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Tytuł:
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Self-Powered Tactile Sensor for Gesture Recognition Using Deep Learning Algorithms.
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Autorzy:
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Yang J; School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Liu S; School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Meng Y; School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Xu W; School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Liu S; School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Jia L; School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Chen G; School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Qin Y; State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China.
Han M; Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing 100871, China.
Li X; School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
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Źródło:
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ACS applied materials & interfaces [ACS Appl Mater Interfaces] 2022 Jun 08; Vol. 14 (22), pp. 25629-25637. Date of Electronic Publication: 2022 May 25.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: Washington, D.C. : American Chemical Society
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MeSH Terms:
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Deep Learning*
Electric Power Supplies*
Artificial Intelligence ; Electricity ; Gestures ; Humans
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Contributed Indexing:
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Keywords: deep learning; electrospun fiber films; piezoelectric nanogenerator (PENG); self-powered; stretchable; triboelectric nanogenerator (TENG)
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Entry Date(s):
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Date Created: 20220525 Date Completed: 20220610 Latest Revision: 20220610
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Update Code:
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20240105
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DOI:
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10.1021/acsami.2c01730
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PMID:
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35612540
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A multifunctional wearable tactile sensor assisted by deep learning algorithms is developed, which can realize the functions of gesture recognition and interaction. This tactile sensor is the fusion of a triboelectric nanogenerator and piezoelectric nanogenerator to construct a hybrid self-powered sensor with a higher power density and sensibility. The power generation performance is characterized with an open-circuit voltage V OC of 200 V, a short-circuit current I SC of 8 μA, and a power density of 0.35 mW cm -2 under a matching load. It also has an excellent sensibility, including a response time of 5 ms, a signal-to-noise ratio of 22.5 dB, and a pressure resolution of 1% (1-10 kPa). The sensor is successfully integrated on a glove to collect the electrical signal output generated by the gesture. Using deep learning algorithms, the functions of gesture recognition and control can be realized in real time. The combination of tactile sensor and deep learning algorithms provides ideas and guidance for its applications in the field of artificial intelligence, such as human-computer interaction, signal monitoring, and smart sensing.
