Search Results (11,289)

Molecularly imprinted polymers (MIPs) are synthetic receptors with cavities shaped around a template, combining antibody-like selectivity with chemical, thermal, and mechanical robustness; low cost; and reusability. This Review examines recent advances in MIP-based biosensing, from bulk materials to thin-film, nanostructured, surface-imprinted, and epitope-imprinted architectures designed to improve site accessibility and performance in complex biofluids. We connect polymer chemistry and interface design to molecular recognition and electrochemical, optical, and mass-sensitive transduction. Applications range from small molecules, proteins, nucleic acids, and viruses to whole cells, encompassing miniaturized, wearable, and point-of-care formats. Particular attention is devoted to design assisted by computational methods and machine learning, as well as to the challenges of reproducibility, standardization, metrology, and sustainability that still limit translation. Rather than universal substitutes for antibodies, MIPs are presented as programmable biointerfaces that integrate molecular recognition, signal transduction, device engineering, and the design of low-environmental-impact materials. Full article

Background: The proliferation of fitness apps and wearable technology provides researchers interested in health and fitness with vast opportunities to test hypotheses involving a continuous stream of data collected on individual research participants in real-world settings. Although there are a variety of methods available to analyze such repeated measures data, one method that may benefit researchers in these endeavors is Area Under the Curve (AUC). To our knowledge, AUC has yet to be assessed for efficacy with many repeated measures collected from fitness apps. As such, the purpose of this study was to assess the efficacy of AUC with larger numbers of repeated measures typical of a fitness app using simulated data. Methods: We generated two samples of 21 hypothetical cyclists with 30 and 100 repeated measures of performance data (speed, time, and power) based on a Strava app segment, and calculated AUC using the trapezoidal rule and definite integrals with the best-fitting line. We then assessed the relations between time, speed, and power with all three calculations using bivariate correlations, multiple regression analysis, and a mediation analysis whereby power was hypothesized to predict a reduction in time indirectly through speed. We repeated these analyses with data assuming a normal and a lognormal distribution, and with equal versus unequal timepoints between consecutive repeated measures in the normal distribution. Results: There was little difference in relative performance comparing the two AUC calculation methods in the two samples (30 versus 100 repeated measures). However, there was a difference in the proportion mediated in the mediation results when comparing the two samples based on the number of repeated measures and spacing between consecutive repeated measures. Conclusions: The results of this study suggest that Area Under the Curve may be a viable method for assessing cumulative level of a behavior when dealing with datasets including many repeated measures such as those acquired from wearable technology and fitness apps. However, there may be differences in results when dealing with different numbers of repeated measures and variability in time between events. As such, future studies should assess AUC with larger numbers of repeated measures using non-simulated data in diverse performance scenarios. Full article

The vestibular system contributes to cardiovascular regulation during changes in gravitational load (e.g., postural transitions, microgravity), in vestibular disorders, and during vestibular stimulations. Studies employing sinusoidal galvanic vestibular stimulation (GVS) reported conflicting autonomic responses, and recent evidence suggests that vestibular afferents encode GVS in a non-linear fashion. Whether this non-linear encoding results in polarity-dependent autonomic responses and whether such responses interact with concurrent cognitive demand remains unknown. To investigate these issues, we used sinusoidal GVS to modulate vestibular input in 35 healthy individuals while physiological signals were recorded using a wearable device. Participants completed a working memory task, preceded and followed by rest periods, under three counterbalanced conditions: right-anodal/left-cathodal GVS (RGVS), left-anodal/right-cathodal GVS (LGVS), and Sham GVS. Perceived stress was repeatedly assessed. During task performance, heart rate increased with RGVS and decreased with LGVS relative to Sham (p = 0.001) and perceived stress increased across all conditions (p < 0.001). Post-task, heart rate remained elevated in RGVS compared to LGVS (p = 0.006), and stress ratings decreased in LGVS compared to the other conditions (p < 0.01). These findings indicate that sinusoidal GVS induces polarity-dependent autonomic effects, primarily during concurrent cognitive demand. This pattern may reflect non-linear vestibular encoding, with its emergence being potentially modulated by attentional shifts. Full article

