A single-fibre computer enables textile networks and distributed inference

Nikhil Gupta, Henry Cheung, Syamantak Payra, Gabriel Loke, Jenny Li, Yongyi Zhao, Latika Balachander, Ella Son, Vivian Li, Samuel Kravitz, Sehar Lohawala, John Joannopoulos and Yoel Fink ()
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Nikhil Gupta: Massachusetts Institute of Technology
Henry Cheung: Massachusetts Institute of Technology
Syamantak Payra: Massachusetts Institute of Technology
Gabriel Loke: Massachusetts Institute of Technology
Jenny Li: Massachusetts Institute of Technology
Yongyi Zhao: Massachusetts Institute of Technology
Latika Balachander: Rhode Island School of Design
Ella Son: Rhode Island School of Design
Vivian Li: Brown University
Samuel Kravitz: Massachusetts Institute of Technology
Sehar Lohawala: Massachusetts Institute of Technology
John Joannopoulos: Massachusetts Institute of Technology
Yoel Fink: Massachusetts Institute of Technology

Nature, 2025, vol. 639, issue 8053, 79-86

Abstract: Abstract Despite advancements in wearable technologies1,2, barriers remain in achieving distributed computation located persistently on the human body. Here a textile fibre computer that monolithically combines analogue sensing, digital memory, processing and communication in a mass of less than 5 g is presented. Enabled by a foldable interposer, the two-dimensional pad architectures of microdevices were mapped to three-dimensional cylindrical layouts conforming to fibre geometry. Through connection with helical copper microwires, eight microdevices were thermally drawn into a machine-washable elastic fibre capable of more than 60% stretch. This programmable fibre, which incorporates a 32-bit floating-point microcontroller, independently performs edge computing tasks even when braided, woven, knitted or seam-sewn into garments. The universality of the assembly process allows for the integration of additional functions with simple modifications, including a rechargeable fibre power source that operates the computer for nearly 6 h. Finally, we surmount the perennial limitation of rigid interconnects by implementing two wireless communication schemes involving woven optical links and seam-inserted radio-frequency communications. To demonstrate its utility, we show that garments equipped with four fibre computers, one per limb, operating individually trained neural networks achieve, on average, 67% accuracy in classifying physical activity. However, when networked, inference accuracy increases to 95% using simple weighted voting.

Date: 2025
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DOI: 10.1038/s41586-024-08568-6

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