Search Results (605)

Accurate monitoring of animal behaviors enables improved management in precision livestock farming (PLF), supporting critical applications including health assessment, estrus detection, parturition monitoring, and feed intake estimation. Although both contact and non-contact sensing modalities are utilized, wearable devices with embedded sensors (e.g., accelerometers, pressure sensors) offer unique advantages through continuous data streams that enhance behavioral traceability. Focusing specifically on contact sensing techniques, this review examines sensor characteristics and data acquisition challenges, methodologies for processing behavioral data and implementing identification algorithms, industrial applications enabled by recognition outcomes, and prevailing challenges with emerging research opportunities. Current behavior classification relies predominantly on traditional machine learning or deep learning approaches with high-frequency data acquisition. The fundamental limitation restricting advancement in this field is the difficulty in maintaining high-fidelity recognition performance at reduced acquisition rates, particularly for integrated multi-behavior identification. Considering that the computational demands and limited adaptability to complex field environments remain significant constraints, Tiny Machine Learning (Tiny ML) could present opportunities to guide future research toward practical, scalable behavioral monitoring solutions. In addition, algorithm development for functional applications post behavior recognition may represent a critical future research direction. Full article

In recent decades, flexible electronics have witnessed remarkable advancements in multiple fields, encompassing wearable electronics, human–machine interfaces (HMI), clinical diagnosis, and treatment, etc. Nevertheless, conventional rigid electronic devices are fundamentally constrained by their inherent non-stretchability and poor conformability, limitations that substantially impede their practical applications. In contrast, conductive hydrogels (CHs) for electronic skin (E-skin) and healthcare monitoring have attracted substantial interest owing to outstanding features, including adjustable mechanical properties, intrinsic flexibility, stretchability, transparency, and diverse functional and structural designs. Considerable efforts focus on developing CHs incorporating various conductive materials to enable multifunctional wearable sensors and flexible electrodes, such as metals, carbon, ionic liquids (ILs), MXene, etc. This review presents a comprehensive summary of the recent advancements in CHs, focusing on their classifications and practical applications. Firstly, CHs are categorized into five groups based on the nature of the conductive materials employed. These categories include polymer-based, carbon-based, metal-based, MXene-based, and ionic CHs. Secondly, the promising applications of CHs for electrophysiological signals and healthcare monitoring are discussed in detail, including electroencephalogram (EEG), electrocardiogram (ECG), electromyogram (EMG), respiratory monitoring, and motion monitoring. Finally, this review concludes with a comprehensive summary of current research progress and prospects regarding CHs in the fields of electronic skin and health monitoring applications. Full article

Background: Electroencephalography (EEG) systems based on textile electrodes are increasingly being developed to address the need for more wearable sensor systems for brain function monitoring. Blink-related oscillations (BROs) are a new measure of brain function that corresponds to brainwave responses occurring after spontaneous blinking, and indexes neural processes as the brain evaluates new visual information appearing after eye re-opening. Prior studies have reported BRO utility as both a clinical and non-clinical biomarker of cognition, but no study has demonstrated BRO measurement using textile-based EEG devices that facilitate user comfort for real-world applications. Methods: We investigated BRO measurement using a four-channel EEG system with textile electrodes by extracting BRO responses using existing, publicly available EEG data (n = 9). We compared BRO effects derived from textile-based electrodes with those from standard dry Ag/Ag-Cl electrodes collected at the same locations (i.e., Fp1, Fp2, F7, F8) and using the same EEG amplifier. Results: Results showed that BRO effects measured using textile electrodes exhibited similar features in both time and frequency domains compared to dry Ag/Ag-Cl electrodes. Data from both technologies also showed similar performance in artifact removal and signal capture. Conclusions: These findings provide the first demonstration of successful BRO signal capture using four-channel EEG with textile electrodes, providing compelling evidence toward the development of a comfortable and user-friendly EEG technology that uses the simple activity of blinking for objective brain function assessment in a variety of settings. Full article

Reproductive efficiency is a critical determinant of productivity and profitability in sheep farming. Traditional selection methods have largely relied on phenotypic traits and historical reproductive records, which are often limited by subjectivity and delayed feedback. Recent advancements in artificial intelligence (AI), including video tracking, wearable sensors, and machine learning (ML) algorithms, offer new opportunities to identify behavior-based indicators linked to key reproductive traits such as estrus, lambing, and maternal behavior. This review synthesizes the current research on AI-powered behavioral monitoring tools and proposes a conceptual model, ReproBehaviorNet, that maps age- and sex-specific behaviors to biological processes and AI applications, supporting real-time decision-making in both intensive and semi-intensive systems. The integration of accelerometers, GPS systems, and computer vision models enables continuous, non-invasive monitoring, leading to earlier detection of reproductive events and greater breeding precision. However, the implementation of such technologies also presents challenges, including the need for high-quality data, a costly infrastructure, and technical expertise that may limit access for small-scale producers. Despite these barriers, AI-assisted behavioral phenotyping has the potential to improve genetic progress, animal welfare, and sustainability. Interdisciplinary collaboration and responsible innovation are essential to ensure the equitable and effective adoption of these technologies in diverse farming contexts. Full article

