Search Results (5,248)

Flexible heat exchangers with intricate three-dimensional (3D) geometries exhibit superior mechanical and thermal performance compared with traditional two-dimensional (2D) designs. Their ability to offer greater design freedom and unique functionalities makes them particularly attractive for wearable medical devices. This study investigates flexible heat exchanger technologies in three main directions: (i) miniaturisation, (ii) integration of physical and mathematical models, and (iii) enhanced adaptability through heterogeneous design integration. Through a combination of literature review, mathematical modelling, and experimental analysis, the thermal efficiency of several configurations is compared, including basic thermoplastic polyurethane (TPU) tubes and 3D bio-inspired TPU tubes with aluminium-finned structures. The findings establish a foundation for the development of next-generation flexible wearable medical cooling devices with improved thermal management capabilities and practical applicability in industrial design. Furthermore, the outcomes of this research will directly support the development of improved wearable cooling devices within a UK-based medical device SME, Paxman Scalp Coolers, facilitating the translation of advanced heat exchanger designs into clinically relevant and commercially viable solutions. Full article

Ankle–foot orthoses (AFOs) are widely used in the rehabilitation of patients with neurological or musculoskeletal disorders. However, treatment outcomes may be influenced by incorrect use of the device or by inappropriate orthosis selection. Since many types of AFOs are available, differing in materials, stiffness, and geometry, an objective evaluation tool can support clinical decision-making. This work presents the design, development, and characterization of an instrumented AFO able to quantify relevant gait parameters in an objective way. The proposed device integrates three measurement modalities in a compact wearable structure. Two longitudinal strain gauges estimate ankle plantar- and dorsiflexion angles. Two force-sensitive elements detect foot–ground contact and allow identification of stance and swing phases of the gait cycle. A single inertial measurement unit (IMU) is used to measure lateral shank inclination. The strain-gauge-based angle estimation was validated against a gold-standard motion capture system, achieving a root mean square error of approximately 1.6 degrees and showing higher accuracy than the IMU for plantar/dorsiflexion measurement, while maintaining a simple electronic architecture. The force sensors were validated using a force platform and demonstrated reliable detection of loading and unloading events. Monitoring lateral inclination through the single IMU provides additional information related to balance and potential fall risk. Data are transmitted via Bluetooth Low Energy (BLE) to a custom Python-based application for real-time visualization and recording. Overall, the results validate the electronic instrumentation and demonstrate reliable system performance, indicating that the proposed instrumented AFO represents a promising platform for objective gait assessment and future clinical applications. Full article

Conventional gel electrodes are the gold standard for surface electromyography (sEMG), yet their bulkiness, stiffness, and limited gel lifetime prevents seamless day-long integration with wearable robots. We integrated ultrathin skin-conformal temporary tattoo electrodes with a powered unilateral hip exoskeleton and compared signal quality during treadmill walking against gel. In this pilot study, five healthy participants completed three consecutive walking blocks at fixed speed: (1) using gel electrodes; (2) using tattoo electrodes to compare signal quality; and (3) using the same tattoo electrodes (not repositioned) after eight hours of wear to simulate a full day of typical device use and to evaluate potential degradation in signal quality over time. Electrodes were positioned on muscles not covered by the exoskeleton interface (tibialis anterior and gastrocnemius medialis), as well as on muscles located beneath the exoskeleton cuff, which were potentially subject to motion artifacts due to the application of external forces by the exoskeleton (rectus femoris and biceps femoris, BF). Across all muscles, for both gel and tattoo electrodes, the root mean square error (RMSE) between normalized sEMG envelopes and biological activation profile was 0.069 ± 0.048, and Pearson’s correlation coefficient (ρ) was 0.844 ± 0.091. Re-testing the same tattoo electrode pair after eight hours confirmed day-long stability without the need for recalibration. Statistical analysis revealed no significant differences in signal quality, also when applying assistive forces, between the two electrode types and across all muscles (RMSE, all p ≥ 0.3125; ρ, all p ≥ 0.1250), as well as no degradation after eight hours (RMSE and ρ: all p ≥ 0.0626, uncorrected). Finally, in a proof-of-concept session, BF activity measured with tattoo electrodes was found reliable to drive hip-extension assistance in real time. Collectively, these results show that tattoo electrodes deliver signal quality comparable to gel electrodes while offering a low-profile skin-conformal interface and day-long usability, making them a promising option for enhancing EMG-based control in wearable robots. Full article

