Search Results (462)

Objective: This study aimed to compare the neuromuscular control characteristics of international- and national-level squash players during forehand strokes using a multichannel surface electromyography (sEMG)-based sensing framework. By integrating wearable biosignal acquisition with muscle synergy and intermuscular coherence analyses, this study sought to identify sensor-derived markers of performance-related neuromuscular control and to provide evidence for sensor-informed squash training and athlete monitoring. Methods: Participants performed standardized forehand strokes, during which multichannel sEMG signals were synchronously collected from major upper-limb, lower-limb, and trunk muscles. The recorded sensor signals were preprocessed and analyzed using non-negative matrix factorization to extract muscle synergies, including the number of synergies, muscle weightings, and synergy activation durations. In addition, time–frequency intermuscular coherence analysis was performed on the sEMG sensor data to quantify coherence differences in the α, β, and γ frequency bands between upper-limb–trunk and lower-limb–trunk muscle pairs. Results: No significant difference was found between the two groups in the number of muscle synergies, with both groups clustering into four synergy modules. However, the sEMG sensor-based analysis revealed clear between-group differences in synergy structure and coordination patterns. International-level players showed higher muscle weightings in major proximal muscles, including the deltoid, pectoralis major, erector spinae, and gluteus maximus, and lower weightings in relatively smaller or more distal muscles such as the biceps brachii and lateral gastrocnemius. In terms of synergy timing, international-level players exhibited significantly shorter activation durations in SYN1 and SYN2, but a significantly longer activation duration in SYN3, than national-level players. For intermuscular coherence, international-level players showed significantly lower coherence in the α, β, and γ bands for multiple upper-limb–trunk and lower-limb–trunk muscle pairs. Conclusions: A multichannel sEMG sensing approach was effective in detecting performance-level differences in neuromuscular control during the squash forehand stroke. International-level players exhibited more efficient and refined neuromuscular coordination, characterized by optimized proximal muscle recruitment, more task-specific synergy timing, and reduced intermuscular coherence across selected muscle pairs. These findings highlight the value of wearable EMG sensors and sensor-based neuromuscular feature extraction for quantitative athlete assessment, movement monitoring, and the development of sensor-guided training strategies in squash. Full article

Background: The Forward Lunge is a representative squash-specific footwork movement involving rapid acceleration, braking, postural stabilization, and return propulsion. This study examined whether pre-competition neuromuscular electrical stimulation (NMES) combined with weighted squats was associated with differences in Forward Lunge performance and neuromuscular control in squash athletes. Methods: Thirty-six male squash athletes were randomly assigned to three groups: Weighted Squats, Fake Stimulation, and Real Stimulation, with 12 participants in each group. After the assigned acute intervention, all participants completed the squash-specific star test. Completion time was recorded using a Microgate Witty photocell timing system, while surface electromyographic (sEMG) signals from 14 right-side muscles were collected using a Delsys Trigno wireless electromyography system. High-speed video was used to identify the Forward Lunge movement cycle, and transistor–transistor logic (TTL) synchronization enabled temporal alignment among timing, video, and sEMG signals. Normalized root mean square (RMS), muscle co-activation index (CI), and non-negative matrix factorization (NMF)-based muscle synergy parameters were calculated. Between-group differences were analyzed using one-way analysis of variance (ANOVA) with Bonferroni post hoc comparisons, and false discovery rate (FDR) correction was applied to secondary neuromuscular outcomes. Results: Star test completion time differed significantly among the three groups (F = 28.65, p < 0.001, η² = 0.63). The Real Stimulation group showed a shorter completion time (10.35 ± 0.45 s) than the Weighted Squats group (11.80 ± 0.55 s) and Fake Stimulation group (11.55 ± 0.50 s). During the Forward Lunge movement cycle, normalized RMS values of the rectus abdominis (ABS; F = 18.56, p < 0.001, η² = 0.55) and latissimus dorsi (LD; F = 13.42, p < 0.001, η² = 0.44) were significantly higher in the Real Stimulation group. The gluteus maximus–biceps femoris (GLM–BF) co-activation index also differed significantly among groups (F = 58.42, p < 0.001, η² = 0.78), with higher values in the Real Stimulation group. Muscle synergy analysis showed group differences in selected muscle activation weights and temporal activation parameters. Conclusions: In this parallel-group acute intervention study based on post-intervention measurements, real NMES combined with weighted squats was associated with shorter star test completion time and altered neuromuscular control during the Forward Lunge movement cycle. The integrated use of photocell timing, wireless sEMG, high-speed video, and TTL synchronization provided temporally aligned sensor-based evidence for evaluating acute pre-competition activation strategies. However, due to the absence of baseline measurements, the findings should be interpreted as post-intervention between-group differences rather than definitive evidence of individual improvement. Full article

