DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jeon, Suil | ko |
dc.contributor.author | Lee, Kyoung Min | ko |
dc.contributor.author | Koo, Seungbum | ko |
dc.date.accessioned | 2022-02-06T06:40:23Z | - |
dc.date.available | 2022-02-06T06:40:23Z | - |
dc.date.created | 2022-02-05 | - |
dc.date.created | 2022-02-05 | - |
dc.date.created | 2022-02-05 | - |
dc.date.created | 2022-02-05 | - |
dc.date.issued | 2022-02 | - |
dc.identifier.citation | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, v.26, no.2, pp.696 - 703 | - |
dc.identifier.issn | 2168-2194 | - |
dc.identifier.uri | http://hdl.handle.net/10203/292060 | - |
dc.description.abstract | The gait kinematics of an individual is affected by various factors, including age, anthropometry, gender, and disease. Detecting anomalous gait features aids in the diagnosis and treatment of gait-related diseases. The objective of this study was to develop a machine learning method for automatically classifying five anomalous gait features, i.e., toe-out, genu varum, pes planus, hindfoot valgus, and forward head posture features, from three-dimensional data on gait kinematics. Gait data and gait feature labels of 488 subjects were acquired. The orientations of the human body segments during a gait cycle were mapped to a low-dimensional latent gait vector using a variational autoencoder. A two-layer neural network was trained to classify five gait features using logistic regression and calculate an anomalous gait feature vector (AGFV). The proposed network showed balanced accuracies of 82.8% for a toe-out, 85.9% for hindfoot valgus, 80.2% for pes planus, 73.2% for genu varum, and 92.9% for forward head posture when the AGFV was rounded to the nearest zero or 1. Multiple anomalous gait features were detectable using the proposed method, which has a practical advantage over current gait indices, including the gait deviation index with a single value. The overall results confirmed the feasibility of using the proposed method for screening subjects with anomalous gait features using three-dimensional motion capture data. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Anomalous gait feature classification from 3-D motion capture data | - |
dc.type | Article | - |
dc.identifier.wosid | 000772331200020 | - |
dc.identifier.scopusid | 2-s2.0-85112642157 | - |
dc.type.rims | ART | - |
dc.citation.volume | 26 | - |
dc.citation.issue | 2 | - |
dc.citation.beginningpage | 696 | - |
dc.citation.endingpage | 703 | - |
dc.citation.publicationname | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | - |
dc.identifier.doi | 10.1109/JBHI.2021.3101549 | - |
dc.contributor.localauthor | Koo, Seungbum | - |
dc.contributor.nonIdAuthor | Jeon, Suil | - |
dc.contributor.nonIdAuthor | Lee, Kyoung Min | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | anomalous gait | - |
dc.subject.keywordAuthor | feedforward neural networks | - |
dc.subject.keywordAuthor | Human gait | - |
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