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

Decoupling dynamic touch signals in optical tactile sensors is challenging because typical systems primarily capture static normal forces and rely on multi-image averaging for dynamic sensing. The authors report a highly sensitive upconversion nanocrystal (UCN)-based optical tactile sensor that instantaneously and quantitatively decomposes dynamic touch signals into vertical normal and lateral shear components from a single image in real time. Mimicking human skin’s sensory architecture, the device yields axisymmetric luminescence for static normal forces and non-axisymmetric profiles for dynamic shear forces. The sensor enables high spatiotemporal screening of small objects and high-resolution fingerprint recognition. Using a machine learning framework for dynamic force discrimination, the system demonstrates Braille-to-speech translation and dynamic biometric recognition of handwriting.

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, Jiseok Lee