The increasing prevalence of diabetes mellitus necessitates the development of advanced glucose-monitoring systems that are non-invasive, reliable, and capable of real-time analysis. Wearable electrochemical biosensors have emerged as promising tools for continuous glucose monitoring (CGM), particularly through sweat-based platforms. This review highlights recent advancements in enzymatic and non-enzymatic wearable biosensors, with a specific focus on the pivotal role of nanomaterials in enhancing sensor performance. In enzymatic sensors, nanomaterials serve as high-surface-area supports for glucose oxidase (GOx) immobilization and facilitate direct electron transfer (DET), thereby improving sensitivity, selectivity, and miniaturization. Meanwhile, non-enzymatic sensors leverage metal and metal oxide nanostructures as catalytic sites to mimic enzymatic activity, offering improved stability and durability. Both categories benefit from the integration of carbon-based materials, metal nanoparticles, conductive polymers, and hybrid composites, enabling the development of flexible, skin-compatible biosensing systems with wireless communication capabilities. The review critically evaluates sensor performance parameters, including sensitivity, limit of detection, and linear range. Finally, current limitations and future perspectives are discussed. These include the development of multifunctional sensors, closed-loop therapeutic systems, and strategies for enhancing the stability and cost-efficiency of biosensors for broader clinical adoption. Full article

The increasing demand for light and comfort smart wearable devices has promoted the cross-integration of textile technology with nanomaterials and nanotechnology. As a potential candidate with excellent piezoelectricity, PVDF has been processed into different forms used for flexible sensors but shows limited practicality due to their discomfort and stiffness from non-yarn level. In this study, PVDF/BN nanofiber yarns (NFYs) were successfully fabricated via conjugated electrospinning. The effects of BN concentration, stretching temperature, and stretching ratio on the structural morphology and mechanical performance of the NFYs were systematically investigated. The results show that under the stretching temperature of 140 °C and stretching ratios of 3.5, smooth 1% PVDF/BN NFYs with highly oriented inner nanofibers can be successfully fabricated, and the breaking strength and elongation of composite NFYs reached 129.5 ± 8.1 MPa and 22.4 ± 3.8%, respectively, 667% higher than the breaking strength of pure PVDF nanoyarns. Hence, with the selection of appropriate nanofiller amounts and optimized post-treatment process, the structure and mechanical property of PVDF NFYs can be significantly improved, and this study provides an effective strategy to fabricate high-performance nanoyarns, which is favorable to potential applications in wearable electronic devices and flexible piezoelectric sensors. Full article

Electrospun nanofiber-based triboelectric nanogenerators (TENGs) have emerged as a highly promising class of self-powered sensors for a broad range of applications, particularly in intelligent sensing technologies. By combining the advantages of electrospinning and triboelectric nanogenerators, these sensors offer superior characteristics such as high sensitivity, mechanical flexibility, lightweight structure, and biocompatibility, enabling their integration into wearable electronics and biomedical interfaces. This review presents a comprehensive overview of recent progress in electrospun nanofiber-based TENGs, covering their working principles, operating modes, and material composition. Both pure polymer and composite nanofibers are discussed, along with various electrospinning techniques that enable control over morphology and performance at the nanoscale. We explore their practical implementations in both contact-type and non-contact-type sensing, such as human–machine interaction, physiological signal monitoring, gesture recognition, and voice detection. These applications demonstrate the potential of TENGs to enable intelligent, low-power, and real-time sensing systems. Furthermore, this paper points out critical challenges and future directions, including durability under long-term operation, scalable and cost-effective fabrication, and seamless integration with wireless communication and artificial intelligence technologies. With ongoing advancements in nanomaterials, fabrication techniques, and system-level integration, electrospun nanofiber-based TENGs are expected to play a pivotal role in shaping the next generation of self-powered, intelligent sensing platforms across diverse fields such as healthcare, environmental monitoring, robotics, and smart wearable systems. Full article