Background/Objectives: Accurate and accessible assessment of upper-limb motion is essential for rehabilitation research, ergonomic evaluation, human–machine interaction, and limb exercise. This work presents a comparative evaluation of upper-limb joint angle estimation obtained from wearable inertial measurement units (IMUs) using a Kinect-based practical benchmark during synchronized data acquisition. Methods: The main variables analyzed were shoulder and elbow joint angles, together with IMU-derived acceleration and surface electromyography (sEMG) signals acquired as complementary physiological information during task execution. Ten healthy adult participants performed predefined upper-limb movements while data from both sensing modalities were recorded simultaneously. Joint angles were estimated independently from IMU and Kinect measurements and compared using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Two One-Sided Tests (TOST) equivalence analysis. Results: For upper- limb motion, IMU-derived estimates showed practical equivalence within the predefined ±10° acceptance margin with small MAE and RMSE values and significant TOST equivalence results (p < 0.001), supporting reliable proximal joint tracking under controlled conditions. Tested elbow motion exhibited large estimation error and large variability, and although the TOST analysis was significant, the equivalence interval slightly exceeded the predefined acceptance bound, indicating comparatively weak agreement between sensing modalities. The presented results should be interpreted as proof-of-concept evidence derived from a comparative benchmark rather than as definitive validation for unrestricted or clinical implementation. The synchronized acceleration and sEMG signals provided complementary temporal information regarding movement execution but were not treated as primary comparative outputs. Conclusions: These findings support the feasibility of wearable IMU-based upper-limb joint angle estimation as a proof-of-concept comparative framework rather than definitive clinical validation. The presented findings support the feasibility of the proposed IMU-based sensing approach for upper-limb joint angle estimation, particularly at the shoulder level, while also highlighting the greater complexity of elbow-related measurements. Further investigation in larger samples, more functionally diverse tasks, and broader populations is required to extend the applicability of the proposed approach. Full article

This paper proposes a two-stage hybrid framework for biosignal quality validation that produces beat-level or segment-level labels for real-time filtering and offline dataset curation. The framework is quantitatively validated exclusively on ECG data. Its modular architecture is designed to extend to further non-stationary periodic biomedical time-series signals including photoplethysmography (PPG), impedance cardiography (ICG), phonocardiography (PCG), electromyography (EMG), and electroencephalography (EEG) through modality-specific parameter adaptation; however, this broader applicability currently reflects architectural extensibility rather than experimentally validated performance. A prerequisite is synchronized acquisition of the primary biosignal together with inertial motion sensing (IMU/accelerometer) and electrode impedance or lead-off status, with the IMU positioned near the sensing electrodes. The first stage performs sensor-integrity gating to reject intervals corrupted by motion or poor electrode contact. The second stage applies software signal quality indices to the remaining beats, including physiological plausibility constraints (R to R peaks analysis), DTW-based morphological consistency against adaptive templates, frequency domain SNR estimation, and baseline wander quantification. This study systematically evaluates and compares the classification performance of six complementary sensor-level and software-based signal quality assessment methods. When integrated within the proposed hybrid framework, validation against expert-annotated ECG quality labels from 20 healthy participants demonstrates high methodological classification accuracy (98.1%), achieving approximately a 98% F1-score, 99% sensitivity, and 97% specificity. Prospective validation on patient populations with cardiovascular pathology is identified as a necessary step toward clinical deployment. This modular approach improves the reliability of downstream analysis by preventing corrupted data from entering feature extraction and model training pipelines, enabling more stable physiological monitoring in free-living conditions, reducing false alarms in continuous monitoring applications, and generating higher-quality datasets for AI-based diagnostic systems. Full article

