Taştan, A. ; Muma, M. ; Zoubir, A. M. (2020)
An Unsupervised Approach for Graph-based Robust Clustering of Human Gait Signatures.
IEEE Radar Conference. virtual Conference (21.-25.09.2020)
doi: 10.1109/RadarConf2043947.2020.9266313
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Classification of gait abnormalities plays a key role in medical diagnosis, sports, physiotherapy and rehabilitation. We demonstrate the effectiveness of a new graph construction-based outlier detection method and and the applicability of a new parameter-free clustering approach on radar-based human gait signatures. Micro-Doppler radar-based human gait signatures of ten test subjects for five different gait types consisting of normal, simulated abnormal and assisted walks are clustered using five different clustering algorithms. The proposed algorithm outperforms existing methods both in cluster enumeration and partition and achieves an overall correct clustering rate of 92.8%. The developed method is promising for performing medical diagnosis in a robust unsupervised fashion.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Taştan, A. ; Muma, M. ; Zoubir, A. M. |
| Art des Eintrags: | Bibliographie |
| Titel: | An Unsupervised Approach for Graph-based Robust Clustering of Human Gait Signatures |
| Sprache: | Englisch |
| Publikationsjahr: | 4 Dezember 2020 |
| Verlag: | IEEE |
| Buchtitel: | 2020 IEEE Radar Conference (RadarConf'20) |
| Veranstaltungstitel: | IEEE Radar Conference |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 21.-25.09.2020 |
| DOI: | 10.1109/RadarConf2043947.2020.9266313 |
| Kurzbeschreibung (Abstract): | Classification of gait abnormalities plays a key role in medical diagnosis, sports, physiotherapy and rehabilitation. We demonstrate the effectiveness of a new graph construction-based outlier detection method and and the applicability of a new parameter-free clustering approach on radar-based human gait signatures. Micro-Doppler radar-based human gait signatures of ten test subjects for five different gait types consisting of normal, simulated abnormal and assisted walks are clustered using five different clustering algorithms. The proposed algorithm outperforms existing methods both in cluster enumeration and partition and achieves an overall correct clustering rate of 92.8%. The developed method is promising for performing medical diagnosis in a robust unsupervised fashion. |
| Freie Schlagworte: | emergenCITY_CPS |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Robust Data Science 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Signalverarbeitung 20 Fachbereich Informatik 20 Fachbereich Informatik > Sichere Mobile Netze LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > emergenCITY |
| TU-Projekte: | HMWK|III L6-519/03/05.001-(0016)|emergenCity TP Bock |
| Hinterlegungsdatum: | 29 Sep 2020 07:38 |
| Letzte Änderung: | 02 Feb 2023 09:52 |
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