Eczema, or atopic dermatitis (AD), is a chronic inflammatory skin condition characterized by persistent itching and scratching, significantly impacting patients’ quality of life. Effective monitoring of scratching behaviour is crucial for assessing disease severity, treatment efficacy, and understanding the relationship between itch and sleep disturbances. This review explores current technological approaches for detecting and monitoring scratching and itching in AD patients, categorising them into contact-based and non-contact-based methods. Contact-based methods primarily involve wearable sensors, such as accelerometers, electromyography (EMG), and piezoelectric sensors, which track limb movements and muscle activity associated with scratching. Non-contact methods include video-based motion tracking, thermal imaging, and acoustic analysis, commonly employed in sleep clinics and controlled environments to assess nocturnal scratching. Furthermore, emerging artificial intelligence (AI)-driven approaches leveraging machine learning for automated scratch detection are discussed. The advantages, limitations, and validation challenges of these technologies, including accuracy, user comfort, data privacy, and real-world applicability, are critically analysed. Finally, we outline future research directions, emphasizing the integration of multimodal monitoring, real-time data analysis, and patient-centric wearable solutions to improve disease management. This review serves as a comprehensive resource for clinicians, researchers, and technology developers seeking to advance objective itch and scratch monitoring in AD patients. Full article

Gait, the fundamental form of human locomotion, occurs across diverse environments. The technology for recognizing environmental changes during walking is crucial for preventing falls and controlling wearable robots. This study collected gait data on level ground (LG), ramps, and stairs using a feed-forward neural network (FFNN) to classify the corresponding gait environments. Gait experiments were performed on five non-disabled participants using an inertial measurement unit, a galvanic skin response sensor, and a smart insole. The collected data were preprocessed through time synchronization and filtering, then labeled according to the gait environment, yielding 47,033 data samples. Gait data were used to train an FFNN model with a single hidden layer, achieving a high accuracy of 98%, with the highest accuracy observed on LG. This study confirms the effectiveness of classifying gait environments based on signals acquired from various wearable sensors during walking. In the future, these research findings may serve as basic data for exoskeleton robot control and gait analysis. Full article

This study proposes a novel heart rate estimation method that detects subtle cardiac-induced vibrations propagated through the cardiovascular system based on the ballistocardiography (BCG) principle, using a six-axis heart rate sensing device that integrates a three-axis accelerometer and a three-axis gyroscope. To validate the effectiveness of the proposed method, a comparative analysis was conducted against heart rate measurements obtained from photoplethysmography (PPG) sensors, which are widely used in conventional heart rate monitoring. Experiments were conducted on 20 adult participants, and frequency domain analysis was performed using different time windows of 30 s, 20 s, 8 s, and 4 s. The results showed that the 4 s window provided the highest accuracy in heart rate estimation, demonstrating that the proposed method can effectively capture fine cardiac-induced vibrations. This approach offers a significant advantage by utilizing inertial sensors commonly embedded in wearable devices for heart rate monitoring without the need for additional optical sensors. Compared to optical-based systems, the proposed method is more power-efficient and less affected by environmental factors such as ambient lighting conditions. The findings suggest that heart rate estimation using the six-axis heart rate sensing device presents a reliable, continuous, and non-invasive alternative for cardiovascular monitoring. Full article

Human activity recognition (HAR) using Wi-Fi-based sensing has emerged as a powerful, non-intrusive solution for monitoring human behavior in smart environments. Unlike wearable sensor systems that require user compliance, Wi-Fi channel state information (CSI) enables device-free recognition by capturing variations in signal propagation caused by human motion. This makes Wi-Fi sensing highly attractive for ambient healthcare, security, and elderly care applications. However, real-world deployment faces two major challenges: (1) significant cross-subject signal variability due to physical and behavioral differences among individuals, and (2) limited labeled data, which restricts model generalization. To address these sensor-related challenges, we propose TCN-MAML, a novel framework that integrates temporal convolutional networks (TCN) with model-agnostic meta-learning (MAML) for efficient cross-subject adaptation in data-scarce conditions. We evaluate our approach on a public Wi-Fi CSI dataset using a strict cross-subject protocol, where training and testing subjects do not overlap. The proposed TCN-MAML achieves 99.6% accuracy, demonstrating superior generalization and efficiency over baseline methods. Experimental results confirm the framework’s suitability for low-power, real-time HAR systems embedded in IoT sensor networks. Full article

