Search Results (1,967)

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

Background/Objectives: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impairments in social communication and the presence of restricted and repetitive behaviors, often accompanied by motor impairments. Previous research indicates that regular physical exercise may reduce autism-related behaviors and improve motor competence. This study aimed to examine the effects of an adapted aquatic exercise program on autism-related behaviors, flexibility, and handgrip strength, key motor functions relevant to daily functioning. Methods: In this parallel-group randomized controlled trial, 35 boys with mild autism spectrum disorder (aged 8.4 ± 2.1 years) were enrolled. Participants were randomly assigned to an exercise group (n = 17) and a control group (n = 18). The exercise group completed a 16-week adapted aquatic exercise program (2 sessions/week, 50 min/session), while the control group received usual education only. The primary outcome was autism-related behaviors assessed by the Autism Behavior Checklist (ABC); secondary outcomes included flexibility and handgrip strength. Results: The exercise group showed significant improvements in Autism Behavior Checklist (ABC) scores, flexibility, and handgrip strength compared with the control group (p < 0.05). Large effect sizes were observed across all outcomes (partial eta squared, ηp² > 0.14). These findings indicate that adapted aquatic exercise confers beneficial effects on behavioral and motor outcomes in children with mild ASD. Conclusions: Regular participation in adapted aquatic exercise reduces autism-related behaviors and improves flexibility and handgrip strength. These findings provide empirical support for the inclusion of aquatic exercise in intervention programs targeting children with ASD and may inform future research and practice. Full article

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

Diagnosing bearing faults remains a crucial challenge, particularly in effectively extracting fault information and achieving high diagnostic accuracy. To address this issue, this study presents a model for diagnosing bearing faults, which comprises three primary stages: feature extraction, feature selection, and classification. In the feature extraction stage, features are extracted from raw motor signals using empirical mode decomposition (EMD) and fast Fourier transform (FFT). In the feature selection stage, an effective method based on binary grey wolf optimization (BGWO) and the equilibrium optimizer (EO) is developed to remove redundant and irrelevant features. Finally, k-nearest neighbours (KNNs) and support vector machine (SVM) classifiers are used to identify bearing fault conditions. The proposed model is evaluated using four datasets: the University of California, Irvine (UCI) benchmark datasets, a motor bearing fault current-signal dataset, the Case Western Reserve University (CWRU) benchmark dataset, and the Machinery Failure Prevention Technology (MFPT) benchmark dataset. The experimental results show that the proposed method improves bearing fault diagnosis accuracy and demonstrates strong robustness compared with conventional methods. Full article

Reaction time (RT) is widely used as a fundamental indicator of central nervous system processing speed. Numerous studies have shown that RT increases with age, generally interpreted as a decline in information processing efficiency. However, most previous studies have focused on absolute RT values, and it remains unclear whether aging also alters the relative relationships between responses under different task conditions. The present study investigated whether aging affects the relative difference between inside and outside pedal reaction times in a Foot Psychomotor Vigilance Test (Foot PVT). A total of 44 participants were analyzed, including 20 younger adults (24 ± 3 years) and 24 older adults (73 ± 5 years). Participants responded to visual stimuli by pressing either the left or right pedal with the right foot. The difference between inside and outside RT (dRT) was calculated for each participant as an index of relative response structure. Group comparisons and correlation analyses were conducted to examine associations with age, height, physical activity level (PAL), and sleep-related factors. As expected, RTs were consistently longer in older adults across conditions. In contrast, dRT did not differ significantly between younger and older groups, with negligible effect sizes (|d| < 0.1). Furthermore, dRT showed no significant correlations with height, PAL, or sleep-related indices. These findings indicate that while aging affects the overall speed of motor responses, the relative temporal structure between response conditions is preserved. This dissociation between global slowing and stable response structure may represent a fundamental characteristic of neuromotor aging. Full article

