Material-Structure Codesign in Triboelectric Sensors: A Body-Region-Specific Roadmap for Human Motion Monitoring and
Yuchen Sun1, Xiaowei Wang2, Xia Cao3
1School of Mathematics and Physics, Centre Green Innovation, University of Science and Technology, Beijing 100083, China.
ACS Sensors
|December 24, 2025
Summary
Self-powered triboelectric sensors (TESs) offer advanced human motion recognition for healthcare and HMI. Material and structural innovations are key to overcoming challenges and enabling precise biomechanical feedback.
Area of Science:
- Materials Science
- Biomedical Engineering
- Wearable Technology
Background:
- Human motion recognition is vital for clinical rehabilitation, human-machine interaction (HMI), and sports science.
- Self-powered triboelectric sensors (TESs) show potential for precision medicine, sign language translation, and robotics.
- Existing TESs face challenges in signal stability, motion decoupling, and long-term durability.
Purpose of the Study:
- To systematically explore challenges in TESs for human motion recognition.
- To highlight the role of material design and structural innovation in enhancing TES performance.
- To provide a technical roadmap for self-powered sensing systems in smart healthcare and immersive interactions.
Main Methods:
- Analysis of triboelectric sensing mechanisms.
- Comparison of traditional polymers with novel high-performance materials.
- Exploration of structural innovations like biomimetic, multimodal, and textile integration.
- Systematic analysis of motion recognition for different body parts (limbs, trunk, head/neck).
Main Results:
- Novel materials and structural designs can overcome limitations in TES performance (dielectric properties, mechanical strength, stability).
- Innovations enhance TES sensitivity, comfort, and suitability for large-area deployment.
- Progress in TES application for recognizing diverse human motions across various scenarios was summarized.
Conclusions:
- Material design and structural innovation are critical for advancing TES performance.
- Integration of AI can enable real-time, precise biomechanical feedback and disease diagnosis.
- Future developments aim to promote TES implementation in smart healthcare and immersive interaction applications.
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