Smartphone-holder use during motorcycling is increasingly common, but its task-dependent ergonomic effects remain insufficiently understood. This study examined visual, postural, and muscular responses during smartphone-holder use under a simulated riding-posture condition. Forty healthy adults completed five smartphone-use tasks: dynamic viewing, static viewing, texting, seated use, and standing use. Each riding-related task condition lasted one minute, with the final 30 s designated as the stable data collection window. For postural variables, instantaneous values were recorded at four time points (0, 10, 20, and 30 s from the onset of the stable window) and averaged. For electromyography (EMG), integrated EMG (IEMG) was computed over the same 30 s window using ten consecutive non-overlapping 3 s epochs, and averaged for normalization. The neck flexion (NF), upper thoracic angle (UTA), gaze angle (GA), viewing distance (VD), and electromyographic activities of the cervical erector spinae (CES) and upper trapezius (UTZ) were measured using integrated motion-analysis and EMG approaches. Two-way mixed ANOVA and repeated-measures correlation analyses were performed. The task condition significantly affected all measured variables, with effect sizes ranging from moderate to large (all ηp² ≥ 0.155), with texting producing the greatest NF, shortest VD, and highest muscle activation. Strong within-subject associations were identified among visual, postural, and muscular variables across riding-related tasks (VD–NF: r = −0.815, p < 0.001). Females exhibited higher CES and UTZ activation than males. These findings reveal a task-dependent visual–postural–muscular co-variation pattern during scooter-mounted smartphone-holder use and support the application of a sensor-based ergonomic assessment for characterizing task-dependent visual–postural–muscular responses during scooter-mounted smartphone-holder use. Full article

Mastication and swallowing are complex physiological processes involving the coordinated activity of multiple tissues in the oral cavity, facial region, and laryngeal system. Some detection methods suffer from limitations such as insufficient information acquisition and inadequate temporal feature analysis. To address these issues, this study proposes a conceptual method for analyzing the state of masticatory and swallowing movements. It integrates maxillofacial electromyographic (EMG) signals with laryngeal movement signals. The goal is to preliminarily explore state analysis of masticatory and swallowing movements over time. A designed gain-adjustable conditioning circuit processes and acquires these signals: maxillofacial EMG signals from EMG electrodes and laryngeal movement signals from flexible PVDF piezoelectric sensors. These two signal streams complement each other’s missing information, enabling comprehensive detection of the state of masticatory and swallowing movements. To address time-point labeling in mastication and swallowing, a sliding-window-based dispersion calculation method was employed to extract characteristic signal nodes, which were then accurately associated with their corresponding physiological motion states. We combined temporal features such as the zero point, onset of fluctuations, characteristic peaks, and baseline recovery from electromyographic (EMG) signals and laryngeal movement signals. This allowed us to establish a correspondence between key time points in the mastication and swallowing processes. The coefficient of determination (R²) for the pressure–voltage linear fit of the PVDF flexible piezoelectric sensor was 0.99446. The pressure resolution was approximately 0.08 kPa. Response times were no more than 15 ms for the EMG channel and no more than 10 ms for the PVDF pressure channel. These results indicate that this method is feasible for extracting oral movement time parameters in healthy subjects. Full article

