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Multi-receptor skin with highly sensitive tele-perception somatosensory.
Du, Yan; Shen, Penghui; Liu, Houfang; Zhang, Yuyang; Jia, Luyao; Pu, Xiong; Yang, Feiyao; Ren, Tianling; Chu, Daping; Wang, Zhonglin; Wei, Di.
Affiliation
  • Du Y; Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China.
  • Shen P; School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
  • Liu H; School of Integrated Circuits and Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 10084, China.
  • Zhang Y; School of Integrated Circuits and Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 10084, China.
  • Jia L; The University of Manchester, Manchester M13 9PL, UK.
  • Pu X; Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China.
  • Yang F; School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
  • Ren T; Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China.
  • Chu D; Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China.
  • Wang Z; School of Integrated Circuits and Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 10084, China.
  • Wei D; Centre for Photonic Devices and Sensors, University of Cambridge, 9 JJ Thomson Avenue, Cambridge CB3 0FA, UK.
Sci Adv ; 10(37): eadp8681, 2024 Sep 13.
Article in En | MEDLINE | ID: mdl-39259789
ABSTRACT
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 ΔV/Δd 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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Neural Networks, Computer Limits: Humans Language: En Journal: Sci Adv Year: 2024 Document type: Article Affiliation country: China Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Neural Networks, Computer Limits: Humans Language: En Journal: Sci Adv Year: 2024 Document type: Article Affiliation country: China Country of publication: United States