Muscle fatigue and injury are the core issues that restrict the improvement of athletes’ competitive performance and the maintenance of sports health for the general population. The traditional muscle health management paradigm is limited by lagging assessment and single indicators, failing to meet the demands of precise training and individualized rehabilitation. With the advances in wearable sensing technology, muscle health management has the potential to transform from the empirical modality to a new data-driven paradigm. However, existing publications either focus on materials innovation and structure design in sensor development, or solely highlight the overlapping physiological mechanisms inducing muscle fatigue and injury. Thus, a comprehensive review presenting insights on the physiological relevance between biological signals fluctuations and muscle health status, the detection mechanisms and functional layouts of wearable sensors to capture these signals, as well as their real-world applications in competitive sports and public fitness is timely needed. Herein, this article systematically reviews the physiological mechanisms of muscle fatigue, injury and repair, with a focus on elaborating the characteristic change patterns of related bioelectrical, biochemical and biomechanical markers in the process. Sensing mechanisms and working layouts of wearable technology are comprehensively summarized. Importantly, corresponding applications in real-world settings associated with improving professional athletic performance and public fitness are proposed, including load monitoring, fatigue evaluation, personalized nutrition management, as well as artificial intelligence (AI)-enabled multimodal fusion. Based on this, future perspectives are envisioned to better aid sports activities and engineer the development of sports science and sports medicine.
Integrated multimodal biosensing platforms are transforming the landscape of bioanalytical technologies by enabling real-time, high-resolution, and multifunctional detection of physiological and environmental biomarkers. This review summarizes the evolution from traditional single-modal biosensors to advanced multimodal systems that unify diverse sensing modalities with computation and storage functionalities. The introduction on the transduction mechanisms of different biosensors and the representative biomarkers was first provided, highlighting the advantages of multimodal sensing with enhanced sensitivity, specificity, and robustness. The advances in fabrication techniques were then discussed, with particular emphasis on printable strategies that facilitate heterogeneous material integration and micro/nanoscale patterning. Moreover, artificial intelligence-driven data processing for on-device decision-making was discussed. Representative applications were then presented in healthcare monitoring, environment detection, and food safety tracking. Finally, current challenges related to material compatibility, data heterogeneity, device scalability, and regulatory barriers were proposed with possible strategies toward fully autonomous and intelligent biosensing systems.
Zero-power optoelectronic synapses, defined as optoelectronic synaptic devices operating without external electrical bias, are emerging as core components for energy-efficient intelligent wearable neuromorphic platforms. Wearable neuromorphic systems require continuous, autonomous operation under strict constraints on power consumption, mechanical compliance, and thermal safety, making conventional electrically biased synaptic devices impractical for long-term body-interfaced use. By harvesting light to drive synaptic modulation without external bias, these devices integrate sensing, learning, memory, and processing within a single self-sustained element. This light-driven operation is therefore particularly well suited for wearable platforms, where energy availability is limited and frequent recharging or battery replacement is undesirable. This review summarizes recent progress in zero-power optoelectronic synapses based on three representative mechanisms: Schottky junctions, heterojunctions, and photothermoelectric effect. Despite notable progress, several fundamental challenges continue to limit practical deployment. These include limited light utilization, insufficient bidirectional weight modulation, instability and variability, mechanical incompatibility, and lack of system-level integration, which remain major hurdles. These limitations hinder the reliable operation, scalability, and long-term applicability of zero-power optoelectronic synapses in realistic wearable neuromorphic platforms. Finally, this review proposes technological strategies for addressing these challenges. We further outline how these advances could enable practical, scalable, and mechanically compliant synaptic platforms for future energy-autonomous, body-interfaced neuromorphic systems capable of continuous perception and intelligent processing.
Fiber memristors represent a transformative platform for next-generation wearable electronics, enabling the seamless integration of non-volatile memory and neuromorphic computing directly onto or within textile fibers. This intrinsic functionalization at the fiber level effectively overcomes the “sense-transmit-process” separation inherent in conventional wearable systems, paving the way for truly intelligent, energy-efficient, and autonomous textiles. This review provides a comprehensive overview of the development and state-of-the-art research in this emerging field. We first elucidate the fundamental device architectures and underlying resistive-switching mechanisms. Subsequently, we systematically summarize the material systems and advanced fabrication strategies employed to construct robust and weavable memristive fibers, followed by a critical analysis of their electrical, mechanical, and functional performance metrics. A dedicated section highlights the cutting-edge applications of fiber memristors, particularly in integrated sensing-memory-computing systems, neuromorphic signal processing, and adaptive human-machine interfaces. Key challenges are thoroughly discussed, along with promising future research directions. By offering a holistic perspective spanning materials, devices, and integrated systems, this review aims to provide comprehensive theoretical insights and technical guidance for the development of next-generation intelligent textiles, thereby accelerating the deep fusion of electronic functionality and textile substrates.
