Wearable Inertial Sensor-Based Hand-Guiding Gestures Recognition Method Robust to Significant Changes in the Body-Alignment of Subject
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jeon, Haneul | - |
dc.contributor.author | Choi, Haegyeom | - |
dc.contributor.author | Noh, Donghyeon | - |
dc.contributor.author | Kim, Taeho | - |
dc.contributor.author | Lee, Donghun | - |
dc.date.accessioned | 2023-03-09T07:40:03Z | - |
dc.date.available | 2023-03-09T07:40:03Z | - |
dc.date.created | 2023-02-27 | - |
dc.date.issued | 2022-12 | - |
dc.identifier.issn | 2227-7390 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/43345 | - |
dc.description.abstract | The accuracy of the wearable inertia-measurement-unit (IMU)-sensor-based gesture recognition may be significantly affected by undesired changes in the body-fixed frame and the sensor-fixed frame according to the change in the subject and the sensor attachment. In this study, we proposed a novel wearable IMU-sensor-based hand-guiding gesture recognition method robust to significant changes in the subject's body alignment based on the floating body-fixed frame method and the bi-directional long short-term memory (bi-LSTM). Through comparative experimental studies with the other two methods, it was confirmed that aligning the sensor-fixed frame with the reference frame of the human body and updating the reference frame according to the change in the subject's body-heading direction helped improve the generalization performance of the gesture recognition model. As a result, the proposed floating body-fixed frame method showed a 91.7% test accuracy, confirming that it was appropriate for gesture recognition under significant changes in the subject's body alignment during gestures. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | MATHEMATICS | - |
dc.title | Wearable Inertial Sensor-Based Hand-Guiding Gestures Recognition Method Robust to Significant Changes in the Body-Alignment of Subject | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/math10244753 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | MATHEMATICS, v.10, no.24 | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000904597300001 | - |
dc.identifier.scopusid | 2-s2.0-85144700106 | - |
dc.citation.number | 24 | - |
dc.citation.title | MATHEMATICS | - |
dc.citation.volume | 10 | - |
dc.contributor.affiliatedAuthor | Lee, Donghun | - |
dc.identifier.url | https://www.mdpi.com/2227-7390/10/24/4753 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.subject.keywordAuthor | gesture recognition | - |
dc.subject.keywordAuthor | bi-directional LSTM | - |
dc.subject.keywordAuthor | wearable sensor | - |
dc.subject.keywordAuthor | biomechanics | - |
dc.subject.keywordAuthor | hand-guiding gesture | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Mathematics | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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