Flexible electrolyte-gated synaptic field-effect transistors (EGFETs) have emerged as a promising platform for neuromorphic visual systems, owing to their low-voltage operation, diverse synaptic plasticity, and exceptional mechanical flexibility. In particular, gel-based electrolytes, including hydrogels and ion gels, play a pivotal role as functional gate dielectrics, enabling efficient ion transport and strong ion–electron coupling through electric double-layer (EDL) formation. By leveraging these unique properties at the semiconductor/gel interface, EGFETs can effectively emulate essential biological synaptic behaviors, including short-term and long-term plasticity under optical stimulation. The inherent compatibility of EGFETs with a broad range of semiconductor channels, gel electrolytes, and flexible substrates enables the development of wearable and conformable neuromorphic platforms that seamlessly integrate sensing, memory, and signal processing within a single device architecture. Recent advances in gel material engineering, such as polymer network design, ionic modulation, and nanofiller incorporation, have significantly improved ion transport dynamics, interfacial stability, and device performance. Despite remaining challenges related to ion migration stability, multi-physical field coupling, and large-area device uniformity, these developments have substantially advanced the practical potential of gel-based systems. This review provides a comprehensive overview of the operating mechanisms, gel-based material systems, synaptic functionalities, mechanical reliability, and future prospects of flexible electrolyte-gated visual synapses, highlighting their considerable potential for next-generation intelligent perception and artificial vision technologies. Full article

Global warming and increasing heatwaves elevate the risk of exertional heat illnesses, particularly heatstroke, in endurance athletes and outdoor workers. This study developed and validated a wearable heatstroke warning system integrating physiological and environmental monitoring with a real-time web dashboard. The wrist- and finger-worn prototype comprised an ESP32 microcontroller and heart rate (MAX30101), skin temperature (MAX30205), ambient temperature and humidity (SHT31), and galvanic skin response (Grove-GSR v1.2) sensors with dual acoustic–visual alerts and WiFi transmission. Fifteen triathletes (18–39 years) completed 30 min of cycling in a climatic chamber: 0–15 min at 24 ± 1 °C, 70 ± 10% RH, and 16–30 min at 27 ± 1 °C, 90 ± 10% RH, with the workload rising from 40%HRmax by 10% every 10 min. Heart rate, estimated core temperature, ambient temperature, relative humidity, and GSR were recorded every 30 s and compared with standard devices using Spearman correlation (p = 0.01) and Wilcoxon signed-rank tests (p < 0.05). Heart rate, skin temperature (used a linear model to calculate core body temperature), ambient temperature, and humidity sensors showed fair–very good validity (r = 0.692, 0.995, 0.994, 0.952), while GSR was low (r = 0.298). No significant differences were observed for heart rate, skin temperature, and humidity (p > 0.05), but body temperature (p = 0.003) and GSR (p < 0.001) differed. The system showed promising validity for real-time heatstroke risk monitoring, with further refinement needed for skin temperature and GSR sensing. Full article

Four-dimensional (4D) printing integrates additive manufacturing with stimuli-responsive materials to fabricate biomedical implants and functional healthcare devices that undergo programmed, time-dependent changes in shape or function. Unlike static 3D-printed constructs, 4D-printed systems can respond to clinically relevant stimuli such as temperature, hydration, pH, light (including near-infrared), magnetic fields, or electrical inputs. These triggers drive defined actuation mechanisms, most commonly thermomechanical shape-memory recovery, swelling-induced morphing, and magnetothermal activation. This review synthesizes the principal material platforms used for biomedical 4D printing, including shape-memory polymers and alloys, hydrogels, liquid-crystal elastomers, and responsive composites, and links material choice to device behavior and translational feasibility. Applications are discussed across self-expanding stents, cardiac occluders, tissue-engineered constructs, implantable drug delivery systems, and adaptive wearables. Key translational challenges include sterilization compatibility, manufacturing reproducibility and quality control, safe stimulus delivery, predictable biodegradation and long-term biocompatibility, and regulatory pathway definition. Full article