Telemedicine is increasingly transforming pediatric cardiology by expanding access to specialized care, supporting early diagnosis, and improving longitudinal monitoring of children with cardiovascular disease. This narrative review summarizes current applications of digital health in pediatric cardiology, with emphasis on congenital heart disease, pediatric hypertension, and arrhythmia management. Tele-echocardiography represents one of the most established telehealth tools, enabling remote interpretation of fetal, neonatal, and pediatric echocardiographic images and improving referral appropriateness, particularly in peripheral or resource-limited settings. In infants with complex congenital heart disease, especially those with single-ventricle physiology during the interstage period, home monitoring programs using mobile applications, pulse oximeters, digital scales, and structured caregiver reporting may facilitate early recognition of clinical deterioration and reduce avoidable transfers. In non-congenital cardiovascular disease, home blood pressure monitoring can improve diagnostic accuracy by reducing white-coat effects and supporting repeated measurements in real-life settings. Smartphone-enabled electrocardiographic devices and wearable technologies may enhance detection of intermittent arrhythmias and strengthen outpatient management. Despite these advantages, challenges remain, including data fragmentation, limited pediatric validation of consumer devices, interoperability issues, privacy concerns, and socioeconomic disparities in technology access. Properly integrated telemedicine may promote more timely, equitable, and patient-centered pediatric cardiovascular care. Full article

Background: Day-to-day movement behavior may influence short-term glycemic variability in adults with type 2 diabetes, but free-living evidence integrating accelerometry with continuous glucose monitoring (CGM) remains limited. Objectives: This study examined whether daily moderate-to-vigorous physical activity (MVPA) and accelerometer-defined sedentary time were associated with 24-h within-day glycemic variability. Methods: In this longitudinal repeated-measures study, 140 adults with type 2 diabetes underwent 14 days of concurrent hip-worn ActiGraph GT3X+ accelerometry and FreeStyle Libre 2 CGM under free-living conditions in Lima, Peru. The primary outcome was a daily CGM-derived coefficient of variation (CV). Linear mixed-effects models separated within-person and between-person components, with Holm adjustment for the two primary within-person associations. Results: The fully adjusted primary analysis included 132 participants and 1728 participant-days. Higher within-person MVPA was associated with a lower daily CV (β = −0.27 percentage points per 10 min/day, 95% CI −0.45 to −0.09; Holm-adjusted p = 0.006), whereas greater accelerometer-defined sedentary time was associated with a higher CV (β = 0.05, 95% CI 0.01 to 0.09; Holm-adjusted p = 0.014). Secondary concurrent analyses showed favorable MVPA and opposite sedentary time associations for time in range, time above range, mean glucose, and glucose standard deviation (all false discovery rate q ≤ 0.018), with no statistically detectable association with time below range. Lagged associations were directionally consistent, but none remained statistically significant after multiplicity correction. Conclusions: Daily MVPA and accelerometer-defined sedentary times were associated with modest differences in within-day glycemic variability and concurrent CGM profiles. These observational findings do not establish causality or a specific exercise dose. Full article

Hypertension is a major risk factor for cardiovascular disease, yet cuffless blood pressure monitoring remains challenging because most existing methods rely on intermittent cuff-based measurements or multimodal physiological signals. This work proposes PPG-FusionNet, a dual-branch deep learning architecture for simultaneous systolic and diastolic blood pressure estimation using only photoplethysmography (PPG) signals. The model combines a local dilated convolutional encoder and a global AutoCorrelation encoder operating on a shared patch embedding, whose representations are integrated through cross-attention fusion and optimized with a constrained dual-head regression objective. The model was trained and evaluated on the PulseDB benchmark using Bayesian hyperparameter optimization and systematic ablation studies to assess each architectural component. PPG-FusionNet achieved mean absolute errors of 7.46 mmHg for systolic blood pressure and 4.72 mmHg for diastolic blood pressure, with near-zero mean errors and compliance with the ANSI/AAMI standard and BHS Grade B for diastolic estimation. Ablation experiments revealed that trend-seasonal decomposition, despite its success in long-horizon forecasting, degraded performance on short PPG windows, whereas cross-attention fusion and AutoCorrelation improved estimation accuracy. These results demonstrate that heterogeneous dual-branch representation learning provides an effective, scalable framework for cuffless blood pressure estimation from a single PPG sensor. Full article

