Behavioral biometric optical tactile sensor for instantaneous decoupling of dynamic touch signals in real time

Changil Son, Jinyoung Kim, Dongwon Kang, Seojoung Park, Chaeyeong Ryu, Dahye Baek, Geonyoung Jeong, Sanggyun Jeong, Seonghyeon Ahn, Chanoong Lim, Yundon Jeong, Jeongin Eom, Jung-Hoon Park, Dong Woog Lee, Donghyuk Kim (), Jungwook Kim (), Hyunhyub Ko () and Jiseok Lee ()
Additional contact information
Changil Son: Ulsan National Institute of Science and Technology (UNIST)
Jinyoung Kim: Ulsan National Institute of Science and Technology (UNIST)
Dongwon Kang: Sogang University
Seojoung Park: Ulsan National Institute of Science and Technology (UNIST)
Chaeyeong Ryu: Ulsan National Institute of Science and Technology (UNIST)
Dahye Baek: Ulsan National Institute of Science and Technology (UNIST)
Geonyoung Jeong: Ulsan National Institute of Science and Technology (UNIST)
Sanggyun Jeong: Ulsan National Institute of Science and Technology (UNIST)
Seonghyeon Ahn: Ulsan National Institute of Science and Technology (UNIST)
Chanoong Lim: Ulsan National Institute of Science and Technology (UNIST)
Yundon Jeong: Ulsan National Institute of Science and Technology (UNIST)
Jeongin Eom: Sogang University
Jung-Hoon Park: Ulsan National Institute of Science and Technology (UNIST)
Dong Woog Lee: Ulsan National Institute of Science and Technology (UNIST)
Donghyuk Kim: Ulsan National Institute of Science and Technology (UNIST)
Jungwook Kim: Gwanak-gu
Hyunhyub Ko: Ulsan National Institute of Science and Technology (UNIST)
Jiseok Lee: Ulsan National Institute of Science and Technology (UNIST)

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Decoupling dynamic touch signals in the optical tactile sensors is highly desired for behavioral tactile applications yet challenging because typical optical sensors mostly measure only static normal force and use imprecise multi-image averaging for dynamic force sensing. Here, we report a highly sensitive upconversion nanocrystals-based behavioral biometric optical tactile sensor that instantaneously and quantitatively decomposes dynamic touch signals into individual components of vertical normal and lateral shear force from a single image in real-time. By mimicking the sensory architecture of human skin, the unique luminescence signal obtained is axisymmetric for static normal forces and non-axisymmetric for dynamic shear forces. Our sensor demonstrates high spatio-temporal screening of small objects and recognizes fingerprints for authentication with high spatial-temporal resolution. Using a dynamic force discrimination machine learning framework, we realized a Braille-to-Speech translation system and a next-generation dynamic biometric recognition system for handwriting.

Date: 2024
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DOI: 10.1038/s41467-024-52331-4

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