| Autor: |
Koichi Kurita, Syota Morinaga |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 7, Pp 139226-139235 (2019) |
| Druh dokumentu: |
article |
| 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: |
Directory of Open Access Journals |
| Externí odkaz: |
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