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논문 기본정보
Wearable Sensor-Based Biometric Gait Classification Algorithm Using WEKA
논문 개요
| 기관명 | NDSL |
|---|---|
| 저널명 | Journal of information and communication convergence engineering |
| ISSN | 2234-8255,2234-8883 |
| ISBN |
논문저자 및 소속기관 정보
| 저자(한글) | Youn, Ik-Hyun,Won, Kwanghee,Youn, Jong-Hoon,Scheffler, Jeremy |
|---|---|
| 저자(영문) | |
| 소속기관 | |
| 소속기관(영문) | |
| 출판인 | |
| 간행물 번호 | |
| 발행연도 | 2016-01-01 |
| 초록 | Gait-based classification has gained much interest as a possible authentication method because it incorporate an intrinsic personal signature that is difficult to mimic. The study investigates machine learning techniques to mitigate the natural variations in gait among different subjects. We incorporated several machine learning algorithms into this study using the data mining package called Waikato Environment for Knowledge Analysis (WEKA). WEKA's convenient interface enabled us to apply various sets of machine learning algorithms to understand whether each algorithm can capture certain distinctive gait features. First, we defined 24 gait features by analyzing three-axis acceleration data, and then selectively used them for distinguishing subjects 10 years of age or younger from those aged 20 to 40. We also applied a machine learning voting scheme to improve the accuracy of the classification. The classification accuracy of the proposed system was about 81% on average. |
| 원문URL | http://click.ndsl.kr/servlet/OpenAPIDetailView?keyValue=03553784&target=NART&cn=JAKO201609651052682 |
| 첨부파일 |
추가정보
| 과학기술표준분류 | |
|---|---|
| ICT 기술분류 | |
| DDC 분류 | |
| 주제어 (키워드) | Classification,Gait analysis,Machine learning algorithm,WEKA |