Search Results (411)

While personal thermal comfort is critical for well-being and productivity, it is often overlooked by traditional building management systems that rely on uniform settings. Modern data-driven approaches often fail to capture the complex interactions between various data streams. This pilot study introduces a high-accuracy, interpretable framework for thermal comfort classification, designed to identify the most significant predictors from a comprehensive suite of environmental, physiological, and anthropometric data in a controlled group of young adults. Initially, an XGBoost model using the full 24-feature dataset achieved the best performance at 91% accuracy. However, after using SHAP analysis to identify and select the most influential features, the performance of our ensemble models improved significantly; notably, a Random Forest model’s accuracy rose from 90% to 94%. Our analysis confirmed that for this homogeneous cohort, environmental parameters—specifically temperature, humidity, and CO₂—were the dominant predictors of thermal comfort. The primary strength of this methodology lies in its ability to create a transparent pipeline that objectively identifies the most critical comfort drivers for a given population, forming a crucial evidence base for model design. The analysis also revealed that the predictive value of heart rate variability (HRV) diminished when richer physiological data, such as diastolic blood pressure, were included. For final validation, the optimized Random Forest model, using only the top 10 features, was tested on a hold-out set of 100 samples, achieving a final accuracy of 95% and an F1-score of 0.939, with all misclassifications occurring only between adjacent comfort levels. These findings establish a validated methodology for creating effective, context-aware comfort models that can be embedded into intelligent building management systems. Such adaptive systems enable a shift from static climate control to dynamic, user-centric environments, laying the critical groundwork for future personalized systems while enhancing occupant well-being and offering significant energy savings. 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

Background/Objectives: Dental anxiety in children is a common issue that can hinder the delivery of effective dental care. Traditional approaches to managing this are often insufficient or involve pharmacological interventions. This study shows the potential of virtual reality (VR) to aid in reducing anxiety in children undergoing simple dental procedures. By immersing children in relaxing VR environments (such as beaches, forests, mountains, or underwater scenes with calm music), the objective is to assess VR’s effectiveness in calming pediatrics patients during these procedures. Methods: Children scheduled for minor dental treatments wore a wearable device that monitored pulse, perspiration, and stress levels. Each child’s baseline data was collected without the VR headset, followed by data collection during VR exposure before and during dental procedures. VR scenarios ranged from soothing nature scenes to animated cartoons, designed to foster relaxation. Results: The data collected showed a reduction in physiological indicators of stress, such as lower heart rate and reduced perspiration, when the VR headset was used. Children appeared more relaxed, with a calmer response during the procedure itself, compared to baseline levels without VR. Conclusions: This study provides preliminary evidence supporting VR as an effective tool for reducing anxiety and stress in pediatric dental patients. By offering an engaging, immersive experience, VR can serve as an alternative or complementary approach to traditional anxiety management strategies in pediatric dentistry, potentially improving patient comfort and cooperation during dental procedures. Further research could determine if VR may serve as an alternative to local anesthesia for non-intrusive pediatric dental procedures. 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

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

The development of wearable robotic exoskeletons has seen rapid progress in recent years, driven by the growing need for technologies that support motor rehabilitation, assist individuals with physical impairments, and enhance human capabilities in both clinical and everyday contexts. Within this field, elbow exoskeletons have emerged as a key focus due to the joint’s essential role in upper limb functionality and its frequent impairment following neurological injuries such as stroke. With increasing research activity, there is a strong interest in evaluating these systems not only from a technical perspective but also in terms of user comfort, adaptability, and clinical relevance. This review investigates recent advancements in elbow exoskeleton technology, evaluating their effectiveness and identifying key design challenges and limitations. Devices are categorized based on three main criteria: mechanical structure (rigid, soft, or hybrid), actuation method, and sensing technologies. Additionally, the review classifies systems by their supported range of motion, flexion–extension, supination–pronation, or both. Through a systematic analysis of these features, the paper highlights current design trends, common trade-offs, and research gaps, aiming to guide the development of more practical, effective, and accessible elbow exoskeletons. Full article

Textile-based wearable devices are rapidly gaining traction in the Internet of Things paradigm and offer distinct advantages for data collection and analysis across a wide variety of applications. Seamlessly integrating electronics in textiles remains a technical challenge, especially when the textiles’ essential properties, such as comfort, breathability, and flexibility, are meant to be preserved. This article investigates screen printing as a textile post-processing technique for electronic integration, and highlights its versatility, cost-effectiveness, and adaptability in terms of design and customization. The study examines two silver-based inks screen-printed on an Oxford polyester textile substrate with a focus on substrate preparation and treatment. Before printing, the textile samples were cleaned with nitrogen gas and then subjected to low-pressure oxygen plasma treatment. For comparative analysis, two samples printed on polyethylene terephthalate (PET) serve as a reference. The findings highlight the importance of plasma treatment in optimizing the printability of textiles and demonstrate that it notably improves the electrical properties of conductive inks. Despite some remaining challenges, the study indicates that screen-printed electronics show promising potential for advancing the development of e-textiles and sensor-integrated wearables. Full article

