Search Results (5,328)

Objectives: To systematically synthesize existing evidence on functional biomechanical tests of the foot and ankle in physiotherapy and sports, focusing on their outcome measures, compatibility with wearable sensor technologies, and psychometric properties. Methods: We performed a systematic review (PRISMA-guided) of PubMed, Web of Science, PEDro, and SPORTDiscus from inception to December 2025. Eligible studies evaluated functional foot/ankle biomechanics in athletes, healthy adults, or adults with musculoskeletal foot/ankle conditions using wearable sensors (e.g., IMUs, wireless pressure insoles). Two reviewers independently screened, extracted data, and appraised methodological quality using the COSMIN Risk of Bias tool, applying property-specific ratings. Heterogeneity precluded meta-analysis; findings were narratively synthesized and tabulated. Results: Twenty full texts were reviewed; four studies (n = 83 participants) met the inclusion criteria. Wearable devices included foot- or trunk-mounted IMUs and wireless pressure insoles. Reported outcomes spanned temporal gait events and inner-stance phases, vertical ground reaction force (vGRF) and centre-of-pressure trajectories, running step rate/stride length, and jump counts in competition. Validity was most frequently assessed: foot-worn IMUs showed millisecond-level agreement with in-shoe pressure references for stance and inner-stance events; pressure insoles demonstrated acceptable agreement with force plates for vGRF/COP alongside fair-to-excellent test–retest reliability; foot- vs. shank-mounted IMUs provided strong agreement for running step rate and stride length; and competition-based jump detection using IMUs achieved high sensitivity. Across studies, reliability indices were inconsistently reported, measurement error (SEM/MDC) was sparse, and MCID was not reported. The COSMIN appraisal ranged from very good/adequate to inadequate, driven primarily by small sample sizes, non-gold-standard comparators, and incomplete psychometric reporting. Full article

The substrate plays a critical yet often underappreciated role in determining the performance, stability and manufacturability of photovoltaic devices. While conventional glass and polymer films have enabled the rapid development of solar technologies, emerging applications such as building-integrated photovoltaics, wearable systems and large-area conformal devices demand the use of non-conventional substrates, including ceramics, metals, paper, textiles and elastomeric materials. This review provides a comprehensive analysis of the current state of the art of non-conventional substrates for photovoltaic technologies, with particular emphasis on the interplay between material properties, surface chemistry and deposition processes. These substrates introduce distinct mechanical, thermal and interfacial constraints that fundamentally alter thin-film growth, defect formation and device reliability. Key challenges such as porosity, roughness, thermal transport limitations and outgassing are discussed in relation to nucleation, film continuity and interfacial stability. The role of substrate-dependent effects in both chemical and physical deposition techniques is critically examined, highlighting cases where conventional processing approaches are insufficient. Representative device demonstrations are analyzed to illustrate how substrate selection influences performance and integration strategies across different photovoltaic platforms. Finally, common limitations and emerging opportunities are identified, emphasizing the need for the co-design of substrates, materials and processing routes. This work establishes a unified framework to guide the development of next-generation photovoltaic devices on unconventional substrates. Full article

With the continued rise in outpatient surgical procedures, modern medicine requires more advanced tools for pain and anxiety monitoring and management. The current standard of care requires patient responses on visual analog scales, which may be subjective and are difficult to assess when a subject is unresponsive. Electrodermal activity (EDA) and pulse rate variability (PRV), two non-invasive, wearable, and objective measurements of sympathetic nervous system activity, can help provide insight into a patient’s psychological or emotional state without user input, allowing for continued monitoring even when a patient is unable to respond. However, methods based on these measurements have largely been relegated to longer duration (>60 s) or post hoc analysis, which does not suit the needs of medical care environments. Here we propose new methods for handling ultra-short (<10 s) signals to allow rapid evaluation of pain and anxiety state. We show how machine learning models trained on these signals can obtain high degrees of classification performance (AUC > 0.88) between no pain or anxiety and medium or higher pain and anxiety on signals obtained during two different forms of painful stimulation. We also show how these signals can measure the degree of stimulation irrespective of perceived pain from the patient. Further development of these algorithms will allow for greater monitoring and control of patient comfort in a clinical setting. Full article