Social robots hold promise for enhancing later-life support, but user needs and preferences for multimodal interaction modalities remain underexplored. This study explores awareness, willingness, perceived barriers, and modality–function associations across multiple interaction modalities among middle-aged and older adults, and proposes a conceptual five-layer architecture for design guidance. A questionnaire survey with 199 Chinese respondents (aged 45–64: 89.4%, 65+: 10.6%) examined perceptions of voice, visual, gestural, affective, sEMG, and brain–computer interface interactions. Voice and visual modalities were the most preferred; gesture and affective interactions were moderately accepted; awareness of sEMG was high but may reflect confusion with other sensor technologies; and BCI awareness and willingness were low. Based on survey findings and the literature, a conceptual five-layer architecture is presented to inform future social-robot design. The sample predominantly comprised middle-aged participants, so findings reflect prospective later-life users rather than the broader older-adult population. This study offers user-centered insights into multimodal social-robot interaction and provides design implications for future development rather than evaluating emotional-health interventions. Full article

Background/Objectives: Industry 5.0 emphasizes the protection and empowerment of human workers. Passive wearables reduce physical strain, but the evaluation of their efficacy remains incomplete when based solely on kinematics or electromyographic (EMG) envelope amplitude, failing to capture the underlying neural “cost” or the compensatory strategies. This paper proposes a computational framework centered on muscle synergy analysis to bridge the gap between laboratory-grade neural assessment and real-world industrial applications. The goal is to move beyond simple biomechanical metrics toward a deeper understanding of neural coordination during device interaction. Methods: Given the practical limitations of high-density EMG in industrial settings, we propose a “streamlining” approach: laboratory-derived synergy models guide the understanding of neural processes and the selection of a minimal set of sensors capable of detecting maladaptive motor compensations and early signs of fatigue. Results: This approach allows for long-term monitoring without compromising natural movement. By decoupling neural strategies from kinematic output, “silent” risk situations can be identified even when movement appears correct but the neural coordination is altered by the passive device. This supports personalized ergonomic indices and predictive prevention protocols, transforming wearables from simple mechanical aids into intelligent, human-centric systems. Conclusions: This framework provides a roadmap for translating complex motor control theories into practical tools for the next generation of safe and sustainable manufacturing. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder affecting millions worldwide, characterized by motor symptoms such as tremor, rigidity, and bradykinesia that significantly impair daily life. The current diagnosis and monitoring rely primarily on clinical observations and rating scales (e.g., the MDS-UPDRS), which are subjective and limited in detecting subtle motor alterations, leading to inter- and intra-rater variability. In recent years, wearable sensors such as surface electromyography (sEMG) and inertial measurement units (IMUs) have emerged as non-invasive tools for quantifying neuromuscular activity and motor performance in PD. When combined with machine learning (ML) and deep learning (DL) techniques, these signals enable the development of models for disease detection, patient classification, and symptom severity assessment. This review provides a structured overview of recent ML and DL approaches applied to surface electromyography for PD monitoring, addressing a gap in the current literature. It analyzes data acquisition strategies, preprocessing techniques, feature extraction methods, model architectures, and evaluation protocols across tasks such as diagnosis, tremor analysis, freezing of gait detection, and gait assessment. Despite promising results, key challenges remain, including limited dataset size, lack of standardization, and poor generalization. Finally, this work highlights emerging trends and identifies a representative processing pipeline to support real-world clinical translation. Full article