Background/Objectives: Evidence on the role of muscle mechanical (myotonic) properties in athletic performance remains limited in young adult and sub-elite populations, particularly in American football, and sex-specific patterns of association are not well understood. This study aimed to investigate the associations between lower extremity myotonic properties and performance outcomes (strength and balance) in American football athletes, with a specific focus on sex-related differences and candidate predictors. Methods: A cross-sectional design was implemented involving 35 American football athletes (17 female, 18 male). Lower extremity muscle tone, stiffness, and elasticity were assessed using MyotonPRO. Strength parameters (lower limb, handgrip, back, and shoulder internal rotation) and balance performance (static and dynamic under eyes-open and eyes-closed conditions) were evaluated using standardized measurement protocols. Pearson correlation analysis was conducted to examine bivariate associations, followed by Least Absolute Shrinkage and Selection Operator (LASSO) regression to determine candidate predictors while addressing multicollinearity. Results: Male athletes exhibited significantly greater height, body mass, and BMI (p < 0.001), alongside elevated myotonic values compared to females. Correlation analyses indicated distinct sex-specific association patterns between myotonic properties and performance metrics. LASSO regression revealed a distinct sex-specific divergence in strength prediction: female strength was predominantly driven by proximal musculature (quadriceps and hamstring elasticity/stiffness), whereas male strength was anchored by distal musculature (gastrocnemius tone/stiffness). Furthermore, rigorous penalization shrunk nearly all balance coefficients to zero in both sexes, indicating that resting myotonic properties do not independently predict dynamic or static postural control. Conclusions: While lower extremity myotonic properties are candidate predictors of multi-regional strength via sex-specific proximal and distal strategies, they do not independently predict balance performance, suggesting postural control relies primarily on active motor recruitment rather than passive resting mechanics. Given the cross-sectional design of this study, causal inferences cannot be drawn, and these findings should be interpreted accordingly. The observed sex-specific differences may support consideration of individualized, sex-informed training strategies in American football athletes. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder with an increasing incidence rate that significantly affects patients’ motor functions and quality of life. Involuntary upper limb tremors (ULTs) commonly manifest unilaterally, affecting either the left or right upper limb. Clinically, ULT frequencies can be categorized into three distinct classes: low-frequency (<4.0 Hz), mid-frequency (4.0–7.0 Hz), and high-frequency (>7.0 Hz) tremors. These tremor motions are characterized by oscillatory or rotational (angular displacement) movements, commonly referred to as the micro-Doppler effect (mDE). This study aims to develop a short-range (<1.0 m) and contactless sensing method for ULT detection based on Doppler millimeter-wave (mm-Wave) radar. The reflected electromagnetic waves indicate time-varying frequency characteristics, which can be analyzed by using time–frequency transform (TFT) methods, such as the Wigner–Ville distribution (WVD) and smoothed pseudo WVD (SPWVD). These TFT methods are employed to extract mDE features, which are subsequently visualized as color-coded spectrograms for ULT classification. Then, a two-dimensional (2D) convolutional neural network (CNN) is employed to automatically recognize the visual feature patterns for ULTs classification based on frequency and amplitude information. In the experimental setup, the W-band (76–81 GHz) Doppler mm-Wave biosensor is implemented for sensing and extracting feature patterns. The proposed classifiers based on “WVD + 2D CNN” and “SPWVD + 2D CNN” are trained and validated by using the collected datasets, with 60% randomly selected for training datasets and 40% for testing datasets in each fold validation. A 10-fold cross-validation method is applied to evaluate the classifier’s performances, achieving an average precision of 95.92 ± 0.60%, average recall of 95.89 ± 0.62%, average F1-score of 0.9588 ± 0.0060, and average accuracy of 95.89 ± 0.62%, respectively. The experimental results demonstrate the feasibility of the proposed classifier for real-time ULTs classification in PD patients using short-range (<1.0 m) and contactless sensing. Full article

