoring, and the predictive maintenance of induction motors. This study reviews state-of-the-art transfer learning techniques, including domain adaptation, fine-tuning, and feature-based transfer for induction motor health monitoring. The key methodologies are analyzed, highlighting their contributions to improving fault detection, diagnosis, and prognosis in industrial applications. Additionally, emerging trends and future research directions are discussed to guide further advancements in this rapidly evolving field. Full article

The decoding of motor imagery (MI) electroencephalogram (EEG) signals is crucial for motor control and rehabilitation. However, as feature extraction is the core component of the decoding process, traditional methods, often limited to single-feature domains or shallow time-frequency fusion, struggle to comprehensively capture the spatio-temporal-frequency characteristics of the signals, thereby limiting decoding accuracy. To address these limitations, this paper proposes a dual-branch neural network architecture with multi-domain feature fusion, the dual-branch spatio-temporal-frequency fusion convolutional network with Transformer (DB-STFFCNet). The DB-STFFCNet model consists of three modules: the spatiotemporal feature extraction module (STFE), the frequency feature extraction module (FFE), and the feature fusion and classification module. The STFE module employs a lightweight multi-dimensional attention network combined with a temporal Transformer encoder, capable of simultaneously modeling local fine-grained features and global spatiotemporal dependencies, effectively integrating spatiotemporal information and enhancing feature representation. The FFE module constructs a hierarchical feature refinement structure by leveraging the fast Fourier transform (FFT) and multi-scale frequency convolutions, while a frequency-domain Transformer encoder captures the global dependencies among frequency domain features, thus improving the model’s ability to represent key frequency information. Finally, the fusion module effectively consolidates the spatiotemporal and frequency features to achieve accurate classification. To evaluate the feasibility of the proposed method, experiments were conducted on the BCI Competition IV-2a and IV-2b public datasets, achieving accuracies of 83.13% and 89.54%, respectively, outperforming existing methods. This study provides a novel solution for joint time-frequency representation learning in EEG analysis. Full article

Introduction: Human immunodeficiency virus (HIV)-associated neurocognitive impairment (NCI) remains a concern despite combination antiretroviral therapy (cART), with cognitive problems often persisting even after viral suppression. The mechanisms underlying neurocognitive deterioration in people living with HIV (PLWH) and the role of plasma biomarkers remain unclear. This study aims to evaluate neurocognitive trajectories and biomarker changes in a real-world cohort of newly diagnosed PLWH initiating cART in Greece. Methods: This prospective, single-center study assessed neuropsychological performance and plasma biomarkers in treatment-naïve PLWH at baseline and 18 months after cART initiation. HIV-associated neurocognitive disorder (HAND) was classified using the Frascati criteria, and plasma biomarkers of inflammation and monocyte activation were measured. Correlations between biomarkers and cognitive performance were analyzed. Results: A total of 39 treatment-naïve PLWH were enrolled in this study. At baseline, 45.7% of participants met criteria for HAND, predominantly, asymptomatic neurocognitive impairment (ANI). Over 18 months, neurocognitive function improved, particularly in speed of information processing, executive function, and visuospatial ability, while verbal fluency, fine motor dexterity, and attention/working memory remained unchanged. Biomarkers of inflammation and monocyte activation decreased following cART, except for neopterin, which increased (10.6 vs. 13 ng/mL, p = 0.002), and plasma NFL (7.5 vs. 7.2 pg/mL, p = 0.54), which remained stable. A negative correlation between monocyte activation markers and cognitive performance was observed only at follow-up, suggesting that systemic inflammation may mask these associations in untreated PLWH. Conclusions: Early cART initiation supports neurocognitive recovery and reduces immune activation in PLWH. The observed correlation between cognitive performance and monocyte activation markers after viral suppression highlights the potential utility of plasma biomarkers in predicting cognitive impairment. Full article