Hydrogel sensors show unique advantages in underwater detection, ocean monitoring, and human–computer interaction because of their excellent flexibility, biocompatibility, high sensitivity, and environmental adaptability. However, due to the water environment, hydrogels will dissolve to a certain extent, resulting in insufficient mechanical strength, poor long-term stability, and signal interference. In this paper, a double-network structure was constructed by polyvinyl alcohol (PVA) and poly([2-(methacryloyloxy) ethyl]7 dimethyl-(3-sulfopropyl) ammonium hydroxide) (PSBMA). The resultant PVA/PSBMA-PA hydrogel demonstrated notable swelling resistance, a property attributable to the incorporation of non-covalent interactions (electrostatic interactions and hydrogen bonding) through the addition of phytic acid (PA). The hydrogel exhibited high stretchability (maximum tensile strength up to 304 kPa), high conductivity (5.8 mS/cm), and anti-swelling (only 1.8% swelling occurred after 14 days of immersion in artificial seawater). Assembled as a sensor, it exhibited high strain sensitivity (0.77), a low detection limit (1%), and stable electrical properties after multiple tensile cycles. The utilization of PVA/PSBMA-PA hydrogel as a wearable sensor shows promise for detecting human joint movements, including those of the fingers, wrists, elbows, and knees. Due to the excellent resistance to swelling, the PVA/PSBMA-PA-based sensors are also suitable for underwater applications, enabling the detection of underwater mannequin motion. This study proposes an uncomplicated and pragmatic methodology for producing hydrogel sensors suitable for use within subaquatic environments, thereby concomitantly broadening the scope of applications for wearable electronic devices. Full article

A prior history of falls remains the strongest predictor of future falls in individuals with Parkinson’s disease (PD). There are limited biomarkers available to identify falls risk before falls begin to occur. The aim of this review is to investigate if features associated with falls risk may be detected by wearable sensors in patients with PD. A systematic search of the MEDLINE, EMBASE, Cochrane, and Cinahl databases was performed. Key quality criteria include sample size adequacy, data collection procedures, and the clarity of statistical analyses. The data from each included study were extracted into defined data extraction spreadsheets. Results were synthesized in a narrative manner. Twenty-four articles met the inclusion criteria. Of these, twelve measured falls prospectively, while the remaining relied on retrospective history. The definition of a “faller” varied across studies. Most assessments were conducted in a clinical setting (18/24). There was considerable variability in sensor placement and mobility tasks assessed. The most common sensor-derived measures that significantly differentiated “fallers” from “non-fallers” in Parkinson’s disease included gait variability, stride variability, trunk motion, walking speed, and stride length. Full article

Fibrous structure is a promising building block for developing high-performance wearable piezoresistive sensors. However, the inherent non-conductivity of the fibrous polymer remains a bottleneck for highly sensitive and fast-responsive piezoresistive sensors. Herein, we reported a polyaniline/reduced graphene oxide @ polydopamine/poly (vinylidene fluoride) (PANI/rGO@PDA/PVDF) nanofiber piezoresistive sensor (PNPS) capable of versatile wearable biomonitoring. The PNPS was fabricated by integrating rGO sheets and PANI particles into a PDA-modified PVDF nanofiber network, where PDA was implemented to boost the interaction between the nanofiber networks and functional materials, PANI particles were deposited on a nanofiber substrate to construct electroactive nanofibers, and rGO sheets were utilized to interconnect nanofibers to strengthen in-plane charge carrier transport. Benefitting from the synergistic effect of multi-dimensional electroactive materials in piezoresistive membranes, the as-fabricated PNPS exhibits a high sensitivity of 13.43 kPa⁻¹ and a fast response time of 9 ms, which are significantly superior to those without an rGO sheet. Additionally, a wide pressure detection range from 0 to 30 kPa and great mechanical reliability over 12,000 cycles were attained. Furthermore, the as-prepared PNPS demonstrated the capability to detect radial arterial pulses, subtle limb motions, and diverse respiratory patterns, highlighting its potential for wearable biomonitoring and healthcare assessment. Full article

Dynamic oxygen uptake (VO₂) reflects moment-to-moment changes in oxygen consumption during exercise and underpins training design, performance enhancement, and clinical decision-making. We tackled two key obstacles—the limited fusion of heterogeneous sensor data and inadequate modeling of long-range temporal patterns—by integrating wearable accelerometer and heart-rate streams with a convolutional neural network–LSTM (CNN-LSTM) architecture and optional attention modules. Physiological signals and VO₂ were recorded from 21 adults through resting assessment and cardiopulmonary exercise testing. The results showed that pairing accelerometer with heart-rate inputs improves prediction compared with considering the heart rate alone. The baseline CNN-LSTM reached R² = 0.946, outperforming a plain LSTM (R² = 0.926) thanks to stronger local spatio-temporal feature extraction. Introducing a spatial attention mechanism raised accuracy further (R² = 0.962), whereas temporal attention reduced it (R² = 0.930), indicating that attention success depends on how well the attended features align with exercise dynamics. Stacking both attentions (spatio-temporal) yielded R² = 0.960, slightly below the value for spatial attention alone, implying that added complexity does not guarantee better performance. Across all models, prediction errors grew during high-intensity bouts, highlighting a bottleneck in capturing non-linear physiological responses under heavy load. These findings inform architecture selection for wearable metabolic monitoring and clarify when attention mechanisms add value. Full article