Human Activity Recognition (HAR) plays a significant role in various applications, from learning a discipline to physical rehabilitation. In older adults, activity patterns can indicate levels of frailty, which helps inform the design of physical training programs to prevent falls and maintain mobility. HAR sensing ranges from wearable sensors such as IMUs and RGB cameras to video, specialized gait laboratories, perturbation units, VR, and other modalities. This paper presents a comprehensive study of depth-based, skeleton-driven HAR and age group recognition (AGR) using data collected from real-world nursing home environments. Depth sensors offer a privacy-preserving and non-invasive alternative to wearable and RGB-based systems, enabling continuous 24-h monitoring without requiring user compliance. We systematically evaluate multiple modeling paradigms, including classical machine learning models (DT, RF, KNN, SVM, HMM, HMM+SVM), sequence-based models (LSTM, TCN, ARNN), and graph-based approaches, using skeletal joint data extracted from depth images. Experiments are conducted on two heterogeneous datasets: NTU RGB+D (younger adults) and ETAP-DID (older adults). We analyze the impact of different joint subset configurations (full-body, limb-only, leg-only, and torso-only) and compare raw joint representations with handcrafted time-series features (TSFEL) for frame-based HAR. Beyond activity recognition, we introduce an AGR pipeline to distinguish younger from older adults based on skeletal motion patterns. We investigate multiple feature representations, including absolute joint positions, root-relative coordinates, bone vectors, and joint velocities, and provide interpretability through feature importance and saliency analysis to identify age-discriminative joints and motion cues. Our study provides a comprehensive analysis of various HAR models applied to depth data, examining model performance and the contribution of joint-based features to HAR and AGR. Our study highlights the potential for personalized privacy-preserved monitoring and intervention in nursing homes. Full article

Aquatic environments impose strong constraints on conventional feedback channels and challenge the deployment of wearable human–machine interfaces. This paper presents the design, characterization, and field validation of a head-worn vibrotactile interface intended for symbolic cue delivery during swimming. The proposed platform consists of a waterproof wearable node, six individually controlled actuators, a custom driver, and a host-side control application that uses a lightweight protocol and autonomous execution to tolerate intermittent wireless connectivity after submersion. Actuator operation was characterized under deployment conditions. Symbolic cues were encoded using spatial and sequential activation patterns and validated in a field protocol with 51 competitive swimmers. To enable practical scoring without additional underwater motion instrumentation, each cue was mapped to an externally observable response. After familiarization with a six-symbol cue–response vocabulary, participants produced the predefined observable responses with 96.96% overall task-level accuracy, with 99.35% in the initial out-of-water trial and 94.55% in the subsequent in-water trial following symbol familiarizing and training. Because trial order was fixed, the between-trial difference cannot be attributed exclusively to the aquatic medium. Symbol-level analysis showed that front-actuator and crawl cues were the most robust. Secondary user evaluation results indicated that the prototype was understandable and tolerable during short sessions. These results support the feasibility of trained symbolic vibrotactile cue delivery for a compact six-cue vocabulary in one head-mounted configuration in aquatic environments and provide an engineering foundation for future closed-loop aquatic feedback systems. Full article

Wearable near-infrared spectroscopy (NIRS) enables non-invasive hemodynamic monitoring, yet conventional CW-NIRS systems suffer from limited temporal resolution, scalp-only measurement, and mandatory manual tuning to compensate for optical heterogeneity across subjects and sites. This work develops NIRSLINK, a modular cascaded wearable NIRS system for high-speed multi-site hemodynamic acquisition. Its flexible probes adopt spring-floating optics with a standardized 30 mm optode separation, integrating dual 735/850 nm LEDs, silicon photodiodes, and a two-stage closed-loop tuning algorithm. A single probe achieves a peak sampling rate of 3 kHz, and up to eight cascaded probes form 52 valid channels, with a signal-to-noise ratio (SNR) of 78.61 ± 7.03 dB and an optical dynamic range (DR) of 101.32 ± 12.41 dB. Phantom experiments verify its millisecond temporal resolution and high sensitivity to blood flow and hemoglobin variations. In vivo trials, including the Valsalva maneuver, forearm occlusion, and two-back cognitive tasks, demonstrate simultaneous recording of hemoglobin concentration shifts, pulse waveforms, and beat-to-beat pulse transit times (PTTs). NIRSLINK supports hemodynamic measurements across multiple anatomical locations, including the forehead, forearm, upper arm, and thigh, covering both cranial and peripheral body regions. The embedded auto-tuning module stabilizes signals from the forehead, forearm, and other regions within the optimal ADC range without manual adjustment, preventing signal saturation and SNR degradation. This scalable adaptive platform overcomes critical drawbacks of traditional wearable NIRS, applicable to cognitive neuroscience, non-invasive cardiovascular assessment, and ambulatory physiological monitoring, and provides design references for multi-site optical sensors. Full article

