Flame-retardant and electromagnetic-interference-shielding silver nanowire/silicon dioxide/aramid nanofiber/sodium alginate lightweight porous composite for wearable sensors.

Smart graphene oxide-based advanced textiles: A review of wearable smart clothing, personal thermal management, and sensor applications

Effects of virtual reality training on musical performance-related physiological responses: A mixed-reality physiological study

Continuous and scalable preparation of highly conductive PEDOT: PSS-based fibers for wearable smart textiles

Patients with Parkinson's disease (PD) suffer from interrelated motor and non-motor symptoms. While most research focuses on motor improvement, this study investigated whether targeting mood via sequential bilateral dorsolateral prefrontal cortex (DLPFC) tDCS could favorably affect motor function in patients maintaining a stable medication 'ON' state. Additionally, we employed wearable smart devices to objectively evaluate real-world changes in daily activity and sleep patterns, complementing traditional clinician-rated scales. PD patients with mild-to-moderate depressive symptoms were enrolled. All participants completed a 7-day baseline monitoring period using a smart band. Participants received ten sessions of bilateral tDCS targeting the DLPFC (anode F3, cathode F4) at 2 mA for 30 minutes, three times a week. Clinical assessments and smart band monitoring were repeated during the final week of treatment. Pre-post changes and correlations were analyzed while controlling for potential confounders. Following tDCS, it was significant improvements in K-MADRS, STAI, AS, UPDRS part III, and PDQ-39. Smart device data showed a significant increase in daily step counts after treatment, while changes in physical activity time and sleep duration were not significant. Changes in step count were strongly correlated with improvements in apathy, and this relationship remained significant after confounding variables (rho = -0.76, p < 0.001). Bilateral DLPFC tDCS significantly improved mood and motor function in patients with PD. Smart band data further showed an increase in daily step counts after the intervention, with reductions in apathy. These findings suggest that tDCS may enhance goal-directed behavior by modulating mood-related pathways, highlighting apathy as an important therapeutic target in PD.

Background and Aim. The growing integration of wearable smart technologies in sports has transformed the way individuals monitor and enhance their physical activity and performance. Devices such as smartwatches and fitness trackers enable users to track physiological and performance-related data, which may influence motivation and training behaviours. Despite their increasing popularity, understanding the factors influencing consumer adoption and acceptance of these technologies in sports remains important. Therefore, the objective of this study was to investigate consumer attitudes towards wearable smart technologies in sports and to examine how perceived training effectiveness, device design satisfaction, and enhanced sports experience influence users’ intention to adopt and continue using these technologies. Methods. A quantitative cross-sectional survey was conducted among 601 participants using a structured questionnaire assessing perceptions of usability, functionality, and motivational impact of wearable smart devices in sports. Data were analysed using IBM SPSS Statistics. Descriptive statistics were used to summarise demographic characteristics, while Pearson correlation analysis was performed to examine relationships among variables. Results. Among the participants, 49.4% were female, 24.6% were male, and 26.0% preferred not to disclose their gender. Correlation analysis revealed several statistically significant but weak relationships between device characteristics and user perceptions. A positive correlation was observed between perceived training effectiveness and satisfaction with device design (r = 0.107, p &lt; 0.01). Additionally, enhanced sports experience was positively associated with the intention to continue using wearable technologies (r = 0.097, p &lt; 0.05). Conclusions. The findings indicate that consumers generally have positive perceptions of wearable smart technologies in sports, with usability, design, and perceived performance benefits influencing their acceptance and continued use. Keywords: Wearable technology; smart wearable devices; technology adoption; consumer acceptance; sports technology

MXene or MXene‐based composite fibers hold potential for powering flexible, wearable smart garments as flexible capacitors. However, pure MXene suffers from extremely poor mechanical properties, while MXene composite fibers exhibit low specific capacitance, both of which limit their practicality for wearable smart clothing applications. To overcome the challenge of low specific capacitance in MXene composite fibers, carbon nanotube (CNT) yarn was acted as an important substrate to be coated with MXene to obtain MXene/CNT yarn supercapacitor (YSC) with excellent electromagnetic interference shielding (EMI). By fully leveraging the synergistic effect between MXene and CNT yarn, the MXene/CNT yarn achieved a conductivity of 1263.82 S/cm with a MXene loading of 0.41 mg/cm. The thermodynamics and kinetics of the coating process were examined. The MXene/CNT YSC achieved a specific capacitance of 70.25 F cm −1 at 20 mV s −1 and 183 F cm −3 at 0.1 A cm −3 and a capacitance retention of ≈86% after 12,000 galvanostatic charge–discharge (GCD) cycles at 0.1 A cm −3 . A fully woven MXene/CNT fabric provided EMI shielding of 35 dB with a thickness of 0.45 mm. Our work provided a new strategy for fabricating energy storage yarns with outstanding EMI shielding performance.