Flexible supercapacitors (SCs) have attracted considerable attention for wearable electronics, and developing high-performance electrolytes is critical for their practical application. While hydrogels have been widely investigated as solid electrolytes, studies on double-network (DN) hydrogel electrolytes specifically addressing the electrode–electrolyte interface stability under mechanical deformation remain relatively scarce. A major obstacle is maintaining a stable electrode–electrolyte interface under large mechanical deformation. Drawing inspiration from the mucus of a snail, which effectively adheres to various surfaces in challenging conditions, we present a self-healing xanthan gum/hydrophobically associated polyacrylamide/NaCl (XG/HPAAm/NaCl) hydrogel polymer electrolyte (HPE) that facilitates the creation of flexible SCs with improved mechanical and electrochemical properties. The optimized 2 wt% XG/HPAAm/0.4 M NaCl DN HPE exhibits a high ionic conductivity of 4.0 S/m, a tensile strength of 0.43 MPa, and an elongation at break of 11.7 mm/mm, along with a high adhesive energy of 254.7 J/m². The tough HPE was coated with a mixed adhesive of 502 cyanoacrylate glue and triethyl citrate (TEC) to create a surface coating resembling “mucus”, onto which activated carbon (AC)-modified carbon cloth (CC) electrodes (CC/AC) were affixed on both sides to construct the flexible SCs. Investigations into the HPE’s characteristics and the SCs’ electrochemical performance at various bending angles reveal that the “mucus-coating” HPE exhibits strong electrode adhesion and significantly improved electrochemical performance. The assembled flexible SC delivers a high specific capacitance of 249.3 F/g at 0.30 A/g, retains 73.4% of its initial capacitance after 20,000 cycles, and maintains 86.9% capacitance retention under 180° bending, outperforming SCs assembled with original HPEs in both performance and stability. This approach provides a versatile method for improving the interfacial properties between electrodes and HPEs, paving the way for innovative applications in robust, self-healing, and flexible devices. Full article

The increasing demand for personalized conservative treatment of idiopathic scoliosis (IS) highlights the need for objective and continuous monitoring of corrective forces during brace therapy. This study aims to evaluate the feasibility and clinical relevance of a smart orthopedic brace equipped with integrated force sensors for long-term biomechanical assessment. Three female patients with different types of idiopathic scoliosis were treated using a custom-designed thoracolumbosacral orthosis incorporating four flexible pressure sensors, enabling real-time and long-term recording of corrective forces at key anatomical locations. Sensor data were analyzed in relation to brace-wearing adherence, patient activity, and radiological outcomes assessed using Cobb angle measurements. The results demonstrated substantial variability in force distribution and wearing patterns among patients, which was associated with differences in treatment effectiveness. Higher and more stable corrective forces near curve apices were generally accompanied by improved radiological outcomes, whereas irregular brace use and uneven pressure distribution limited therapeutic effects. Long-term monitoring enabled identification of insufficient correction zones and adherence issues. In conclusion, the proposed sensor-based orthotic system provides clinically relevant information on force distribution and brace use, supporting individualized therapy optimization. These findings indicate that smart braces can enhance clinical decision-making and contribute to more effective and personalized scoliosis management. Full article

