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Textile emitter for AI-enhanced human-machine interaction
Ya Sun1,2, Xuanyu Cui1,2, Gaoyang Kong1,2
1State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
National Science Review
|June 29, 2026
Summary
This study introduces a novel textile emitter that converts mechanical motion into wireless infrared thermal signals for human-machine interaction. This battery-free wearable technology enables precise control without embedded electronics.
Area of Science:
- Materials Science
- Human-Computer Interaction
- Wearable Technology
Background:
- Wearable textile interfaces offer advanced human-machine interaction potential.
- Wireless, battery-free on-body signal generation in textiles without embedded circuitry is a significant challenge.
Purpose of the Study:
- To develop a textile emitter for precise human-machine interaction.
- To enable wireless, battery-free signal generation using mechanical deformation.
Main Methods:
- Fabrication of a textile emitter using strain-responsive IR-emitting fibers.
- Transduction of user-induced mechanical deformation into time-coded infrared (IR) thermal signals.
- Wireless decoding of encoded IR signals using machine learning algorithms.
Main Results:
- The textile emitter functions as a programmable signal generator without embedded electronics or power sources.
- Seamless integration into garments ensures comfort and stable performance.
- Machine learning achieved up to 99.6% recognition accuracy for diverse interaction commands.
Conclusions:
- The AI-enhanced textile emitter provides a scalable and robust foundation for future wearable systems.
- This technology facilitates advanced applications in information communication, robotic control, and digital interaction.
- It overcomes the challenge of wireless, battery-free signal generation in smart textiles.
