Machine Learning-Assisted Gesture Sensor Made with Graphene/Carbon Nanotubes for Sign Language Recognition.

Journal: ACS applied materials & interfaces
PMID: 39297553

Abstract

Gesture sensors are essential to collect human movements for human-computer interfaces, but their application is normally hampered by the difficulties in achieving high sensitivity and an ultrawide response range simultaneously. In this article, inspired by the spider silk structure in nature, a novel gesture sensor with a core-shell structure is proposed. The sensor offers a high gauge factor of up to 340 and a wide response range of 60%. Moreover, the sensor combining with a deep learning technique creates a system for precise gesture recognition. The system demonstrated an impressive 99% accuracy in single gesture recognition tests. Meanwhile, by using the sliding window technology and large language model, a high performance of 97% accuracy is achieved in continuous sentence recognition. In summary, the proposed high-performance sensor significantly improves the sensitivity and response range of the gesture recognition sensor. Meanwhile, the neural network technology is combined to further improve the way of daily communication by sign language users.

Authors

  • Hao-Yuan Shen
    School of Integrated Circuits and Electronics, MIIT Key Laboratory for Low-Dimensional Quantum Structure and Devices, Beijing Institute of Technology, Beijing 100081, China.
  • Yu-Tao Li
    Institute of Microelectronics, Tsinghua University, Beijing, China.
  • Hang Liu
    Interventional Department, Changhai Hospital, Second Military Medical University, Shanghai 200433, China.
  • Jie Lin
    Department of Reproductive Medicine, Zigong Hospital of Women and Children Health Care, Zigong, China.
  • Lu-Yu Zhao
    School of Integrated Circuits and Electronics, MIIT Key Laboratory for Low-Dimensional Quantum Structure and Devices, Beijing Institute of Technology, Beijing 100081, China.
  • Guo-Peng Li
    College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
  • Yi-Wen Wu
    Department of Ultrasound, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Tian-Ling Ren
    Institute of Microelectronics and Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China.
  • Yeliang Wang
    School of Integrated Circuits and Electronics, MIIT Key Laboratory for Low-Dimensional Quantum Structure and Devices, Beijing Institute of Technology, Beijing 100081, China.

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