The human spinal cord is increasingly recognized as an active and adaptable component of sensorimotor function, contributing to motor control, pain modulation, and recovery after neurological injury. Within this framework, the Hoffmann reflex (H-reflex) has evolved from a classical electrophysiological phenomenon into a useful probe of spinal circuit function. Rather than reflecting motoneuron excitability alone, H-reflex amplitude and modulation arise from the interaction of Ia afferent transmission, presynaptic inhibition, homosynaptic depression, and interneuronal networks that regulate sensorimotor gain in a state-dependent manner. This review synthesizes classical and contemporary evidence to position the H-reflex as an indirect measure of spinal inhibitory function in humans. We integrate physiological mechanisms with findings from studies in chronic pain syndromes, spasticity, Parkinson’s disease, and recovery after central nervous system injury, where alterations in spinal inhibitory processes have been described. We further discuss methodological and conceptual challenges that limit clinical translation, including state dependence, protocol heterogeneity, and the lack of normative reference frameworks. Finally, we outline directions for integrating H-reflex paradigms with complementary approaches to improve the interpretation of spinal circuit function and its relation to clinical phenomena. Framed in this context, the H-reflex can be considered a valuable experimental and translational tool, whose utility depends on careful methodological implementation and physiologically informed interpretation. Full article

Objective: Despite increasing use of behavioural interventions in paediatric swallowing and feeding care, the evidence base remains limited and difficult to interpret due to small sample sizes, heterogeneous interventions, diverse outcome measures, and variability in study populations. This review and meta-analysis, therefore, aimed to evaluate the effects of behavioural interventions and botulinum toxin injections on drooling, swallowing, feeding, and oral-motor outcomes in children, based exclusively on randomised controlled trials (RCTs). Methods: Systematic searches were conducted in CINAHL, Embase, and PubMed to identify RCTs. Pharmacological and surgical interventions were excluded, except for botulinum toxin injections, which were analysed as a distinct intervention category given their widespread clinical use in paediatric drooling management. Methodological quality was assessed using the Revised Cochrane Risk of Bias tool (RoB 2). Random-effects meta-analyses were performed, with prediction intervals calculated to account for between-study heterogeneity and to assess the expected range of effects in comparable future studies. Results: Twenty-eight studies were included. Behavioural interventions demonstrated moderate-to-large effects on oral-motor outcomes, whereas botulinum toxin injections demonstrated the strongest effects on drooling. Outcomes measured using multi-item caregiver-reported tools showed larger effects. No significant effects were observed for swallowing or feeding outcomes. Effect sizes varied by age, outcome measure, and respondent type, indicating systematic sources of variation in intervention effects. Prediction intervals indicated substantial clinical and methodological variability, suggesting that intervention effects are context-dependent and not consistently generalisable across populations and settings. Conclusions: Intervention effectiveness in paediatric swallowing and feeding is domain-specific, with consistent benefits observed for drooling and oral-motor outcomes but not for swallowing or feeding. Outcomes are strongly influenced by the measurement approach. High-quality, standardised RCTs targeting specific functional domains are needed to strengthen the evidence base and inform clinical decision making. However, substantial variability in intervention effects across studies suggests that treatment effectiveness may vary with population characteristics, intervention approaches, and outcome measurement methods. Full article

Tool wear directly affects surface quality, dimensional accuracy, and manufacturing cost in milling operations, making reliable wear state classification essential for process control. This paper presents an offline deep learning framework for multiclass tool wear classification using the UC Berkeley milling dataset (NASA-Ames). Statistical features are extracted from vibration, acoustic emission, and spindle motor current signals, and dimensionality is reduced from 78 to 9 informative variables using LASSO regression. A four-layer Long Short-Term Memory (LSTM) network then models the temporal evolution of tool degradation across three wear states: healthy, degraded, and failed. Two model variants are compared: Model A uses sensor-derived features only, while Model B additionally incorporates feed rate and depth of cut as inputs. To prevent data leakage, partitioning is performed at the machining-case level rather than at the individual window level. Model A achieves 92% classification accuracy; Model B reaches 95%, demonstrating that cutting conditions provide contextual information that resolves ambiguity between wear states produced under different machining regimes. These results confirm that combining multisensor feature fusion, LASSO-based selection, and sequential deep learning constitutes an effective framework for tool wear classification in milling. Full article