Objectives: The primary objective of this study was to determine whether motor disorders are significantly more prevalent in children with Autism Spectrum Disorder (ASD) without co-occurring genetic or neurological conditions compared to neurotypical children. Another aim was to explore the applicability of the MABC-2 test for assessing motor skills in a Polish cohort of children with ASD. Additionally, this study sought to develop a basic framework for motor skill assessment in children with autism. Methods: This study included 166 Caucasian children, both sexes, aged 5–12 years, without intellectual disability (IQ ≥ 70), without concomitant genetic or neurological disorders, particularly epilepsy or cerebral palsy. The study group consisted of children with ASD (n = 71), and the control group consisted of neurotypical children (n = 95). The participants were assessed with the Movement Assessment Battery for Children–second edition (MABC-2), MABC-2 checklist and the Developmental Coordination Disorder Questionnaire (DCDQ), used as a reference point. Results: The children with ASD obtained significantly lower MABC-2 test results in all subtests in comparison with the control group. The children with suspected or diagnosed coordination disorders were characterized by a significantly greater number of co-occurring non-motor factors than the other participants of this study. MABC-2 test showed greater consistency with DCDQ than with the MABC-2 questionnaire. Conclusions: Children with ASD present a lower level of manual dexterity and balance and greater difficulties in performing tasks, including throwing and catching, in comparison with neurotypical children. The MABC-2 test with the MABC-2 checklist and DCDQ questionnaire constitute a complementary diagnostic tool. Full article

Background/Objectives: Dyskinetic cerebral palsy (DCP) often implies severe motor impairment and risk of health problems. Our aim was to follow up a group of young adults with DCP that we previously examined as children, to describe health, function, and living conditions. Methods: Interviews regarding health issues, treatments, and living conditions, and quality of life (RAND-36) and fatigue questionnaires were completed. Gross and fine motor function, communication, and speech ability were classified, and weight, height, spasticity, and dystonia were assessed and compared to previous data. Joint range of motion (ROM) was compared to older adults with DCP. Results: Dystonia was present in all fifteen participants, and spasticity in all but two. A decrease was found mainly in those who received intrathecal baclofen (ITB). ROM limitations were most pronounced in shoulder flexion, abduction and inward rotation (while outward rotation was hypermobile), hip abduction, hamstrings, and knee extension. The majority had frequent contact with primary and specialist healthcare. Seven participants were underweight, eight had a gastrostomy, and seven had ITB. Upper gastrointestinal and respiratory problems were frequent. Orthopedic surgery for scoliosis was reported in five, and lower extremity in nine, while fractures were reported in six participants. RAND-36 revealed physical functioning, general health, and vitality as the greatest problem areas. Fatigue was significant in 64%. Eight participants lived with their parents. Participants at more functional levels completed tertiary education and lived independently. Conclusions: Most participants had severe impairment and many health issues, despite decreased dystonia and spasticity due to ITB. Sleep problems and pain were uncommon. Full article

Hand gestures are essential for interaction in augmented and virtual reality (AR/VR), allowing users to intuitively manipulate virtual objects and engage with human–machine interfaces (HMIs). Accurate gesture recognition is critical for effective task execution. However, users often encounter difficulties due to the lack of immediate and clear feedback from head-mounted displays (HMDs). Current tracking technologies cannot always guarantee reliable recognition, leaving users uncertain about whether their gestures have been successfully detected. To address this limitation, haptic feedback can play a key role by confirming gesture recognition and compensating for discrepancies between the visual perception of fingertip contact with virtual objects and the actual system recognition. The goal of this paper is to compare a simple vibrotactile ring with a full glove device and identify their possible improvements for a fundamental gesture like pinching and fine moving of objects using Microsoft HoloLens 2. Where the pinch action is considered an essential fine motor skill, augmented reality integrated with haptic feedback can be useful to notify the user of the recognition of the gestures and compensate for misaligned visual perception between the tracked fingertip with respect to virtual objects to determine better performance in terms of spatial precision. In our experiments, the participants’ median distance error using bare hands over all axes was 10.3 mm (interquartile range [IQR] = 13.1 mm) in a median time of 10.0 s (IQR = 4.0 s). While both haptic devices demonstrated improvement in participants precision with respect to the bare-hands case, participants achieved with the full glove median errors of 2.4 mm (IQR = 5.2) in a median time of 8.0 s (IQR = 6.0 s), and with the haptic rings they achieved even better performance with median errors of 2.0 mm (IQR = 2.0 mm) in an even better median time of only 6.0 s (IQR= 5.0 s). Our outcomes suggest that simple devices like the described haptic rings can be better than glove-like devices, offering better performance in terms of accuracy, execution time, and wearability. The haptic glove probably compromises hand and finger tracking with the Microsoft HoloLens 2. Full article