Triboelectric nanogenerators (TENGs) have emerged as versatile self-powered platforms for wearable and implantable biomedical sensing, offering an alternative to battery-dependent electronic devices. By converting biomechanical energy from physiological motion into electrical signals, TENGs enable simultaneous energy harvesting and active sensing within flexible, lightweight, and biocompatible architectures. This review summarizes recent advances from 2020 to 2025 in triboelectric nanogenerator (TENG)-based cardiovascular monitoring. The discussion focuses on material systems, device configurations, sensing mechanisms, and applications including pulse detection and cuffless blood pressure estimation. Representative studies are compared to highlight emerging trends in wearable and self-powered sensing technologies. However, differences in experimental conditions, anatomical sites, calibration methods, and signal-processing approaches limit direct comparison of reported performance. In addition, challenges such as subject-specific calibration, motion artifacts, and limited clinical validation remain. Overall, this review highlights current progress and outlines key challenges for future development and translation of TENG-based cardiovascular monitoring systems. Full article

Hemorrhagic shock remains a leading cause of preventable death in trauma, yet traditional vital signs may fail to reflect early blood loss before physiological compensatory mechanisms are no longer able to maintain hemodynamic stability. The Compensatory Reserve Measurement (CRM) algorithm offers early detection capability using physiological waveforms but requires testing with emerging wearable sensor technologies for operational deployment. This study tested the Epicore Epidermal Patch for Imperceptible Care (EPIC) wearable healthcare device (WHD) for CRM-based hemodynamic monitoring during progressive central hypovolemia induced by lower-body negative pressure (LBNP) to simulate hemorrhage. Twenty participants underwent progressive LBNP while photoplethysmography (PPG) signals were recorded from EPIC sensors placed at the clavicle and triceps alongside a clinical-grade finger pulse oximeter for reference. Signal quality, heart-rate accuracy, and CRM predictions were evaluated across multiple filtering approaches. The triceps placement achieved signal quality comparable to the pulse oximeter reference when Chebyshev Type II filtering was applied, as well as high heart-rate accuracy. CRM derived from the EPIC sensor placed at the triceps tracked compensatory trends during progressive hypovolemia, but prediction magnitudes were inaccurate compared to calculated CRM values. In contrast, the clavicle placement consistently performed poorly across all measurements, regardless of the signal-processing approach. These findings support the feasibility of soft, flexible wearable sensors for continuous hemorrhage monitoring at the triceps location in operational environments where traditional finger-based pulse oximetry is impractical. Full article

Knee joint moment is an important biomechanical parameter for sports assessment, rehabilitation monitoring, and human–machine interaction. However, direct measurement is often restricted to laboratory-based settings. Surface electromyography (sEMG) offers a non-invasive alternative for indirect joint moment estimation, but many existing deep learning models remain too computationally demanding for potential wearable edge deployment. To address this gap, this study proposes Topo2DCNN-LSTM, a lightweight two-dimensional (2D) convolutional neural network model, designed for sagittal-plane knee flexion moment estimation. The model used a feature-based sequential representation, transforming raw sEMG signals into compact Root Mean Square (RMS) feature frames. The input was processed by a lightweight 2D convolutional neural network (CNN) encoder and paired with long short-term memory (LSTM) units. The model was trained on a public walking dataset of healthy subjects with synchronized sEMG and joint kinetics at two treadmill speeds. When compared with selected deep learning baselines, the quantized model achieved a mean RMS Error of 0.088 ± 0.020 Nm/kg at 1.2 m/s and 0.114 ± 0.034 Nm/kg at 1.8 m/s. On a SparkFun Thing Plus–SAMD51, it achieved an average inference latency of 28 ms using 71,316 bytes of random-access memory (RAM) and 257,172 bytes of flash. These results support its use as a proof of concept for personalized unilateral knee moment estimation with isolated on-device inference feasibility under resource-constrained and limited walking conditions. Full article