Background: Youth with type 1 diabetes (T1D) often show low physical activity levels and a long time spent in sedentary and poor sleep, which may worsen their health. This study aimed to describe baseline movement and sleep patterns in children and adolescents with T1D and compare them with behaviors recorded during a structured summer camp. Methods: Twenty-three participants (13.33 ± 2.13 years) completed physical fitness tests, self-report questionnaires, and 7–8 days of wearable monitoring before camp. During a 10-day diabetes summer camp, participants continued wearing the devices to track physical activity, sedentary time, and sleep. Comparisons between pre- and during-camp periods were performed using paired statistics, and linear regressions examined associations between activity and sleep awakenings. Results: At baseline, device-based monitoring showed low physical activity levels, long sedentary time and poor sleep. Self-reported data confirmed low activity levels and long time spent in sedentary activities, especially screen time. During camp, daily steps increased significantly (p < 0.001), as well as all the physical activity intensities (p < 0.01). Sedentary time decreased significantly (p < 0.001), and sleep duration declined (p < 0.001), but awakenings were shorter (p = 0.005). Baseline sedentary time predicted longer nocturnal awakenings, while greater increases in steps during camp correlated with longer awakenings. Conclusions: Children and adolescents with T1D showed low baseline activity, high sedentary time, and poor sleep. Participation in the structured summer camp appears to be associated with changes in physical activity, sedentary behavior, and sleep patterns. Full article

As global population aging intensifies, fall-related injuries among the elderly have become a critical public health concern. Existing fall detection methods based on wearable IMUs all extract features in the sensor’s body frame, failing to exploit the information embedded in sensor signals. Some higher-performing methods incorporate magnetometer-fused Euler angles to enrich features, but their dependence on specific hardware and fusion algorithms makes exact replication during deployment difficult. In contrast, the proposed MadgwickFall-Net relies on acceleration and angular velocity, and, to the best of our knowledge, for the first time introduces the Madgwick algorithm into fall detection to transform inertial signals into a gravity-aligned global coordinate system. A four-branch parallel architecture processes signals from both coordinate frames, fully exploiting the complementarity between dual-frame signals. Cross-validation on the KFall dataset using 5-fold subject-independent stratification demonstrates an F1-Score of 0.9824 and accuracy of 98.36%, specifically, four main evaluation indicators outperform all comparison models. With only 59.7 KB parameters, the model is suitable for edge device deployment. Rolling inference experiments demonstrate a median pre-impact lead time of 390 ms. MadgwickFall-Net offers a practical and deployable solution for real-world wearable fall detection systems, demonstrating strong potential for protecting elderly individuals in daily life scenarios. 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

Chronic kidney disease (CKD) is a progressive and irreversible disorder affecting over 850 million individuals globally and is associated with significant morbidity, mortality, and healthcare burden. Conventional diagnostic approaches rely on intermittent laboratory measurements, including serum creatinine, estimated glomerular filtration rate (eGFR), and urinary albumin, which provide limited temporal resolution and fail to capture dynamic physiological changes. Recent advances in wearable biosensing technologies offer new opportunities for continuous, non-invasive monitoring of biochemical and physiological markers relevant to renal function. This review provides a comprehensive analysis of wearable biosensors for CKD monitoring, focusing on sensing mechanisms (electrochemical, optical, and field-effect transistor), biofluid interfaces (sweat, interstitial fluid, and saliva), and materials engineering strategies enabling flexible, high-performance devices. Emphasis is placed on biofluid transport dynamics, analytical performance across sampling matrices, and system-level integration with wireless communication and digital health platforms. Key challenges limiting clinical translation, including biofouling, enzymatic instability, and variability in biofluid composition, are examined—alongside emerging solutions such as antifouling interfaces, synthetic recognition elements, and multimodal sensing architectures. Finally, regulatory pathways and the role of artificial intelligence in digital nephrology are discussed. This review highlights the potential of wearable biosensors to transform CKD management through continuous monitoring, early detection, and personalized therapeutic intervention. Full article