Hemolytic uremic syndrome (HUS), a thrombotic microangiopathy primarily affecting the kidneys, can also involve the central nervous system (CNS), often leading to significant morbidity and mortality. Neurologic manifestations are among the most severe extra-renal complications, particularly in children and during outbreaks of Shiga toxin-producing Escherichia coli (STEC)-associated HUS (typical (tHUS)). This review explores the clinical spectrum, pathophysiology, diagnostic workup, and age-specific outcomes of neurologic involvement in both typical (tHUS) and atypical (aHUS). Neurologic complications occur in up to 11% of pediatric and over 40% of adult STEC-HUS cases in outbreak settings. Presentations include seizures, encephalopathy, focal deficits, movement disorders, and posterior reversible encephalopathy syndrome (PRES). Magnetic resonance imaging (MRI) commonly reveals basal ganglia or parieto-occipital lesions, though subtle or delayed findings may occur. Laboratory workup typically confirms microangiopathic hemolytic anemia (MAHA), thrombocytopenia, and kidney damage, with additional markers of inflammation or metabolic dysregulation. Eculizumab is the first-line treatment for aHUS with CNS involvement, while its utility in STEC-HUS remains uncertain. Although many children recover fully, those with early CNS involvement are at greater risk of developing epilepsy, cognitive delays, or fine motor deficits. Adults may experience lingering neurocognitive symptoms despite apparent clinical recovery. Differences in presentation and imaging findings between age groups emphasize the need for tailored diagnostic and therapeutic strategies. Comprehensive neurorehabilitation and long-term follow-up are crucial for identifying residual deficits. Continued research into predictive biomarkers, neuroprotective interventions, and standardized treatment protocols is needed for improving outcomes in HUS patients with neurological complications. Full article

Electroencephalography (EEG) and surface electromyography (sEMG) signals are widely used in human–machine interaction (HMI) systems due to their non-invasive acquisition and real-time responsiveness, particularly in neurorehabilitation and prosthetic control. However, existing deep learning approaches often struggle to capture both fine-grained local patterns and long-range spatio-temporal dependencies within these signals, which limits classification performance. To address these challenges, we propose a lightweight deep learning framework that integrates adaptive spatial attention with multi-scale temporal feature extraction for end-to-end EEG and sEMG signal decoding. The architecture includes two core components: (1) an adaptive attention mechanism that dynamically reweights multi-channel time-series features based on spatial relevance, and (2) a multi-scale convolutional module that captures diverse temporal patterns through parallel convolutional filters. The proposed method achieves classification accuracies of 79.47% on the BCI-IV 2a EEG dataset (9 subjects, 22 channels) for motor intent decoding and 85.87% on the NinaPro DB2 sEMG dataset (40 subjects, 12 channels) for gesture recognition. Ablation studies confirm the effectiveness of each module, while comparative evaluations demonstrate that the proposed framework outperforms existing state-of-the-art methods across all tested scenarios. Together, these results demonstrate that our model not only achieves strong performance but also maintains a lightweight and resource-efficient design for EEG and sEMG decoding. Full article