Search Results (557)

Background: We present a miniature (30 × 34 mm) 64-channel data acquisition headstage optimized for high-density surface electromyography. Methods: The headstage is made up of a multi-channel ASIC analogue front-end utilizing only MOS transistors, fabricated in 350 nm CMOS technology (IC die dimensions 6.9 × 1.8 mm), combined with an off-the-shelf multi-channel current-input ADC (DDC264, Texas Instruments). The ASIC analogue front-end employs MOS-based capacitors for both processing and AC-coupling. Results: The combination of these two sub-circuits enables the simultaneous recording of 64 channels at a typical sampling rate of 4 KHz with a maximum analogue bandwidth of 0.5–1500 Hz and a resolution of 20-bits. Typical input-referred-noise, determined by the analogue front-end, is 3.5 μV_RMS for a surface EMG bandwidth of interest of 20–500 Hz. This two-chip solution results in a power consumption of 5 mW per channel. Analogue performance variability of the custom ASIC was characterized across a dataset of 960-channels (15 dies) from two fabrication runs. Conclusions: This work practically demonstrates the viability of using both a MOS-only analogue front-end and commercially available off-shelf high-performance back-end hardware already developed for medical imaging applications to record high-density surface biosignals. The aforementioned techniques can be employed to reduce the size and cost for systems or wearable devices; facilitating the translation of high-density bio-acquisition setups from the research environment to more affordable commercial products. Full article

The electrochemical performance of eight commercially available bioelectrodes for electrophysiological measurements was systematically evaluated in isotonic saline solution. The studied bioelectrodes included sintered Ag/AgCl pellet, cup and ring, an Ag/AgCl multipin, tin (Sn) ring and disc, a gold cup, and a stainless-steel needle. Open circuit potential (OCP) and drift rate, electrochemical impedance spectroscopy (EIS), and electrochemical noise (ECN) measurements were performed to assess interfacial stability, impedance behavior, and generated noise in time and frequency domains. Scanning electron microscopy (SEM) and Energy-dispersive X-ray spectroscopy (EDS) were used to study the morphology and chemical composition of the bioelectrodes. Ag/AgCl-based bioelectrodes exhibited the highest OCP stability and potential reproducibility, lowest impedance, and electrochemical noise, attributed to the fast and reversible Ag/AgCl electrochemical equilibrium, and high area related to roughness and porosity. EIS analysis showed predominantly low-resistance charge-transfer behavior and high capacitance for Ag/AgCl bioelectrodes, while tin, gold, and stainless-steel bioelectrodes displayed higher impedance and mixed capacitive/resistive responses associated with passive oxide films and slower interfacial kinetics. Tin, gold, and stainless-steel bioelectrodes also presented substantially higher low-frequency noise and OCP drift rate. Among all tested bioelectrodes, sintered Ag/AgCl bioelectrodes demonstrated the most favorable electrochemical characteristics for electrophysiological signal acquisition, particularly for low-amplitude and low-frequency biosignals. Full article

Sleep monitoring and analysis are essential for understanding overall health, improving sleep quality, and detecting potential disorders early. This study presents a multimodal approach for automatic sleep staging and quality assessment using a reduced set of bio-signals: a single electroencephalographic (EEG) lead (F4–F3), a single EOG lead, and the photo-plethysmographic (PPG) signal. The proposed methodology includes a hierarchical sleep staging classifier, an automatic sleep staging algorithm, and a subject-specific Sleep Quality Index (SQI) for objective sleep quality assessment. The 5-class sleep staging classifier employs a cascaded architecture of two sequential 3-class models (Wake-REM-NREM and N1-N2-N3), trained and tested on multimodal features derived from physiological signals (EEG, EOG, and PPG) of the BOAS (Bitbrain Open Access Sleep) dataset. The resulting 5-class classifier achieved 90.8% accuracy with a reduced memory footprint (3.14 MB). To assess subject-independent generalization and prevent data leakage between training and test sets, a Leave-One-Subject-Out (LOSO) validation was performed, confirming the robustness of the proposed classifier across unseen subjects. The classifier was subsequently integrated into an automatic sleep staging algorithm. Validation on 14 unseen subjects yielded accuracies ranging from 80.26% to 91.99% using heuristic post-processing rules, while a Hidden Markov Model (HMM)-based approach further improved performance, reaching a peak accuracy of 91.99%. The proposed SQI combines sleep-related metrics extracted from staging, considering multiple sleep aspects (i.e., duration, intensity, and continuity-fragmentation). A calibration strategy was proposed to customize the SQI based on sleep scoring parameters and the subjective quality score derived from sleep diaries and questionnaires (PSQI). This subject-specific strategy was validated on a public dataset, optimizing weights across multiple nights, followed by an independent test on a subsequent night and demonstrating strong alignment between the calculated SQI and the subjective sleep quality score (MAE = 10.81). Finally, the framework provides resource-efficient sleep staging and custom quality estimation, validating its readiness for practical, long-term sleep monitoring. Full article