Background/Objectives: The aim of this pilot study was to evaluate the feasibility of developing individualized machine learning models using nocturnal wearable-derived autonomic nervous system (ANS) and sleep metrics to predict next-day headache risk in patients with migraine. We also examined the associations between nocturnal ANS and sleep measures and patient-reported outcome measures (PROMs) related to nociplastic pain, migraine burden, and non-restorative sleep (NRS). Methods: Adults with migraine wore the wrist-worn Empatica EmbracePlus^® wearable during sleep and completed daily headache diaries for approximately 4 weeks (N = 10). Participants also completed daily headache diaries and PROMs assessing nociplastic pain, migraine burden, and non-restorative sleep. Personalized machine learning (ML) models were developed to predict next-day headache using nocturnal ANS activity (e.g., pulse rate variability (PRV), electrodermal activity (EDA), respiratory rate (RR)) and sleep metrics (e.g., interruptions, duration, awakenings). Model performance was evaluated using area under the receiver operating characteristic and precision–recall curves (AUROC, AUPRC), sensitivity, specificity, accuracy, and precision. Spearman correlations assessed the relationship between wearable-derived metrics and patient-reported outcome measurements of sleep quality (PROMIS-Fatigue, PROMIS-Sleep Disturbance) and a surrogate marker of nociplastic pain (Fibromyalgia (FM) Score). Results: 9 out of 10 participants wore the EmbracePlus device for at least the target duration of four weeks. For the next-day headache prediction, model performance varied between individuals; area under the ROC curve (AUROC) ranged from 28.2% to 81.2%. Nocturnal measures of EDA were strongly correlated with the FM score (Spearman’s rho = 0.72–0.75, p < 0.05). Conclusions: Phasic EDA may warrant further investigation as a potential physiological indicator related to nociplastic pain mechanisms and next-day headache. However, these findings are preliminary, and larger multicenter trials are needed to confirm results of this pilot study. Full article

Millions of persons worldwide experience varying degrees of hearing loss, traditionally addressed through prosthetic solutions such as hearing aids and cochlear implants. However, a significant proportion of individuals cannot benefit from these technologies, cannot access them, or choose not to use them. In this context, non-prosthetic assistive technologies have emerged as a complementary paradigm, leveraging advances in sensing, artificial intelligence, and wearable computing to transform acoustic information into alternative perceptual representations rather than restoring auditory function. This survey provides a review of such systems, focusing on technologies that enhance environmental awareness, communication, and social interaction. Existing approaches are categorized along two main dimensions: the tasks they perform and the platforms on which they operate. Task-oriented analysis includes sound recognition (speech and non-speech), sound source localization, emotion recognition, sign language recognition, and related emerging functionalities. Platform-based analysis emphasizes wearable devices and mobile solutions enabling real-time and context-aware assistance. The survey further highlights key research trends, including real-time auditory scene analysis, portable processing, and artificial intelligence. It shows that recent studies increasingly demonstrate that combining auditory, visual, and haptic modalities improves robustness and usability in real-world conditions, particularly in noisy and dynamic environments. Finally, open challenges such as energy efficiency, latency, evaluation methodologies, and user acceptance are discussed. By synthesizing existing work and identifying open research directions, this survey aims to provide a structured foundation for future developments in intelligent, non-prosthetic assistive systems that redefine how auditory information is accessed and interpreted. Full article

This work presents a noninvasive imaging method to locate veins using a tuned microwave loop resonator. It offers a low-cost, fast, and effective solution to the challenges in venipuncture. The sensor features a loop resonator with a 5.2 mm radius, incorporating a self-tuning mechanism, and operates at 2.408 GHz with a reflection coefficient of −48.77 dB. It generates localized high-intensity electric fields that penetrate tissues to sufficient depths, enabling the detection of veins based on shifts in resonant frequencies that are induced by the varied dielectric properties of blood vessels. Two-dimensional raster scan simulations of the cephalic and median cubital veins yielded a ∼25 MHz downward resonant-frequency shift between vein and non-vein positions, with the median cubital vein still detectable at depths up to 6 mm. To quantify generalization to real tissues, a decision tree classifier trained on 63 simulation samples and evaluated on 335 in vivo measurements achieved 82.09% classification accuracy (sensitivity 81.25%, specificity 83.02%), demonstrating that the simulation-derived frequency contrast transfers reliably to experimental data despite inter-subject tissue variability. Extensive tests conducted demonstrate the sensor’s effectiveness, producing consistent and distinguishable frequency shifts when the sensor moves on the skin across veins. This technology holds significant promise for improving venipuncture accuracy, minimizing complications, and enhancing patient comfort. Full article

