Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China.
School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
Sci Adv. 2024 Sep 13;10(37):eadp8681. doi: 10.1126/sciadv.adp8681. Epub 2024 Sep 11.
The limitations and complexity of traditional noncontact sensors in terms of sensitivity and threshold settings pose great challenges to extend the traditional five human senses. Here, we propose tele-perception to enhance human perception and cognition beyond these conventional noncontact sensors. Our bionic multi-receptor skin employs structured doping of inorganic nanoparticles to enhance the local electric field, coupled with advanced deep learning algorithms, achieving a Δ/Δ sensitivity of 14.2, surpassing benchmarks. This enables precise remote control of surveillance systems and robotic manipulators. Our long short-term memory-based adaptive pulse identification achieves 99.56% accuracy in material identification with accelerated processing speeds. In addition, we demonstrate the feasibility of using a two-dimensional (2D) sensor matrix to integrate real object scan data into a convolutional neural network to accurately discriminate the shape and material of 3D objects. This promises transformative advances in human-computer interaction and neuromorphic computing.
传统非接触传感器在灵敏度和阈值设置方面的局限性和复杂性对扩展传统的人类五种感官构成了巨大挑战。在这里，我们提出远程感知，以超越这些传统的非接触传感器来增强人类的感知和认知。我们的仿生多受体皮肤采用无机纳米粒子的结构化掺杂来增强局部电场，结合先进的深度学习算法，实现了 14.2 的 Δ/Δ 灵敏度，超过了基准。这使得对监控系统和机器人操纵器的精确远程控制成为可能。我们基于长短期记忆的自适应脉冲识别实现了材料识别的 99.56%的准确率，同时加快了处理速度。此外，我们还展示了使用二维（2D）传感器矩阵将真实物体扫描数据集成到卷积神经网络中以准确区分 3D 物体形状和材料的可行性。这有望在人机交互和神经形态计算方面取得变革性进展。