Deep learning has achieved remarkable progress in electrocardiogram (ECG) analysis, but its heavy dependence on labeled data greatly increases annotation cost. This work proposes wall-aligned vector embedding (WAVE), a self-supervised learning framework that effectively extracts prior knowledge from unlabeled ECGs to reduce reliance on labels. WAVE fully leverages the diversity, synergy, and lead correlation of multi-lead ECGs by explicitly incorporating the correspondence between ECG leads and cardiac walls. Specifically, a multi-branch network captures lead-wise diversity; wall-wise synergy is modeled by concatenating leads from the same wall and projecting them via shared projection; and a dual alignment task is designed to learn correlations both within and across cardiac walls. Experimental results demonstrate that WAVE consistently surpasses all baselines under various evaluation settings, and maintains strong performance even when only a small fraction of labeled ECGs is available. Furthermore, components such as dual alignment, shared projection, wall-based concatenation, and mean target embedding are empirically verified to significantly enhance pretraining quality. In summary, WAVE learns highly informative ECG representations from unlabeled data, enabling low-cost and label-efficient ECG analysis for real-world cardiovascular diagnostics. Full article

Background/Objectives: Advancements in neuroscience and machine learning have increasingly enabled brain pattern recognition based on bio-signal measurements, such as electroencephalography (EEG). These developments support next-generation technologies, including brain–computer interfaces (BCIs) and AI-assisted systems. However, volume conduction (VC) effects remain a major source of contamination in EEG recordings, affecting both univariate analyses and functional connectivity estimation. Methods: In this work, we propose a VC mitigation method that explicitly models and suppresses VC components in the observed functional networks. Specifically, the observed functional network is decomposed into a matrix capturing only VC-related components (i.e., components attributed to volume conduction) and a residual matrix, where the residual is regarded as a proxy for a VC-mitigated functional network that better reflects the underlying functional interactions. The VC component matrix is modeled using a decay function parameterized by the inter-electrode distance matrix, capturing the dominant spatial bias induced by VC. To estimate these parameters, we introduce supervised channel importance, quantified as the mutual information between experimental labels and channel signals, as a proxy for task-relevant neural activity. The parameters are optimized such that the unsupervised node importance derived from the VC-mitigated functional network, defined as the average node strength, aligns with the supervised channel importance. Results: Evaluation results using a deep-learning framework demonstrate that, compared with the observed functional network, the VC-mitigated functional network improves classification performance in brain pattern recognition tasks. Full article

Quantum machine learning (QML) provides a framework for benchmarking wearable biosignal classification relevant to stress detection. Motivated by the burden of stress-related conditions, this study compares three quantum classifiers with seven classical baselines using heart rate and respiration rate features as inputs under noise-free and noisy conditions. Uncertainty was quantified using Nadeau–Bengio-corrected confidence intervals and percentile bootstrap ($B=1000$). The variational quantum classifier (VQC) achieved an accuracy of $99.47\%/97.30\%$ (noise-free/noisy), the quantum support vector classifier (QSVC) achieved $99.90\%/99.37\%$, and PegasosQSVC achieved $99.80\%/99.70\%$. Additionally, under the assessed proof-of-concept conditions, statistical equivalence between the QSVC and the best-performing classical model was established at $\Delta =1$ pp; PegasosQSVC under noise achieved equivalence at $\Delta =2$ pp with accuracy degradation of less than $0.10$ pp. The time feature was identified as the primary separability driver in a post hoc classical ablation. Tree-based models were robust on physiological features alone. The surveyed methods provide a reproducible, noise-aware benchmark for wearable physiological signal classification; however, the reported high accuracies are based on a deliberately separable proof-of-concept benchmark and do not demonstrate clinical utility or a quantum advantage. Full article

