Textile emitter for AI-enhanced human–machine interaction
Year: 2026
Type: article
Abstract: Wearable textile interfaces hold significant promise for advanced human-machine interaction, yet realizing wireless, battery-free on-body signal generation without embedded circuitry in textiles remains a formidable challenge. Here, we present a textile emitter that enables precise interaction by directly transducing user-induced mechanical deformation into time-coded infrared (IR) thermal signals. Constructed from strain-responsive IR-emitting fibers, the textile emitter simultaneously functions as a programmable signal generator without embedded electronic circuits or power sources within the textile. Seamlessly integrated into everyday garments, it ensures stable performance while maintaining comfort and wearability. Inspired by Morse code, user motions are encoded as temporally structured IR signals and wirelessly decoded by machine learning algorithms, achieving up to 99.6% recognition accuracy across diverse interaction commands. This AI-enhanced textile emitter establishes a scalable and robust foundation for future wearable systems in information communication, robotic control, and digital interaction.
Source: National Science Review
DOI: 10.1093/nsr/nwag231
Institutions: Shanghai Jiao Tong University, State Key Laboratory of Metal Matrix Composites, National University of Singapore, Tsinghua University, Harbin Institute of Technology, State Key Laboratory of Robotics and Systems, Fudan University, State Key Laboratory of Surface Physics, Xiamen University
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,000
Awards: G1181227161, G7985551514
OpenAlex ID: W7154695597