Scalable fabrication of structurally colored composite films (SCCFs) integrating facile processability, mechanical robustness, and environmental stability remains a critical challenge. Herein, a supramolecular processing and covalent locking strategy to address this bottleneck is proposed, enabling the room-temperature production of cross-linked SCCFs (C-SCCFs) from commercially available materials. This approach leverages dynamic supramolecular interactions between SiO2 colloidal particles and a cross-linkable methacrylated polyethylenimine (PEI-MA) to facilitate rapid, shear-induced colloidal ordering. Subsequent UV-triggered covalent cross-linking of methacrylate groups permanently locks this ordered structure into a dense covalent network. Optimization of cross-linking density and colloid volume fraction provided a balance between flexibility, strength, and optical intensity. The resulting C-SCCFs exhibit high optical reflectance, tunable colors, and excellent mechanical robustness, withstanding over 12,000 bending cycles and multiple swelling-drying processes. Furthermore, we demonstrate the versatility of this platform by creating laser-engraved patterns, conformal coatings on 3D surfaces, and durable integrations with textiles. This work establishes a general and scalable route to high-performance photonic materials, paving the way for applications in wearable technology, anticounterfeiting, and smart coatings.

Wearable health monitoring technologies are growing at over 20% annually, highlighting the shift toward connected and preventive healthcare. In practical scenarios such as hospitals, ICUs, home healthcare, elderly care, and remote patient monitoring, there is a critical need for systems that can continuously track vital health parameters and provide instant alerts during emergencies. These environments demand portable, real-time, and reliable solutions capable of monitoring patient conditions both locally and remotely. Traditional healthcare monitoring relies on periodic manual measurements and hospital-based equipment, which may fail to detect sudden health abnormalities in time and lack continuous tracking capabilities. Furthermore, conventional systems do not provide real-time remote access, automated alerting, or integrated data analytics, limiting their effectiveness in modern healthcare. To address these challenges, the proposed IoT-Driven Tele-ECG Health Monitor utilizes the ESP32 microcontroller to develop a wearable and intelligent health monitoring system. The system integrates heart rate sensors, temperature sensors, and vibration sensors to continuously monitor physiological parameters, while the ESP32 processes and displays data on an LCD for local monitoring. Through built-in Wi-Fi connectivity, the system transmits real-time health data to an IoT cloud platform via a gateway, enabling remote monitoring and data visualization. The system automatically detects abnormal conditions such as irregular heart rate or elevated temperature and sends instant alerts to doctors or caregivers. This portable and IoT-enabled solution enhances patient safety, enables early diagnosis, supports remote healthcare delivery, and contributes to the advancement of smart and connected medical systems.

Triboelectric nanogenerators (TENGs) have been widely explored for wearable motion monitoring, yet existing designs are mainly confined to single-site sensing, limiting their ability to capture coordinated multijoint movements. Here, a multinetwork conductive hydrogel with both robustness and conductivity was developed and further utilized to construct a TENG sensor, which demonstrated outstanding durability by maintaining stable output voltage over 3000 operating cycles with 6.8% signal decay after 15 days of storage (25 ± 2 °C), as well as rapid response and recovery times of 34 and 15 ms. To capture motion signals, TENG sensors were strategically deployed at the plantar, knee, shoulder, and wrist. Building upon this sensing platform, two integrated systems were established through the integration of a data processing hardware module and a personal computer (PC) software module: a real-time kinetic chain signal monitoring system (RKCSMS) and a wireless intelligent sports evaluation system (WISES). The two systems enabled accurate movement recognition, systematic assessment of kinetic chain patterns, and real-time feedback. This study demonstrated the strong potential of TENGs based on conductive hydrogels for applications in flexible smart wearables, rehabilitation assessment, sports training, and health management.

Multifunctional conductive polyaniline/cotton fabric with integrated EMI shielding and flame retardancy via in situ interfacial polymerization.

Graphene incorporation into polymer fibers offers a strategy to tune nanoscale morphology while preserving mechanical conformity for flexible composite applications. Graphene-based dopants can enable modulation of polymer fiber structure; however, the relationship between graphene incorporation, fiber morphology, and mechanical flexibility must be evaluated. This study investigates the integration of graphene oxide (GO) and reduced graphene oxide (RGO) into fibrous materials to tailor the structural and surface characteristics by fabricating GO- and RGO-enhanced poly(vinylidene fluoride) (PVDF) fibers via a wet-spinning process and examining the tunability of their morphology and its influence on mechanical properties. The effect of graphene doping and reduction state on fiber architecture is explored using scanning electron microscopy (SEM), atomic force microscopy (AFM), and Brunauer-Emmett-Teller (BET) surface area analysis. Fourier transform infrared (FTIR) and Raman spectroscopy analyses confirmed the incorporation and reduction of graphene derivatives within the PVDF matrix while revealing corresponding changes in chemical functionality and the piezoelectric phase of PVDF. Mechanical flexibility is assessed through tensile testing, revealing increased stiffness with graphene addition, although maintaining sufficient structural integrity for wearable applications. These results collectively demonstrate that graphene doping provides a facile route to engineer composite fibers, enabling a balance between morphological complexity and mechanical compliancy, while establishing graphene-enhanced fibers as promising materials for flexible sensing systems and wearable smart textiles.