Wearable technologies are reshaping gait rehabilitation by shifting assessment and therapy from intermittent, clinic-based observation to continuous, data-driven, and adaptive care. This narrative review synthesizes the biomechanical basis of gait instability, the architecture and classification of wearable rehabilitation systems, and the clinical evidence for motion sensors, smart insoles, biofeedback devices, robotic and orthotic wearables, neuromodulatory systems, immersive platforms, and artificial intelligence (AI)-enabled closed-loop interventions. Its main contribution is an integrated framework that links AI, digital biomarkers, device components, adaptive control, and translational implementation, rather than treating wearable rehabilitation as a device-only or disease-specific topic. Current evidence indicates that these technologies can improve gait speed, symmetry, balance, endurance, fall-risk monitoring, and dual-task performance in neurological, musculoskeletal, frailty-related, and aging populations. However, the field is still limited by heterogeneous protocols, small samples, limited longitudinal validation, insufficient device standardization, usability barriers, cybersecurity concerns, uncertain reimbursement, and restricted interoperability with healthcare systems. Future progress will depend on multimodal sensor fusion, explainable and federated AI, digital twins, adaptive wearable robotics, tele-rehabilitation pathways, and large-scale pragmatic trials that validate effectiveness in real-world rehabilitation settings. Full article

Reliable detection of event-related potentials (ERPs) at the single-trial level remains a challenge due to low signal-to-noise ratio and high variability in electroencephalography (EEG) recordings. This work investigates the use of the Deep-Match framework (Deep-MF) for ERP detection. We examine whether incorporating prior knowledge of an ERP template into deep learning models improves detection performance. As a proof-of-concept study, the framework was evaluated on a single dataset with multi-channel EEG recordings during laser stimulation. The model was trained in two stages. First, an encoder–decoder architecture was trained to reconstruct input EEG signals in order to learn compact signal representations. In the second stage, the decoder was replaced with a detection module and the network was fine-tuned for ERP identification. Two model variants were evaluated: a standard model with randomly initialized filters and a Deep-MF model in which input kernels were initialized using ERP templates. Models performance was assessed on a single-trial ERP detection task during leave-one-out validation, and then compared with matched filter detector. The neural network models outperformed the matched filter detector and proposed that the Deep-MF model slightly outperformed the detector with standard kernel initialization for the majority of held-out subjects. Although both approaches exhibited substantial inter-subject variability, Deep-MF achieved a higher average F1-score (0.37) compared to the standard network (0.34), indicating improved robustness to cross-subject differences. Performance varied considerably across participants. The best performance obtained by Deep-MF reached an F1-score of 0.71, exceeding the maximum score achieved by the standard model (0.59). These results showed that ERP-informed kernel initialization provides improvements in single-trial ERP detection under subject-independent evaluation. These findings demonstrate that integrating domain knowledge with deep learning architectures can improve single-trial ERP detection. The proposed approach provides a step towards practical wearable EEG and passive brain–computer interface applications, as well as towards real-time monitoring of cognitive processes. Full article

Balance impairment threatens mobility and independence in older adults with cardiovascular disease, yet cardiac rehabilitation (CR) has traditionally prioritised aerobic capacity and cardiovascular outcomes. This narrative review examines the mechanisms and assessment of balance impairment, balance recovery within CR, and implications for clinical practice. Balance impairment reflects interactions among musculoskeletal, sensory, cognitive–motor, and cardiovascular constraints that affect different domains of postural control to varying extents. Standardised clinical measures do not capture these domains equally, and single scores or completion times can obscure the deficits underlying poor performance. Instrumented and wearable technologies extend clinical assessment by quantifying postural sway and gait, helping to distinguish broader mobility gains from recovery within specific balance domains. To translate this distinction into practice, this review recommends individualised, balance-focused CR, with assessment guiding task-specific training and virtual reality or exergaming providing graded practice and performance feedback to promote mobility and independence. Full article