Flexible acceleration sensors demonstrate revolutionary potential in healthcare, structural vibration monitoring, and consumer electronics owing to their unique conformal adhesion capability and mechanical adaptability. However, current academic research presents two distinct paradigms for realizing flexibility: one is the hybridly flexible sensor, which incorporates traditional micro-electro-mechanical System (MEMS) acceleration sensor chips with flexible packaging/substrates; the other is the intrinsically flexible sensor, whose sensing unit and substrate are entirely composed of flexible materials enabled by microstructural design. This review first analyzes the fundamental differences and design challenges between these two flexible architectures. It then systematically elucidates five core sensing mechanisms—capacitive, piezoresistive, triboelectric, piezoelectric, and electromagnetic—comparing their working principles, material systems, structural designs, and performance metrics. Among these, piezoelectric and triboelectric types exhibit distinctive advantages in self-powering capability, whereas resistive and capacitive approaches offer greater ease of integration. Furthermore, the applications of intrinsically flexible acceleration sensors in structural health monitoring, wearable devices, automotive safety, and other fields are discussed, with particular emphasis on their unique strengths in real-time vibration monitoring. Finally, the review summarizes existing challenges, such as the trade-off between sensitivity and flexibility, and provides theoretical insights to guide future innovations in intrinsically flexible acceleration sensor technology. Full article

Wearable neurotechnology depends critically on continuous movement monitoring to characterize motor impairment and recovery in real-world settings. While joint torque serves as a clinically essential kinetic marker, estimating it directly on-device from inertial signals remains challenging due to stringent computational, memory, and energy constraints. Lightweight pipelines typically omit computationally expensive time–frequency processing; however, this omission degrades the observability of dynamics encoded in 1D IMU signals and diminishes the effectiveness of standard knowledge distillation strategies. To enable reliable on-device torque inference, we propose a Physically Guided Dual-Consistency Knowledge Distillation (PDC-KD) framework that explicitly integrates biomechanical priors into the learning process through two collaborative pathways: parameter-manifold alignment and physics-guided compensation. The student network receives guidance through Fisher-information-weighted parameter transfer, ensuring robust knowledge distillation despite significant model capacity mismatch. Furthermore, the framework incorporates a physics-guided regularization term that enforces dynamically consistent torque trajectories via a numerically stable Cholesky-parameterized constraint. Experiments demonstrate that the student model preserves teacher-level predictive accuracy while operating within the stringent resource constraints of edge devices (achieving a 98% parameter reduction, ∼2× faster inference, and ∼1 ms latency). Moreover, the proposed method yields torque estimates with enhanced dynamical consistency, providing an efficient biosignal-processing solution for wearable neurotechnology platforms demanding real-time movement analytics. Full article

Spiking Neural Networks (SNNs) executed on neuromorphic hardware promise energyefficient, low-latency inference well-suited to edge deployment in size, weight, and powerconstrained environments such as autonomous vehicles, wearable devices, and unmanned aerial platforms. However, a coherent research pathway to deployment of neuromorphic devices remains elusive. This paper presents a structured review and position on the state of SNN-based vision across four interconnected dimensions: network architectures, training methodologies, event-based datasets and simulation techniques, and neuromorphic computing hardware. We survey the evolution from shallow convolutional SNNs to spiking Transformers and hybrid designs which leverage the advantages of SNNs and conventional artificial neural networks. We also examine surrogate gradient training and ANN-to-SNN conversion approaches, catalogue real-world and simulated event-based datasets, and assess the landscape of neuromorphic platforms ranging from rigid mixed-signal architectures to fully-configurable digital systems. Our analysis reveals that while each area has matured considerably in isolation, critical integration challenges persist. In particular, event-based datasets remain scarce and lack standardisation, training methodologies introduce systematic gaps relative to deployment hardware, and access to neuromorphic platforms is restricted by proprietary toolchains and limited development kit availability. We conclude that bridging these integration gaps, rather than advancing individual components alone, represents the most important and least addressed work required to realise the potential of SNN-based vision at the edge. Full article