The rapid evolution of wearable technology has intensified the demand for advanced, multifunctional materials that enable seamless human health monitoring, environmental sensing, and the integration of intelligent systems. As wearable sensors become increasingly integrated into smart technologies, they are revolutionizing remote health tracking, personal wellness management, and environmental monitoring. Among the diverse range of materials explored for such applications, two‐dimensional (2D) transition metal dichalcogenides (TMDs) have emerged as frontrunners because of their exceptional mechanical flexibility, superior electrical conductivity, optical tunability, and distinctive surface chemistry. In particular, molybdenum dichalcogenides (MoX 2 , X = S, Se, Te) have demonstrated remarkable potential in next‐generation wearable devices, offering tunable electronic properties, high sensitivity, and excellent stability. This review provides a comprehensive overview of recent advancements in MoX 2 ‐based wearable supercapacitors, gas sensors, and health monitoring technologies, emphasizing their fundamental properties, fabrication strategies, and integration challenges. Unlike conventional materials, MoX 2 nanostructures enable the development of ultrathin, high‐performance, and highly adaptive wearables with enhanced functionalities. Additionally, we present a holistic perspective that bridges material science with real‐world applications, offering a strategic roadmap for future research in wearable devices.

The integration of multi-modal wearable smart devices is revolutionizing sports science, facilitating a paradigm shift from subjective coaching to a quantitatively-driven, personalized athletic development model. This study designs, implements, and validates a sophisticated wearable system for monitoring and optimizing the training of university-level track and field athletes. We present a custom-designed system architecture integrating high-frequency Inertial Measurement Units (IMUs) and electrocardiography (ECG) sensors, synchronized via a proprietary protocol. The software system employs edge computing for low-latency biomechanical feature extraction using an Extended Kalman Filter and a gradient-boosting regression model on the cloud backend for fatigue prediction. A 12-week randomized controlled trial was conducted with 50 athletes. Results demonstrated that the experimental group achieved statistically significant improvements in running economy (a 6.5% reduction in VO₂ at 16 km/h), a 10.3% enhancement in a novel Movement Quality Index, and a 45% reduction in time-loss injuries. The study concludes that a deeply integrated, multi-modal wearable system, underpinned by robust data processing pipelines and machine learning, can profoundly enhance training precision, performance outcomes, and athlete health.

Recent growth in green construction projects in Saudi Arabia has increased the use of advanced construction systems, necessitating more proactive and intelligent approaches to safety management. This study examines the role of artificial intelligence (AI) in improving hazard mitigation and worker safety in green construction projects. Five AI-enabled components were assessed: hazard detection, risk prediction, wearable smart devices, AI-based training systems, and AI-supported compliance tools. A quantitative descriptive–analytical approach was adopted using a structured questionnaire distributed to 350 employees working on green construction sites. Simple random sampling was applied, and reliability and validity were confirmed through expert review and Cronbach’s alpha testing. Data were analysed using SPSS and AMOS to examine relationships within the proposed model. The findings indicate that all AI components significantly enhance safety performance. AI-based training systems showed the strongest influence (R2 = 0.509), followed by wearable devices (R2 = 0.334), hazard detection (R2 = 0.216), risk prediction (R2 = 0.124), and compliance tools (R2 = 0.060). The results confirm the robustness of the model and demonstrate that AI integration supports predictive safety management, improved compliance, and reduced incidents, aligning with Saudi Arabia’s Vision 2030 sustainability objectives.

Wearable thermal management devices are gaining increasing attention for healthcare, rehabilitation, and personal comfort applications. Here, we present a textile-based wearable electrothermal pad that integrates a woven heater with a yarn temperature sensor on a flexible, breathable fabric platform. The heater element was fabricated using cotton yarn coated with silver nanowires (AgNW) and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) doped with ethylene glycol (EG-PEDOT:PSS), enabling uniform heat distribution, washability, and low-voltage operation. The AgNW/EG-PEDOT:PSS yarn heater rapidly reached 45 °C within 60 s at an applied voltage of 1.5 V and demonstrated good thermal stability and repeatability over 2,000 stretching-releasing cycles. The temperature sensor, based on polystyrene-coated PEDOT:PSS (PS/PEDOT:PSS) yarn, exhibited thermistor behavior with resistance decreasing as the temperature increases. It showed a negative temperature coefficient of resistance of 0.25%°C-1 and a response time of less than 130 s. The yarn temperature sensor also maintained resistance stability under bending and stretching-releasing conditions. To demonstrate a wearable electrotherapy platform, the PS/PEDOT:PSS yarn temperature sensor was integrated with a woven AgNW/EG-PEDOT:PSS heater to enable closed-loop feedback for thermal regulation. Programmable, on-demand thermotherapy and real-time thermal management could be wirelessly controlled via a smartphone. This custom-designed electrothermal pad offers a promising solution for wearable thermotherapy and smart clothing applications.