Electroencephalography (EEG) is widely used in brain-computer interfaces (BCIs) for decoding motor imagery (MI) signals. However, existing methods remain limited in extracting multi-scale local spatiotemporal features and effectively integrating them with global feature information, leaving room for further improvement in classification accuracy. To address this issue, we propose HMT-Net, a hybrid architecture that integrates multi-scale convolution, the Mamba state-space model, and a self-attention mechanism. The model consists of a shallow feature embedding (SFE) module for spatiotemporal feature extraction, a multi-scale local feature extractor (MSLFE), and a Mamba–transformer global feature encoder (MTGFE). Specifically, the MSLFE employs dual-branch convolutions and channel attention to achieve adaptive multi-scale perception, while the MTGFE combines Mamba’s linear sequence modeling capability with multi-head attention to efficiently capture global dependencies. Unlike conventional Mamba or transformer EEG models, HMT-Net couples linear state-space modeling with global pairwise attention, avoiding the representational limits inherent in each individual architecture. Experiments on the BCI-IV-2a, BCI-IV-2b, and HGD datasets show that HMT-Net achieves subject-dependent accuracies of 84.07%, 89.60%, and 96.02%, respectively, outperforming EEGNet, FBCNet, EEGConformer, and ATCNet by 11.65%, 5.02%, 5.13%, and 6.60%, respectively, on BCI-IV-2a. Furthermore, HMT-Net achieves the best accuracy in subject-independent experiments, demonstrating strong generalization capability. Ablation studies and visualizations further validate the effectiveness and interpretability of the proposed model. Full article

Smartphone-based augmented reality (AR) exercise systems show promise for supporting physical activity among older adults, yet the effect of presentation-layer information density on motor performance and cognitive workload in this population remains poorly understood. This study investigated how varying feedback density affects exercise correctness, error correction latency, and perceived workload in community-dwelling older adults (N = 60, aged 65–74 years) performing marching in place under three conditions: MIN, MOD, and RICH. The movement detection algorithm and binary correctness signal $C\left(t\right)$ were held invariant across conditions, isolating presentation-layer density as the sole manipulated variable. One-way repeated-measures ANOVA revealed significant density effects on all three outcomes. MOD produced the highest exercise correctness (M = 74.72%), shortest error correction latency (M = 2.45 s), and lowest perceived workload (M = 41.40); RICH yielded pronounced degradation across all measures. These findings provide preliminary empirical evidence consistent with a Capacity-Relative Density Equilibrium (CRDE) perspective, a conceptual framework that proposes performance as a zone-structured function of the demand-to-capacity ratio (D/K). The framework remains tentative and requires further empirical operationalization due to the lack of a direct measure of cognitive capacity (K). From this perspective, we identify three potential design principles, actionable sufficiency, density threshold, and dual-task alignment, as practical heuristics for mobile AR systems targeting older adult populations. Full article