This paper presents a novel design for a bionic knee exoskeleton equipped with a variable stiffness actuator based on rope-driven artificial muscles. To meet the varying stiffness requirements of the knee joint across different gait modes, the actuator dynamically switches between multiple rope bundle configurations, thereby enabling effective stiffness modulation. A mathematical model of the knee exoskeleton is developed, and the mechanical properties of the selected flexible aramid fiber ropes under tensile loading are analyzed through both theoretical and experimental approaches. Furthermore, a control framework for the exoskeleton system is proposed. Wearable experiments are conducted to evaluate the effectiveness of the variable stiffness actuation in improving compliance and comfort across various gait patterns. Electromyography (EMG) results further demonstrate that the exoskeleton provides a compensatory effect on the rectus femoris muscle. Full article

This study aimed to examine the effect of the orientation angle of center facing arms on the elongation and strength of 3D-printed textiles with two different re-entrant cellular auxetic structures. An experimental research design, consisting of 6 (auxetic-structure textiles) × 3 (repetition), was employed. Star-shaped re-entrant auxetic structures (star re-entrant) with orientation angles of 25°, 30°, and 35° and floral-based star-shaped re-entrant auxetic structures (floral re-entrant) with orientation angles of 55°, 60°, and 65° were developed using the fused deposition modeling 3D-printing method through identifying commonly used auxetic structures in the 3D-printed textiles’ development. A statistically significant relationship was found between load and elongation of both star re-entrant and floral re-entrant. The findings indicated that 3D-printed textiles with both star re-entrant and floral re-entrant structures exhibited an enhanced elongation with the increase in orientation angle, making the textile products more flexible and potentially providing better wear comfort. However, the strength of both star re-entrant and floral re-entrant textiles was not significantly affected by the orientation angle of center facing arms. The findings demonstrated the potential to enhance the elongation of 3D-printed auxetic-structure textiles without compromising their strength for ensuing comfort by adjusting the orientation angle of center facing arms. Full article

Cardiovascular disease (CVD) is the leading cause of global mortality, with hypertension affecting over one billion people. Current noninvasive blood pressure (BP) systems, like cuffs, suffer from discomfort and placement errors and lack continuous monitoring. Wearable solutions promise improvements, but technologies like photoplethysmography (PPG) and bioimpedance (BIOZ) face usability and clinical accuracy limitations. PPG is sensitive to skin tone and body mass index (BMI) variability, while BIOZ struggles with electrode contact and reusability. We present a novel, strain gauge-based wearable BP device that directly quantifies pressure via a dual transducer system, compensating for tissue deformation and external forces to enable continuous, accurate BP measurement. The reusable, energy-efficient, and compact design suits long-term daily use. A novel leg press protocol across 10 subjects (systolic: 71.04–241.42 mmHg, diastolic: 53.46–123.84 mmHg) validated its performance under dynamic conditions, achieving mean absolute errors of 2.45 ± 3.99 mmHg (systolic) and 1.59 ± 2.08 mmHg (diastolic). The device showed enhanced robustness compared to the Finapres, with less motion-induced noise. This technology significantly advances current methods by delivering continuous, real-time BP monitoring without reliance on electrodes, independent of skin tone, while maintaining a high accuracy and user comfort. Full article

The complex structure of the wrist joint supports the hand to complete a variety of dexterous and accurate operations in daily living, which in turn makes it vulnerable to motor injury due to stroke, sports, occupational, or traffic accidents. As a supplement to traditional medical treatment, timely and effective rehabilitation training can accelerate the recovery process of wrist motor function. The wearable rehabilitation device in this work exhibits excellent application prospects in the field of human rehabilitation training due to its inherent flexibility and safety. Inspired by the motion principle of tendons and muscles, a modular vacuum-actuated artificial muscle (VAM) is proposed, with the advantages of being lightweight and having a high contraction ratio. The VAMs are applied to the development of a wearable wrist rehabilitation device (WWRD) prototype, which can realize wrist rehabilitation training in the motion directions of extension, flexion, radial deviation, and ulnar deviation. The design concept, structural model, and motion analysis of a WWRD are introduced to provide a reference for the design and analysis of the WWRD prototype. To evaluate the performance of the WWRD, we establish the force and motion parameter models of the WWRD and carry out performance experiments. The process of wrist rehabilitation training is tested and evaluated, which indicates that the WWRD with VAMs will enhance flexibility, comfort, and safety in wrist rehabilitation training. This work is expected to promote the development of high-performance wearable wrist rehabilitation devices based on an understanding of the bionic vacuum-actuated artificial muscles. Full article

