Detection Technique of Individual Characteristic Appearing in Walking Motion
Autor: | Syota Morinaga, Koichi Kurita |
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Rok vydání: | 2019 |
Předmět: |
General Computer Science
Computer science Measure (physics) convolutional neural network Electrostatic induction 02 engineering and technology 01 natural sciences Convolutional neural network Signal 010309 optics Wavelet walking motion 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Waveform General Materials Science Sensitivity (control systems) person identification business.industry General Engineering deep learning 020207 software engineering Pattern recognition Transformation (function) lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business lcsh:TK1-9971 human activities |
Zdroj: | IEEE Access, Vol 7, Pp 139226-139235 (2019) |
ISSN: | 2169-3536 |
DOI: | 10.1109/access.2019.2943495 |
Popis: | In this study, we develop a technique to measure walking under noncontact conditions by using an ultrahigh sensitivity electrostatic induction technique. The results of walking measurements using this technique indicate that the detected electrostatic-induced current waveform exhibits a peak at the time of foot contact or detachment owing to walking. Based on these results, we construct a theoretical model in which an induced current is generated, and the correspondence relationship between walking and electrostatic-induced current is revealed. Furthermore, when comparing the walking signals of each participant, we used a scalogram obtained by performing a wavelet transformation on the walking signal. Person identification was attempted by learning the participant's scalogram using a convolutional neural network (CNN). |
Databáze: | OpenAIRE |
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