Low back pain (LBP) is a major global health problem and can result in a variety of movement impairments. Advances in smart technology have enabled the collection of novel streams of movement data, and machine learning (ML) methods have been increasingly used for data analysis. However, many existing technologies remain expensive and unsuitable for widespread clinical use, and ML approaches have largely focused on distinguishing people with LBP from healthy controls rather than identifying meaningful subgroups within the LBP population. Motion Tape (MT) is a recently developed wearable strain sensor that translates skin deformation from underlying movement and muscle engagement into electrical signals. In this exploratory study involving 10 participants with LBP, we demonstrate that MT data from six sensors applied on the lower back capture rich movement information capable of characterizing movement patterns among participants with LBP. We propose a feature engineering approach based on biomechanical features as well as time-series causal discovery applied to multivariate sensor time-series data to extract directed inter-segment coordination patterns. We further develop an exploratory subgroup discovery pipeline by aggregating clustering coassociation information across diverse movement tasks. Our causal coordination features show promising discriminative information across several movement types, capturing aspects of motor control not reflected in amplitude-based or embedding-based features alone, such as asymmetries and movement restrictions. Preliminary ensemble clustering analysis indicates three potential LBP subgroups distinguished by biomechanical and inter-segment coordination patterns, which may reflect varied strategies under different movement demands. We investigate the differences in clinical characteristics among these LBP subgroups. We show that time-series foundation models are not well suited for LBP subgrouping due to their uninterpretability, which is improved in our feature engineering pipeline. This framework could reveal additional subgroups with larger cohorts and may generalize to other sensor modalities. Full article

To address global knowledge bias, weak rare-fault recognition, and high edge-deployment costs caused by heterogeneous sample sizes, data quality, fault categories, and monitoring modalities among multiple clients, this paper proposes a rare-fault-preserving federated dynamic model slimming method based on relational knowledge. The core idea is to formulate lightweight federated diagnosis as a joint optimization problem of rare-fault knowledge preservation and redundant knowledge suppression. At each local client, output-discriminative knowledge, class-prototype relations, and input-sensitive relations are extracted to describe diagnostic knowledge from the decision, structure, and weak-response levels. At the federated server, a rare-fault-aware weighting mechanism adjusts the contribution of local knowledge according to sample scarcity, output reliability, and distribution dispersion and then fuses multi-granularity relational knowledge to optimize the global teacher model. A relation-constrained gated slimming strategy is further designed for the student model, enabling the lightweight model to retain critical diagnostic channels while suppressing repetitive and low-contribution information. Experiments on the CWRU bearing dataset and the HUST multimodal motor dataset show that the proposed method achieves higher diagnostic accuracy, rare-fault recall, and deployment efficiency under composite imbalance, cross-condition generalization, and modality-missing deployment scenarios. These results demonstrate the effectiveness of the proposed method for raw-data-free and privacy-aware multi-client motor fault diagnosis. Full article

Background: Extremely and very preterm infants are at high risk for adverse neurodevelopmental outcomes. Early prediction remains challenging when relying on static clinical markers or single time-point neuroimaging. Serial cranial ultrasound (CUS) enables repeated bedside assessment of cerebral growth and may provide longitudinal trajectory biomarkers integrable with routine clinical data. Methods: In this retrospective two-center cohort study, 89 preterm infants (<32 weeks’ gestation and/or <1500 g birth weight) were assessed using the Bayley-III at 24 months corrected age. Brain volume trajectory features were derived from serial CUS using a standardized ellipsoid model. A three-level analytical framework was applied as follows: univariate regression (62 models, Bonferroni and Benjamini–Hochberg correction), multivariate SVM classification with five-fold GroupKFold cross-validation, ensuring patient-level data separation and feature importance analysis with interaction characterization using stratified Spearman correlation and two-dimensional partial dependence plots. Results: Multivariate classification yielded modest but above-chance performance (balanced accuracy 0.277–0.463, Cohen’s κ 0.042–0.152). Respiratory morbidity duration—mechanical ventilation and BPD severity—were the most robustly associated univariate predictors, surviving Bonferroni correction. Brain volume trajectory features showed no significant univariate associations but contributed conditionally within the multivariate framework as follows: the interaction between brain volume slope and trajectory linearity was the strongest for cognitive outcome (Δr = 0.47), and postnatal growth restriction showed amplified adverse effects at lower birth weight for motor outcome (Δr = 0.47). Conclusions: This study demonstrates the value of ML methods as structured analytical tools for characterizing predictor–outcome relationships in preterm neurodevelopment; respiratory morbidity and brain volume trajectory features emerged as the most informative predictor classes. Prospective multicenter validation is required before clinical translation. Full article