ReviewScience advances2025
Machine learning-assisted triboelectric nanogenerator technology for intelligent sports.
Review in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Harnessing in-situ tribo-plasma emission for energy harvesting.Science advances · 2026Article
- Triboelectric Nanogenerators for Vehicle Energy Harvesting and Intelligent Sensing.Micromachines · 2026Review
- InSe-Enabled Wearable Triboelectric Nanogenerator for Biomechanical Energy Harvesting and Motion Sensing.ChemistryOpen · 2026Article
- Scalable Production of Motion-Enabled Self-Charging Power Textiles with Highly Durable Zinc-Ion Fiber Batteries.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Triboelectric Wearable Sensors for Human-Centric Smart Electronics: From Self-Powered Sensing to Artificial Intelligence-Assisted Human-Machine Interface Systems.Nano-micro letters · 2026Review
- Soft Ionic and Electronic Triboelectric Nanogenerators: Toward Attachable and Implantable Biomedical Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Ionic Hydrogel Triboelectric Sensor for Real-Time Swimming Kinematics Monitoring and Stroke Recognition.ChemistryOpen · 2026Article
- A time-stamping tactile sensor enabled by pseudoconductive interface design at dielectric heterojunctions.Science advances · 2026Article
- Sustainable Materials Design With Multi-Modal Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- An Electromechanical Power Law in Sustainable Thermally Drawn Triboelectric Nanocomposite Fibers for Sensing in Continuum Robots.ACS applied materials & interfaces · 2026Article
- Emergency Clinical Decision for Sports Injury Management: A Wearable Sensor-Driven Framework from Training to Rehabilitation.Biosensors · 2026Review
- Fine reconstruction of badminton swing dynamic trajectory assisted by event camera.Scientific reports · 2026Article
- SnSe/PVDF-HFP Composite Fibrous Membrane with Excellent Piezoelectric and Photocatalytic Dual Properties.ACS omega · 2026Article
- Recent Advances and Challenges of Textile-Based Triboelectric Nanogenerators for Smart Healthcare and Sports Applications.Nanomaterials (Basel, Switzerland) · 2026Review
- Human bioimpedance-based state detection technologies for sports health monitoring: a review.Frontiers in physiology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.