Prolonged operation of mechanical components under high-temperature environment severely affects their performance and service life. It is urgently needed a high-temperature resistance sensor to monitor local temperature changes in the component to enhance the safety. However, conventional sensors are difficult to simultaneously achieve excellent sensing performance and conformability at high-temperature environments for curved surfaces. Herein, a polyacrylonitrile (PAN)/zirconium acetate [Zr(OAc)4]-based flexible and high-temperature-resistant piezo/pyroelectric bifunctional sensors are fabricated via heat treatment and typical package method. Incorporation of Zr(OAc)4 reduced the cyclization temperature of PAN molecular chains and effectively enhanced both the mechanical properties and electrical output of the composite membranes sensor. Compared to pure PAN, the PAN/Zr(OAc)4 composite nanofiber sensor (heat-treated at 450 °C) exhibits higher voltage output (~12.9 V) and piezoelectric sensitivity (~1.67 V/N) at room temperature. In addition, the sensor exhibits excellent pyroelectric output performance across a wide temperature gradient range, enabling accurate detection of engine temperature fluctuations. Remarkably, the sensor maintain stable piezoelectric and pyroelectric outputs after 5,000 press-release cycles at 400 °C, highlighting its robustness under extreme conditions. These results demonstrate that PAN/Zr(OAc)4 sensor provides reliable, flexible, and multifunctional solutions for real-time temperature monitoring in automobile engines, offering substantial benefits for extending engine lifespan and ensuring driving safety.
The rapid evolution of smart textiles has created a pressing demand for soft conductive fibers that simultaneously possess outstanding mechanical flexibility and high electrical conductivity. Emerging two-dimensional materials, particularly graphene and transition metal carbides/nitrides (MXenes), serve as ideal building blocks for constructing such high-performance soft conductive fibers. This review systematically summarizes recent advances in soft conductive fibers based on graphene and MXene nanosheets, with a primary focus on their integration into smart textiles. This review focus on the mainstream fabrication techniques including wet spinning, surface coating, and electrospinning which translate the intrinsic microscopic properties of graphene and MXene nanosheets into practical macroscopic fibrous assemblies. These soft conductive fibers can be effectively woven into smart textiles for a variety of wearable applications, such as electromagnetic shielding, flexible sensing, personal healthcare, thermal management and energy harvesting/storage. Furthermore, the review also discusses graphene/MXene composite and hybrid fibers, highlighting their fabrication strategies, synergistic reinforcement mechanisms, and enhanced performance benefits. Finally, we present a critical perspective on the opportunities and challenges facing graphene and MXene fibers in the pursuit of practical, large-scale wearable applications. Owing to their unique combination of properties, graphene and MXene fibers establish a robust platform for advanced wearable electronics and pave the way for next-generation smart textiles.
Flexible tactile sensors capable of resolving complex mechanical stimuli are essential for advanced electronic skins. However, simultaneous perception of force magnitude, direction, and dynamic loading remains challenging without complex circuitry. Here, we report a kirigami-enabled, skin-inspired flexible sensor that achieves spatially distributed receptor-like responses through a three-dimensional (3D) laser-induced graphene (LIG) network. By transferring LIG from a kirigami-engineered polyimide substrate, we transform a planar conductive layer into a 3D architecture with height-dependent electrical characteristics. This structural differentiation enables spatially-encoded electromechanical transduction, where heterogeneous sensitivities across the 3D-LIG network translate simple stimuli into high-dimensional signal features. Consequently, the architecture inherently decouples force amplitude from dynamic loading through its non-linear deformation profiles. By further leveraging kirigami-induced anisotropy, the sensor achieves simultaneous resolution of multidirectional force vectors without requiring complex peripheral circuitry. The sensor demonstrates a linear pressure range of 0-35 kPa and high durability over 10,000 cycles. Leveraging this “structural coding” paradigm, the device enables high-accuracy recognition of surface roughness (95.34%) and gait patterns (91.77%) via machine learning. This work offers a robust strategy for biomimetic tactile sensing by integrating 3D structural engineering with intrinsic multidimensional signal decoupling. This design provides a promising foundation for intelligent prosthetics and human machine interfaces, achieving force vector resolution within a single structure.
Aging populations face growing multimorbidity, while episodic clinical assessments fail to capture gradual physiological changes unfolding during daily life. Although wearable technologies enable continuous monitoring, single-modality systems provide incomplete and context-limited insight. This Perspective focuses on hybrid wearable sensors that integrate physical and chemical sensing for geriatric healthcare. Hybrid wearable sensing provides a pathway toward continuous, predictive, and personalized geriatric health management. By monitoring continuously multiple health parameters, such multimodal systems have distinct advantages for real-time monitoring, including early risk detection and more personalized health assessment through the integration of complementary physical and biochemical signals. We discuss recent advances in wearable physical sensors, alongside with emerging wearable chemical sensors, then argue that chem-phys hybrid integration enables more interpretable and clinically actionable assessment of aging trajectories than single-modality wearable systems. Finally, we discuss translational requirements and future prospects, including robust real-world operation, AI-driven inference, and integration with telemedicine and home-based care.