In the new era of internet of things, big data collection and analysis based on widely distributed intelligent sensing technology is particularly important. Here, we report a flexible and durable wood-based triboelectric nanogenerator for self-powered sensing in athletic big data analytics. Based on a simple and effective strategy, natural wood can be converted into a high-performance triboelectric material with excellent mechanical properties, such as 7.5-fold enhancement in strength, superior flexibility, wear resistance and processability. The electrical output performance is also enhanced by more than 70% compared with natural wood. A self-powered falling point distribution statistical system and an edge ball judgement system are further developed to provide training guidance and real-time competition assistance for both athletes and referees. This work can not only expand the application area of the self-powered system to smart sport monitoring and assisting, but also promote the development of big data analytics in intelligent sports industry.

Combining traditional textiles with triboelectric nanogenerators (TENGs) gives birth to self-powered electronic textiles (e-textiles). However, there are two bottlenecks in their widespread application, low power output and poor sensing capability. Herein, by means of the three-dimensional five-directional braided (3DB) structure, a TENG-based e-textile with the features of high flexibility, shape adaptability, structural integrity, cyclic washability, and superior mechanical stability, is designed for power and sensing. Due to the spatial frame-column structure formed between the outer braided yarn and inner axial yarn, the 3DB-TENG is also endowed with high compression resilience, enhanced power output, improved pressure sensitivity, and vibrational energy harvesting ability, which can power miniature wearable electronics and respond to tiny weight variations. Furthermore, an intelligent shoe and an identity recognition carpet are demonstrated to verify its performance. This study hopes to provide a new design concept for high-performance textile-based TENGs and expand their application scope in human-machine interfacing.

The rapid development of internet of things, big data, and artificial intelligence is propelling sports science into a data-driven era, demanding real-time, multidimensional athletic performance monitoring. Triboelectric nanogenerators (TENGs) have demonstrated exceptional potential in intelligent sports. However, the complexity and volume of TENG-generated data pose challenges for manual analysis. Machine learning (ML), with strengths in pattern recognition and adaptive processing, provides a powerful solution to enhance TENG-based sensing signal interpretation. This review systematically explores the integration of ML and TENG technology for intelligent sports. First, the fundamental theory and basic knowledge of TENGs are introduced, highlighting their versatility in sports sensing systems. Subsequently, a comprehensive overview of ML models for TENG signal analysis is discussed. Recent advancements of ML-assisted TENG-based intelligent sports applications, including sports training evaluation, sports health monitoring, and virtual/augmented reality sports, are then highlighted. Last, current challenges and future prospects of TENG-based intelligent sports systems are discussed.

The growing demand for fast, reliable, and accessible information in the vastly connected world makes disposable sensors increasingly important. However, reducing their costs, environmental impact, and usability remains challenging. Here, we report a low-cost, biodegradable, and recyclable all-lignocellulosic triboelectric nanogenerator (AL-TENG) for self-powered disposable medical monitoring. Based on a facile in situ lignin regeneration & chemical crosslinking modification strategy, a high-performance lignocellulosic bioplastic is synthesized from resource-abundant and renewable biomass for fabricating the AL-TENG. The whole device has a low environmental impact as it can be easily recycled and biodegraded at its end-of-life. Furthermore, a self-powered smart ward system and a self-powered contactless medical monitoring system are developed to improve the convenience for patients and reduce the risk of mutual infection. This work can expand the application of self-powered systems to disposable medical sensing, which may greatly promote the development of intelligent wards and disposable electronics.