상세정보
학술지
Unsupervised Learning for Human Activity Recognition Using Smartphone Sensors
- 발행일
- 201410
- 출처
- Expert Systems with Applications, v.41 no.14, pp.6067-6074
- ISSN
- 0957-4174
- 출판사
- Elsevier
- 협약과제
- 14NS5100, Connected Self: 라이프로그 정보와 스트림형 데이터 마이닝을 통한 건강 모니터링, 배창석
- 초록
- To provide more sophisticated healthcare services, it is necessary to collect the precise information on a patient. One impressive area of study to obtain meaningful information is human activity recognition, which has proceeded through the use of supervised learning techniques in recent decades. Previous studies, however, have suffered from generating a training dataset and extending the number of activities to be recognized. In this paper, to find out a new approach that avoids these problems, we propose unsupervised learning methods for human activity recognition, with sensor data collected from smartphone sensors even when the number of activities is unknown. Experiment results show that the mixture of Gaussian exactly distinguishes those activities when the number of activities k is known, while hierarchical clustering or DBSCAN achieve above 90% accuracy by obtaining k based on Cali흦ski-Harabasz index, or by choosing appropriate values for and MinPts when k is unknown. We believe that the results of our approach provide a way of automatically selecting an appropriate value of k at which the accuracy is maximized for activity recognition, without the generation of training datasets by hand. © 2014 Elsevier Ltd. All rights reserved.
- KSP 제안 키워드
- Data collected, Experiment results, Healthcare Services, Hierarchical Clustering, Human activity recognition(HAR), Learning methods, Meaningful information, New approach, Smartphone sensors, Supervised Learning Techniques, mixture of Gaussian