Abnormal gait associated with neuromuscular and musculoskeletal disorders represents a growing clinical burden, particularly in aging populations. This study presents a modular, low-cost Smart Rehabilitation Walker (SRW) that integrates multimodal sensing and real-time haptic feedback to enable simultaneous gait monitoring and corrective intervention in both clinical and home environments. The system combines force-sensing resistors for bilateral load symmetry assessment, inertial measurement units for fall detection, and surface electromyography (sEMG) for neuromuscular activity monitoring within a closed-loop assistive feedback architecture. A 15-day pilot study involving ten individuals with rheumatoid arthritis and clinically observed neurological gait abnormalities demonstrated measurable improvements in gait biomechanics. The Force Symmetry Index (FSI), calculated using the Robinson symmetry metric, decreased from an average of 0.9691 to 0.2019, corresponding to a 79.26% average reduction in inter-limb load asymmetry. Concurrently, sEMG measurements showed a substantial increase in neuromuscular activation (ΔEMG = 4.28), with statistical analysis confirming a significant improvement across participants (paired t-test: t(9) = 13.58, p < 0.001). To model rehabilitation trajectories, a nonlinear predictive framework based on Gaussian Process Regression achieved high predictive accuracy (R² ≈ 0.9, with a mean RMSE of 0.0385), while providing uncertainty-aware trend estimation. Validation using an independent amyotrophic lateral sclerosis gait dataset further demonstrated the transferability of the analytical pipeline. These results highlight the potential of sensor-enabled assistive walkers as scalable platforms for quantitative gait rehabilitation, adaptive feedback, and long-term mobility monitoring. Full article

Precise monitoring of forearm muscle groups is crucial for decoding motor intentions in human–machine interfaces (HMIs) and rehabilitation. However, traditional surface electromyography (sEMG) electrodes face significant challenges in densely packed muscle regions with large skin deformations, leading to severe signal crosstalk and unstable contact. Here, we report a flexible, low-cost 16-channel copper electrode array system designed for the high-density monitoring of multiple forearm muscle activities. Through a facile fabrication process, rigid copper is transformed into a conformable sensing interface. The optimized serpentine interconnects endow the array with excellent stretchability and effectively isolate motion-induced stress, ensuring high-quality signal acquisition under complex deformations. The high-density 2 × 8 array enables the spatiotemporal mapping of distributed flexor and extensor muscle groups. Integrated with a customized wireless data acquisition system, the array successfully demonstrates real-time, multi-channel sEMG monitoring of various hand movements (e.g., fist clenching, wrist flexion/extension), clearly revealing specific muscle activation patterns. This low-cost, high-performance flexible sensor array provides a highly promising tool for complex gesture decoding, electromyographic imaging, and next-generation wearable HMIs. Full article

Surface electromyography (sEMG) is widely used for gesture recognition, yet the way classic feature–classifier pipelines fail under realistic signal degradations is still poorly quantified. Existing studies typically report accuracy on clean laboratory data, leaving open how amplitude saturation and channel dropout jointly affect different feature combinations, classifiers, and subjects. In this work, we provide, to our knowledge, the first systematic robustness map of a conventional sEMG pipeline under controlledclipping and single-sensor failure. sEMG from nine subjects performing a multi-session, multi-gesture protocol is windowed (250 ms, 50 ms hop) and represented using four common time-domain features (Root Mean Square, Variance, Zero Crossing, and Waveform Length). We exhaustively evaluated single features and all pairwise fusions with three standard classifiers (Support Vector Machine (RBF kernel), Linear Discriminant Analysis, and Random Forest) over (i) a sweep of symmetric saturation thresholds (${10}^{-6}$–${10}^{-1}$) and (ii) five single-channel dropout scenarios, reporting subject-wise dispersion rather than aggregate scores alone. This design enables explicit characterization of the following: (1) accuracy recovery as clipping weakens for each feature pair; (2) dependency of robustness on which channel fails; and (3) differences among Support Vector Machine, Linear Discriminant Analysis, and Random Forest under identical degradations. The results show that lightweight feature pairs (Root Mean Square + Waveform Length, Variance + Zero Crossing, and Waveform Length + Zero Crossing) coupled with Random Forest form a consistently robust operating point, with performance recovering as clipping weakens and remaining resilient under single-channel dropout. Beyond robustness, the conventional pipeline trains substantially faster than representative deep learning baselines under a unified end-to-end timing definition, supporting real-time recalibration and repeated robustness sweeps in wearable deployments. Full article