Underwater communication devices typically suffer from large size and high power consumption, which pose significant challenges for real-time monitoring of swimmers’ heart rate and distance. To tackle these challenges, this study successfully developed a wearable acoustic-Bluetooth dual model communication-based real-time heart rate monitoring and ranging system (WARM) for swimmers by implementing an integrated miniaturized acoustic transducer design, narrow-pulse OOK modulation, and acoustic multipath interference suppression techniques. The final self-developed system measures 47 mm × 36 mm × 18 mm and weighs 54 g. Six swimming volunteers were recruited to conduct underwater real-time heart rate monitoring and distance measurement experiments for performance evaluation of this self-developed system. Experimental results demonstrate that within an effective communication range of 2500 cm, the system achieved an average transmission power consumption of 52–58 mW, a frame loss rate of only 1.1%, and a mode-switching time of 1–2 s between the underwater acoustic and Bluetooth transmissions. In addition, the system enabled real-time heart rate monitoring and underwater ranging, with an average ranging error below 50 cm. These results verify the reliability and stability of the proposed system and provide a useful reference for the design and application of wearable underwater communication systems. Full article

Real-time psychological stress detection on wearable and edge devices requires models that are accurate, computationally efficient, and small enough for on-device deployment. This paper proposes a Micro-Attention CNN Hybrid Architecture for stress recognition using wearable bio-signals. The model uses six sensor channels, namely tri-axial acceleration, electrodermal activity, heart rate, and skin temperature, and classifies three stress levels: no stress, low stress, and high stress. This study is conducted on a public wearable sensor dataset collected from 15 nurses during hospital work, providing a realistic benchmark for continuous stress monitoring under practical conditions. The proposed architecture combines one-dimensional and depthwise separable convolutions with a lightweight attention module to emphasize the most informative temporal patterns in short multivariate signal segments. To support deployment on resource-constrained devices, we further apply structured pruning, selective quantization-aware training, and post-training quantization. The full-precision model achieves a Macro-F1 score of 99.63%, while the final compressed model retains 98.03% Macro-F1 with a model size of 1.76 kilobytes and a CPU inference latency of 0.40 ms. Additional analyses show that most residual errors occur near the boundary between low stress and neighboring classes, while simple post-compression calibration improves reliability. These results demonstrate that accurate and low-latency stress detection using wearable bio-signals is feasible on compact edge hardware without transmitting raw sensor streams off-device. Full article

Biomechanics sits at the interface of engineering and medical sciences, offering essential insight into how tissues, organs, and biological systems respond to mechanical loading. This review brings together recent advances in musculoskeletal and cardiovascular biomechanics, illustrating how experimental techniques, computational modeling, and multiscale analysis are used to characterize load transfer, tissue deformation, fatigue, and injury mechanisms. In musculoskeletal applications, predictive simulations, wearable sensing technologies, and neuromechanical assessment tools support improved injury prevention, rehabilitation planning, and assistive device development. In the cardiovascular domain, patient-specific modeling, fluid–structure interaction analyses, and advanced imaging approaches clarify how hemodynamics, vessel wall mechanics, and device–tissue interactions influence disease progression, implant performance, and therapeutic outcomes. Emerging technologies including artificial intelligence, machine learning, digital twin frameworks, biofabrication, soft robotics, and self-powered sensing are enabling data-driven, real-time, and personalized interventions that connect mechanistic understanding with clinical practice. Despite these advances, challenges remain in accounting for individual variability, integrating multiscale data, and translating computational predictions into clinically validated solutions. By emphasizing interdisciplinary strategies that unite biomechanics, computational analytics, and innovative device engineering, this review outlines a pathway toward predictive, patient-centered healthcare and next-generation therapeutic and rehabilitation solutions. Full article