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

This study introduces an innovative mixed-mode universal biquad filter implemented using multiple-input operational transconductance amplifiers (MI-OTAs). Based on the advantage of OTAs, which possess multiple inputs, the proposed mixed-mode universal filter using MI-OTAs can implement both non-inverting and inverting standard filtering functions such as low-pass, high-pass, band-pass, band-stop, and all-pass filters in voltage-mode, transadmittance-mode, current-mode, and transimpedance-mode, which is the maximum capability of mixed-mode universal filters. The natural frequency of all filtering functions can be electronically controlled. Based on the multiple-input bulk-driven MOS transistor (MOST) technique, the OTA can also operate at very low supply voltage and provide wide-input voltage swing. The technique of MOST, operating in the weak inversion region, is used to achieve the low-power consumption of OTA. The MI-OTA circuit and mixed-mode universal filter were designed and simulated using Cadence Virtuoso, utilizing TSMC’s 65-nm CMOS technology. At a 0.5 V supply voltage, the filter demonstrated a simulated power consumption of 450 nW at a natural frequency of 156 Hz. In these ranges of power consumption and natural frequency, it can be expected that the proposed filter can be built as an versatile integrated circuit for low-frequency applications such as bio-signal processing. The design parameters were successfully validated through both post-layout extractions and discrete hardware prototyping utilizing commercially available LM13700N ICs. Full article

This work presents a pilot study to analyze the effect of aging on motor performance of young adults (YAs) and older adults (OAs) through wrist movement assessment, using an upper limb rehabilitation robot (ULRR) in passive mode coupled to a maze-solving task serious video game. The proposed approach considers the use of kinematic metrics, such as ROM, path accuracy, and movement smoothness, as quantitative biomarkers that evidence differences between YAs and OAs. An experimental protocol was conducted with 20 participants: 10 OAs and 10 YAs. Standardized wrist movements corresponding to flexion (F), extension (E), radial deviation (R), and ulnar deviation (U) were assessed at each level of the maze. The kinematic analysis was based on metrics for range of motion (ROM), path accuracy, smoothness, and root-mean-square error (RMSE) in trajectory tracking. The results revealed clear differences between the groups: the YAs achieved a greater ROM and made fewer errors on mean (2.167 errors for YAs compared to 6.000 errors for OAs), and showed a lower RMSE, while the OAs showed greater smoothness in their movements, because the YAs exhibit greater variability and disturbances in movement when correcting and controlling their movements to achieve good performance, reflecting more precise motor control and a greater capacity for error correction during movements with trajectory constraints. 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

Stroke is the second leading cause of death globally and a major contributor to lower-limb disability, affecting gait, balance, and functional independence in elderly populations. While robot-assisted rehabilitation has demonstrated effectiveness in motor recovery, access remains limited due to high costs and geographic barriers, particularly in Latin America. This study presents ExoKnee, a low-cost knee exoskeleton designed through biomimetic principles and 3D-printed fabrication as a proof-of-concept device targeting gait rehabilitation in elderly adults. The system integrates a single-degree-of-freedom pneumatic actuator controlled by electromyography (EMG) signals from the quadriceps muscle, enabling knee flexion and extension (90° to 180°). The design was evaluated through finite element analysis and dynamic simulations in MATLAB/Simulink R2024a under constant, stepwise, and sinusoidal reference inputs in a digital-twin environment. Expert validation using the Content Validity Coefficient yielded a mean score of 0.8747, reflecting preliminary expert agreement on the conceptual design’s coherence and relevance. The prototype demonstrated controlled movements through a 6-bar pneumatic system with EMG-triggered relay activation, validated at the proof-of-concept level through simulation and single-subject threshold calibration. ExoKnee addresses critical gaps by offering an anthropometrically informed, biosignal-driven, and locally manufacturable rehabilitation platform for low- and middle-income countries, pending clinical validation. Future work will focus on clinical trials and adaptive EMG control strategies. Full article

Understanding muscle activation patterns during sport-specific skills is essential for optimizing performance and training strategies. In basketball, upper limb actions such as passing and receiving require precise coordination and effective neuromuscular control. The main goal of this study was to analyze and compare the muscle activity of the biceps brachii and triceps brachii during the execution and reception of the two-handed chest pass in basketball players with different levels of competitive experience. Surface electromyography (EMG) data were collected from 14 federated athletes, aged between 11 and 29 years, using the BioSignal Plux system. Participants were allocated into two groups according to their playing experience. Muscle activation was analysed in terms of activation time (AT) and percentage of muscle activation (%MA), normalised to maximum voluntary contraction (MVC). A linear mixed model was used to evaluate the effects of experience level, limb dominance, and their interaction while accounting for repeated measures within participants. No significant differences were observed between dominant and non-dominant limbs for any variable. Significant differences between experience/age groups were identified mainly in the triceps brachii, particularly for activation time in the lateral head and %MA in the long head. In general, more experienced/aged athletes demonstrated higher levels of neuromuscular activation and shorter activation times, suggesting different motor control strategies. A significant positive association was found between years of practice and %MA of the long head of the triceps brachii. These findings provide novel insights into neuromuscular recruitment during both the execution and reception phases of the basketball chest pass and may inform training strategies aimed at enhancing technical efficiency across developmental stages. Full article