This study proposes a unified multimodal temporal motion state perception framework for optical imaging-oriented biomedical applications, integrating visual skeleton sequences, inertial measurement unit (IMU) signals, and surface electromyography (EMG) signals. The framework utilizes modality-specific encoders and a cross-modal temporal alignment attention mechanism to explicitly model temporal offsets from heterogeneous sensing streams. A multimodal temporal Transformer backbone is introduced to capture long-range motion dependencies and cross-modal interactions, while an uncertainty-aware fusion module dynamically allocates weights based on modality confidence. Experimental results demonstrate that the proposed approach achieves an accuracy of 94.37%, an F1-score of 93.95%, and a mean average precision of 96.02%, outperforming mainstream baseline models. Robustness evaluations further confirm stable performance under visual occlusion and sensor noise. These results indicate that the framework provides a highly accurate and robust solution for rehabilitation assessment, sports training monitoring, and wearable intelligent interaction systems. Full article

Background: Stroke-related impairments in balance and gait are among the most common and disabling sequelae, significantly limiting functional independence and increasing fall risk. This study investigated the effects of short-term dynamic balance training on balance and gait in post-stroke hemiplegic patients. Methods: In this randomized controlled pilot trial, 16 post-stroke hemiplegic patients (intervention group, n = 8; control group, n = 8; mean age ≈ 58 years; predominantly male) were assigned to either a control group receiving conventional rehabilitation or an intervention group receiving additional daily dynamic balance training using the Prokin-252 system (30 min/day, 5 days/week, 3 weeks). Primary outcome measures included balance performance (Berg Balance Scale, mini-BESTest, single-leg stance), center-of-pressure (COP) parameters, gait performance (Timed Up and Go Test), and surface electromyography (sEMG) activity. Results: Following the intervention, both groups demonstrated improvements; however, the intervention group showed significantly greater gains in balance and gait outcomes. Specifically, Berg Balance Scale scores improved significantly (p = 0.012), as did mini-BESTest scores (p = 0.004). Eyes-closed single-leg stance time increased significantly on both sides (p < 0.05). COP analysis revealed reductions in sway area and trajectory length under challenging conditions. sEMG analysis indicated increased activation of the affected-side gluteus medius. In terms of gait performance, the intervention group demonstrated greater improvements in Timed Up and Go Test performance (p = 0.002), dual-task walking, and gait phase symmetry. Conclusions: Supplementing conventional rehabilitation with dynamic balance training effectively enhances balance and gait function in post-stroke patients, potentially through improved neuromuscular control. The integration of sensor-based COP analysis and sEMG provides additional mechanistic insight into rehabilitation outcomes. Full article

Upper extremity (UE) pain and pathology are prevalent among manual wheelchair users (MWUs) due to repetitive loading demands, highlighting the need for tools to identify high-risk tasks and inform injury prevention. This study investigated the feasibility of classifying activity intensity for wheelchair-related tasks using wearable sensors and supervised machine learning. Twenty-four MWUs with chronic spinal cord injury completed a standardized mobility course and simulated activities of daily living while UE electromyography (EMG) and inertial measurement unit (IMU) data were collected. Signals segmented into 3, 5, and 10 s windows, and time- and frequency-domain features were extracted and labeled as low, moderate, or high intensity. Multiple classification algorithms were evaluated using subject-dependent and subject-independent cross-validation, and dimensionality reduction was explored to assess class separability. Subject-dependent analyses demonstrated performance above chance but below 75% accuracy, with decision tree models demonstrating superior performance, particularly when trained on data segmented into 5 s windows. IMU features outperformed EMG features, but combining signal types enhanced performance. Subject-independent analyses revealed similar overall accuracy across signal types, but decreased high-intensity classification for EMG data, indicating subject dependency. Findings support the potential of wearable sensor-based machine learning with population-specific findings for activity intensity classification in MWUs, while highlighting challenges related to inter-subject variability for injury risk prediction. Full article