DC Field | Value | Language |
---|---|---|
dc.contributor.author | 전수일 | ko |
dc.contributor.author | 구승범 | ko |
dc.date.accessioned | 2022-01-04T06:48:26Z | - |
dc.date.available | 2022-01-04T06:48:26Z | - |
dc.date.created | 2021-12-28 | - |
dc.date.issued | 2020-08-25 | - |
dc.identifier.citation | 대한기계학회 바이오공학부문 학술대회 | - |
dc.identifier.uri | http://hdl.handle.net/10203/291534 | - |
dc.description.abstract | Through numerous gait in a lifetime, pedestrians have their own unique gait patterns and many studies have analyzed gait patterns to distinguish the person’s identity Various methods are used for gait analysis to distinguish a person's identity, but existing methods have poor performance in gait prediction in different environment environments. In this study, we used a nonlinear automatic encoder to predict gait 3D kinematics in various environments. Trajectories of joint centers in one gait cycle were represented as a gait data of subject and obtained for 488 subjects by a motion capture system. Each segment’s orientation sequence in three-dimensional gait data was converted into a lower-dimensional vector by two auto-encoder layers. Using gait feature vectors extracted through a trained autoencoder, motion transfer with parameter converting between three different environments performed. This study showed that our method can extract lower-dimensional features of the gait data and can be used for gait motion prediction in different environments. | - |
dc.language | Korean | - |
dc.publisher | 대한기계학회 | - |
dc.title | Gait motion prediction in different environments using the autoencoder-based gait feature extraction method | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | 대한기계학회 바이오공학부문 학술대회 | - |
dc.identifier.conferencecountry | KO | - |
dc.identifier.conferencelocation | 강원도 강릉(온라인) | - |
dc.contributor.localauthor | 구승범 | - |
dc.contributor.nonIdAuthor | 전수일 | - |
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