This systematic review analysed the role, opportunities and challenges of digital technologies in promoting physical activity among rural children and adolescents aged 10–18. By identifying and analyzing 10 eligible studies across four major databases between June 2016 and June 2026, the research classified the tools used into five categories: behaviour-monitoring technologies (like wearable activity trackers), educational technologies (digital learning platforms), communication and social media technologies, immersive technologies (virtual and augmented reality), and digital health technologies. The implications of these tools were examined in relation to physical education curricula and the development of healthy lifestyles. While the available evidence suggests that these technologies support rural youth by enhancing self-monitoring, increasing health awareness, improving access to educational resources, and creating context-specific opportunities for active engagement, the data reveals that evidence of sustained improvements in moderate-to-vigorous physical activity remains limited. Furthermore, implementation is heavily hindered by barriers such as the digital divide, limited technological infrastructure, inequalities in resource access, and the threat of sedentary behaviours associated with excessive screen time. Consequently, the findings underscore a need for coordinated educational and health strategies, offering practical recommendations to integrate these digital technologies within rural educational settings to foster long-term active lifestyles. Full article

Non-invasive continuous glucose monitoring remains an important technological challenge in diabetes management. This study was revised as a literature-grounded simulation and methodological proof-of-concept rather than an experimental clinical validation. The analysis used 320 independent synthetic reference–NIR glucose pairs generated from physiological ranges and performance distributions reported in the cited literature, together with an illustrative 576-point, 48-h synthetic time series. Three sensor archetypes were informed by published optical systems. Descriptive agreement was evaluated using mean absolute relative difference (MARD), Pearson correlation, Bland–Altman analysis, and the supplied Clarke Error Grid (CEG) categories. Candidate physiological and environmental factors were examined using Pearson correlations with 95% confidence intervals, raw p-values, and Holm correction. The synthetic dataset produced an overall MARD of 10.38% (SD = 7.67%), r = 0.958 (R² = 0.918, p < 0.001), a Bland–Altman bias of 4.32 mg/dL, and 89.4%/10.3%/0.3% of records in CEG zones A/B/D, respectively. All candidate-factor correlations were small and non-significant after multiplicity correction; the largest was hematocrit (r = 0.090, 95% CI −0.019 to 0.198; Holm-adjusted p = 0.852). Differences in MARD among sensor archetypes were also non-significant (ANOVA p = 0.369; Kruskal–Wallis p = 0.543). Adaptive calibration is therefore presented as a literature-informed framework for future prospective validation, not as a performance improvement demonstrated by the present synthetic dataset. No FDA or ISO compliance or clinical applicability is claimed. Full article

Wearable electronic textiles require flexible, lightweight, and sustainable energy-harvesting platforms. This study presents a proof-of-concept fabric-assisted thermally responsive voltage-generating prototype fabricated from recycled electronic and textile waste. Copper and aluminum current-collector foils recovered from discarded non-functional lithium-ion mobile-phone batteries, together with woven apparel cutting waste composed of 70% cotton, 28% polyester, and 2% elastane, were used as the main device components. The recovered conductive foils were cleaned, dried, and manually integrated into the textile substrate using a weaving and piercing-based approach. Under preliminary human forearm-contact testing, the fabricated prototype generated a maximum RMS open-circuit voltage of 180.75 mV at a body-to-ambient temperature difference of 5.82 K. The prototype also retained 90.73%, 86.88%, 81.33%, and 73.58% of its initial RMS open-circuit voltage after 100 rolling, bending, twisting, and folding cycles, respectively. However, the present study measured open-circuit voltage only, and contributions from contact potential, moisture-assisted galvanic effects, oxide layers, pressure-dependent contact resistance, electrochemical processes, and measurement artefacts cannot be fully excluded. Therefore, the results should be interpreted as preliminary proof-of-concept evidence rather than complete validation of practical thermoelectric power-generation performance. Future work should include controlled Seebeck measurements, direct active-junction temperature monitoring, current and power output, load matching, internal resistance, control samples, repeated trials, and durability testing. Full article