Driver drowsiness detection has become an important application of sensor-based monitoring systems aimed at improving road safety. This review focuses on sensing technologies and physiological parameters used for real-time drowsiness detection in drivers. The surveyed approaches are categorized into physiological sensing methods, including electroencephalography (EEG), electrocardiography (ECG), galvanic skin response (GSR), and photoplethysmography (PPG), and mechanical sensing methods, including respiration rate, eye blinking, head movement, yawning, and steering wheel gripping force. Each method is analyzed from a sensor system perspective, considering signal acquisition principles, measurement location, and practical deployment constraints. In addition, the reviewed techniques are evaluated based on real-time capability, level of sensor attachment, cost, restriction of user movement, and suitability for standalone operation. The comparison highlights that mechanical sensing approaches provide non-invasive and cost-effective solutions; however, they are sensitive to environmental noise and behavioral variability. In contrast, physiological sensing methods offer more direct and earlier indicators of fatigue-related changes in biosignals, although they typically require wearable or contact-based sensors and more complex acquisition systems. The review further indicates that multimodal sensor fusion is increasingly being adopted to improve robustness and reliability in real-world driving conditions. Overall, this work provides a structured overview of sensing modalities and highlights key considerations for designing efficient, real-time driver monitoring systems. Full article

Multimodal emotion recognition is challenging because behavioral signals and physiological responses evolve at different temporal rates. Facial expressions and speech often change rapidly after an emotional event, whereas peripheral biosignals such as electrodermal activity, blood volume pulse, and skin temperature exhibit delayed and smoother dynamics. This temporal inconsistency can degrade fusion performance, particularly in real-world recordings with noisy or missing modalities. To address this issue, this study proposes SGMT, an S-PACE Gated Multimodal Transformer for emotion recognition using speech, facial video, and physiological signals. The proposed SGMT introduces S-PACE, a physiology-guided cross-attention mechanism that aligns fast behavioral cues with slower biosignal representations without assuming a fixed temporal delay. A Quality-Aware Gate further improves robustness by adaptively weighting modalities according to signal reliability. The fused representations are processed using a Temporal Swin Transformer and a Perceiver Fusion module for arousal–valence prediction and emotion quadrant classification. Experiments are conducted on the Korean multimodal emotion datasets KEMDy20 and K-EmoCon under different modality settings. SGMT achieves arousal UARs of 68.4% on KEMDy20 and 62.9% on K-EmoCon, with quadrant accuracies of 44.7% and 62.5%, respectively. Ablation studies demonstrate that the proposed alignment and gating strategies provide more stable multimodal fusion than conventional feature concatenation. The results indicate that SGMT effectively adapts to varying modality availability and improves multimodal emotion recognition in naturalistic environments. Full article

Wearable devices increasingly capture biosignals such as electrocardiograms, photoplethysmograms, inertial signals, and electrodermal activity during daily life, enabling earlier detection and continuous monitoring outside the clinic. Real-time edge machine learning can convert these streams into timely, privacy-preserving inference by placing computation on a wearable (device-only) or a paired phone, with intermittent cloud assist used selectively for dashboards, summarisation, and lifecycle management. Clinical adoption remains uneven because free-living data are noisy, labels are often delayed, and device ecosystems evolve over time. This narrative review organises the literature as an end-to-end deployment pathway: sensing and artefact management, streaming windowing and multimodal alignment, and model families suited to on-device inference. We compare classical feature-based pipelines with learned representations, including compact CNN/TCN and recurrent and efficient attention-based models, and discuss when self-supervised pretraining and distillation are most useful in low-label settings. We then synthesise deployment engineering levers (quantisation, pruning, and distillation) and benchmarking requirements, emphasising runtime constraints that determine feasibility: latency per update, peak RAM, energy per inference, duty cycle, and thermal behaviour. Applications are grouped across cardiovascular monitoring, blood pressure and haemodynamics, sleep and respiration, and movement and stress, with explicit attention to false-alert burden, adherence, and workflow integration. To support translation, we provide a validation ladder and a reliability toolkit covering calibration, uncertainty-aware thresholds and deferral, drift monitoring triggers, and safe update governance. The novelty of this review is a deployment-oriented synthesis that ties modelling choices to edge tiers and resource budgets and provides reusable reporting templates, including an edge-cost card and comparative tables spanning modalities, models, deployment levers, applications, and reliability requirements. Full article