With growing interest in wearable technologies, the development of flexible sensors and products that can monitor the human body while being comfortable to wear is gaining momentum. While various textile-based strain sensors have been proposed, their implementation in practical, exercise-specific applications remains limited. In this study, we developed a knitted strain sensor that monitors elbow angles, focusing on dumbbell exercise, which is a basic exercise in sports, and verified its performance. The material of the developed knitted strain sensor with a plain stitch structure comprised a silver-coated nylon conductive yarn and an acrylic/wool blended yarn. To evaluate the electrical and physical characteristics of the developed sensor, a textile folding tester was used to conduct 100 repeated bending experiments at three angles of 30°, 60°, 90° and speeds of 10, 30, 60 cpm. The system demonstrated excellent elasticity, high sensitivity (gauge factor = 698), fast responsiveness, and reliable performance under repeated stress, indicating its potential for integration into wearable fitness or rehabilitation platforms. Full article

Advancements in thermoregulating textiles have been propelled by innovations in nanotechnology, composite materials, and smart fiber engineering. This article reviews recent scholarly papers on experimental passive and active thermoregulating textiles to present the latest advancements in these fabrics, their mechanisms of thermoregulation, and their feasibility for use. The review underscores that phase-change materials enhanced with graphene, boron nitride, and carbon nanofibers offer superior thermal conductivity, phase stability, and flexibility, making them ideal for wearable applications. Shape-stabilized phase-change materials and aerogel-infused fibers have shown promising results in outdoor, industrial, and emergency settings due to their durability and high insulation efficiency. Radiative cooling textiles, engineered with hierarchical nanostructures and Janus wettability, demonstrate passive temperature regulation through selective solar reflection and infrared emission, achieving substantial cooling effects without external energy input. Thermo-responsive, shape-memory materials, and moisture-sensitive polymers enable dynamic insulation and actuation. Liquid-cooling garments and thermoelectric hybrids deliver precise temperature control but face challenges in portability and power consumption. While thermoregulating textiles show promise, the main challenges include achieving scalable manufacturing, ensuring material flexibility, and integrating multiple functions without sacrificing comfort. Future research should focus on hybrid systems combining passive and active mechanisms, user-centric wearability studies, and cost-effective fabrication methods. These innovations hold significant potential for applications in extreme environments, athletic wear, military uniforms, and smart clothing, contributing to energy efficiency, health, and comfort in a warming climate. Full article

Sleep posture detection is a potentially important component of sleep quality assessment and health monitoring. Accurate identification of sleep postures can offer valuable insights into an individual’s sleep patterns, comfort levels, and potential health risks. For example, improper sleep postures may lead to musculoskeletal issues, respiratory disturbances, and even worsen conditions like sleep apnea. Additionally, for long-term bedridden patients, continuous monitoring of sleep postures is essential to prevent pressure ulcers and other complications. Traditional methods for sleep posture detection have several limitations: wearable sensors can disrupt natural sleep and cause discomfort, camera-based systems raise privacy concerns and are sensitive to environmental conditions, and pressure-sensing mats are often complex and costly. To address these issues, we have developed a low-cost non-contact sleeping posture detection system. Our system features eight optimally distributed triaxial accelerometers, providing a comfortable and non-contact front-end data acquisition unit. For sleep posture classification, we employ an improved density peak clustering algorithm that incorporates the K-nearest neighbor mechanism. Additionally, we have constructed a Parallel Convolutional Spatiotemporal Network (PCSN) by integrating Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) modules. Experimental results demonstrate that the PCSN can accurately distinguish six sleep postures: prone, supine, left log, left fetus, right log, and right fetus. The average accuracy is 98.42%, outperforming most state-of-the-art deep learning models. The PCSN achieves the highest scores across all metrics: 98.64% precision, 98.18% recall, and 98.10% F1 score. The proposed system shows considerable promise in various applications, including sleep studies and the prevention of diseases like pressure ulcers and sleep apnea. Full article

Quantifying inflammation plays a critical role in understanding the progression and development of various diseases. Non-invasive or minimally invasive wearable biosensors have garnered significant attention in recent years due to their convenience, comfort, and ability to provide continuous monitoring of biomarkers, particularly in infectious diseases and chronic diseases. However, there are still areas for improvement in developing reliable biosensing devices to detect key inflammatory biomarkers in clinically relevant biofluids. This review first introduces common biofluids with a focus on the most clinically significant inflammatory biomarkers. Specifically, it discusses the challenges encountered in extracting and detecting analytes in these biofluids. Subsequently, we review three popular types of non-invasive wearable biosensors for inflammation monitoring (microneedle patches, flexible electronic skins, and textile-based sensors). The design and operational considerations of these devices are analyzed, followed by an exploration of the information processing approaches employed during data processing. Finally, we envision future opportunities by guiding the development and refinement of non-invasive or minimally invasive wearable biosensors for continuous inflammation monitoring in chronic diseases. Full article