Heart rate variability (HRV) has emerged as a key biomarker for assessing autonomic nervous system activity, stress, fatigue, and emotional states. With the rapid development of wearable sensor technologies, HRV analysis has expanded from clinical environments to real-world, continuous monitoring. This review summarizes current applications of HRV metrics in wearable devices, including fitness tracking, mental stress assessment, sleep quality evaluation, and early detection of physiological or psychological disorders. Recent advances in photoplethysmography (PPG)-based HRV estimation have enabled noninvasive and user-friendly measurement, though challenges remain in accuracy under motion and variable environmental conditions. We also discuss methodological considerations, such as artifact correction, data segmentation, and the integration of HRV with other biosignals for multimodal analysis. Emerging research suggests that combining HRV with metrics such as respiration rate, skin conductance, and accelerometry can enhance robustness and interpretability in dynamic settings. Finally, future directions are proposed toward personalized health analytics, emotion-aware computing, and real-time adaptive feedback systems. This review highlights the growing potential of wearable HRV analysis as a foundation for preventive healthcare and human–machine symbiosis. Full article

In response to the pressing need for miniaturized MEMS microphones in wearable technology and mobile devices, and to surmount the technical limitations inherent in conventional piezoelectric microphones, which typically depend on enlarging chip dimensions or decreasing stiffness to attain low resonance frequencies, this study introduces a novel piezoelectric MEMS microphone (PMM) design predicated on a flexibility-enhanced rotating structure. The proposed design utilizes an aluminum scandium nitride (Al_0.8Sc_0.2N) piezoelectric thin film with 20% scandium doping and incorporates four equivalent sensing units formed by four curved cutting lines centrally located on the chip. This configuration employs a nested arrangement of four cantilever beams to substantially increase vibration compliance, thereby effectively lowering the natural frequency without altering the chip’s external size. Three-dimensional finite element simulations reveal that, relative to traditional triangular cantilever beam architectures, the flexibility-enhanced rotating structure reduces the natural frequency from 15.6 kHz to 13.49 kHz while enhancing sensitivity from −44.6 dB to −40 dB. The device was fabricated via a comprehensive microfabrication process and subsequently characterized within a standardized acoustic testing environment. Experimental results indicate that the microphone attains a sensitivity of −43.84 dB at 1 kHz and exhibits a first resonance frequency of 13.5 kHz, closely aligning with simulation predictions. Furthermore, the signal-to-noise ratio (SNR) reaches 58.3 dB across the full range of human-audible frequencies. By leveraging the flexibility-enhanced rotating structure, this work achieves an optimal compromise between elevated sensitivity and reduced resonance frequency within a compact form factor, thereby offering a viable technical solution for the advancement of high-performance miniature acoustic sensors. Full article

Pain is a profoundly stressful experience that significantly impacts an individual’s daily life. In many situations, people can express the intensity of pain via some observable physical actions like crying or shouting. However, in cases where the patient is non-communicative, they cannot convey their feelings through these actions. In both scenarios, automatically monitoring pain intensity using technology presents a considerable challenge. In the literature, researchers have presented numerous techniques for automatic pain monitoring using multiple approaches. This technological survey paper aims to provide an overview of current advancements in the field of automatic pain monitoring. In this paper, we present a taxonomy that summarizes our survey on the utilization of technology areas for monitoring pain automatically. Those technologies are based on Internet of Things (IoT), computer vision, and multimodal techniques. These technologies utilize various modalities, including physiological signals, facial expressions, vocalizations, and behavioral patterns, to detect and quantify pain. The paper discusses the advantages and limitations of each modality, as well as the challenges faced in developing accurate and reliable pain monitoring systems. Additionally, the paper surveys the current state of research in this field, including the development of machine learning algorithms and wearable devices for pain monitoring. Overall, this paper provides a comprehensive overview of the current state of automatic pain monitoring technology and highlights areas for future research and development. This paper also creates a keyword map that will serve as a valuable resource for researchers, enabling them to refine their investigations by identifying frequently used terms and emerging trends within each domain. Full article