School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan Metropolitan City, 44919, Republic of Korea.
Department of Chemical and Biomolecular Engineering, Sogang University, Seoul, 04107, Republic of Korea.
Nat Commun. 2024 Sep 12;15(1):8003. doi: 10.1038/s41467-024-52331-4.
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.
对于行为触觉应用来说，期望光学触觉传感器能够解耦动态触觉信号，但这极具挑战性，因为典型的光学传感器通常仅测量静态正压力，并且使用不精确的多图像平均法进行动态力感测。在此，我们报告了一种基于上转换纳米晶体的高灵敏度行为生物计量光学触觉传感器，它可以实时从单个图像中即时、定量地将动态触觉信号分解为垂直正压力和横向剪切力的单个分量。通过模仿人类皮肤的感觉结构，对于静态正压力，获得的独特发光信号是轴对称的，而对于动态剪切力，其是非轴对称的。我们的传感器具有高时空分辨率的小型物体筛选能力，并可实现高时空分辨率的指纹认证。利用动态力判别机器学习框架，我们实现了盲文到语音的翻译系统和下一代用于手写的动态生物识别系统。