Background: As China enters the digital era and actively promotes an active aging strategy, smart wearable devices have become increasingly prevalent among older adults; however, their impact on health inequality remains unclear. This study investigates the association between smart wearable devices and health, as well as health inequality, among Chinese older adults, and further examines the mediating roles of joy of living and social participation. Methods: Data were derived from two waves (2018 and 2020) of the China Longitudinal Aging Social Survey (CLASS), with a final sample of 7098 adults aged 60 and above. A two-way fixed-effects model, propensity score matching-difference-in-differences (PSM-DID) approach, and mediation analysis were employed. Results: Smart wearable devices were significantly positively associated with both health and health inequality among older adults in China. Mediation analysis revealed that joy of living and social participation played an intermediary role. Conclusions: This study provides preliminary evidence that smart wearable devices are associated with health and health inequality among Chinese older adults. Policy efforts should focus on developing more user-friendly devices, promoting digital literacy among older adults, and supporting disadvantaged groups. Furthermore, the mediating effects suggest that fostering joy of living and encouraging active social participation may serve as effective pathways to improve health.

A theoretical study was conducted on a graphene ribbon suspended mass micro-electromechanical systems (MEMS) optical force (acceleration) sensor based on a Fabry-Perot (FP) microcavity. The study found that when the suspended mass is subjected to an external force or acceleration, its position shifts, altering the cavity length of the FP microcavity and thereby enabling optical sensing. The sensor can operate in both spectral and intensity modes. Benefiting from the ultrathin and highly stretchable properties of graphene, the sensor exhibits high sensitivity and a wide detection range. In spectral mode, the maximum sensitivity to force reaches -6nm/pN, and the sensitivity to acceleration can reach -1.4nm/g. In intensity mode, the force sensitivity can be as high as 0.74pN-1. The designed sensor is ultra-compact, suitable for on-chip integration, and can be fabricated using large-scale manufacturing techniques. It holds significant potential for applications in wearable electronics, smart devices, robotics, and other fields.

ABSTRACT Electronic waste has emerged as a major environmental challenge, driven by the massive consumption and a limited lifetime of modern electronic devices, stimulating the development of sustainable electronics. Here, an all‐biomaterial gelatin‐choline‐citric acid ([Ch][CA]) ionogel is developed as an active binder to realize self‐sintered, healable, and recyclable printed giant magnetoresistance (GMR) sensors using [Co/Cu] 50 microflakes as functional fillers. By tuning the choline‐to‐citric‐acid ratio, excess protons are released during drying, etching oxide passivation layers on the flakes and driving in situ self‐sintering. The printed sensors possess magnetoresistance ratio of 7.1% at 300 mT and maintain stable performance under bending down to a 0.4 mm radius. The reversible sol‐gel transition of the gelatin‐[Ch][CA] binder matrix imparts healing capability upon mild heating and full recyclability via dissolution and magnetic recovery of fillers. The ionogel provides excellent mechanical flexibility enabling integration in smart wearables. This demonstration of self‐sintering, healable, and recyclable printed GMR sensors based entirely on biomaterial‐based binders offers a sustainable route toward next‐generation flexible magnetoelectronics.

Artificial Intelligence (AI) is transforming modern healthcare by enabling intelligent data analysis, predictive diagnostics, and personalized treatment solutions. In parallel, traditional wellness systems such as the chakra energy framework emphasize the balance between physical, mental, and spiritual health. This study proposes a novel holistic healthcare model that integrates AI-based health monitoring with chakra energy analysis within a temple-based wellness environment. The research takes inspiration from the spiritual ecosystem surrounding the Tirumala Venkateswara Temple located in Tirupati, which attracts millions of pilgrims seeking spiritual and psychological wellbeing. The proposed system utilizes wearable sensors and smart health devices to collect physiological parameters such as heart rate, stress levels, blood pressure, and sleep patterns. These health indicators are processed using Artificial Intelligence and Machine Learning algorithms to identify patterns related to emotional and physical imbalances associated with the seven chakras. The temple environment, including meditation, chanting, and spiritual rituals, acts as a natural wellness space that may influence stress reduction and emotional balance.