상세정보
학술대회
Human Activity Recognition using Wearable Accelerometer Sensors
- 발행일
- 201610
- 출처
- International Conference on Consumer Electronics (ICCE) 2016 : Asia, pp.273-277
- 협약과제
- 16HH1800, 인체활동 통합관리지원을 위한 다중 웨어러블 SW융합모듈 및 유연 SW 응용플랫폼 기술개발 , 송기봉
- 초록
- Human Activity recognition has a wide range of applications such as remote patient monitoring, rehabilitation and assisting disables. Physical activity reduces the risk of many chronic diseases and is consider as a key factor for healthy life. In order to improve the state of global healthcare, numerous healthcare devices has been introduced that allows doctors to perform remote monitoring and increase users' motivation and awareness. Real time activity recognition systems encourage users to adopt healthier life style by increasing personal awareness about physical activities and its positive consequences on health. In this paper a machine learning based technique is proposed to enhance the accuracy of activity recognition system using feature selection method on an appropriate set of statistically derived features. A publically available HAR dataset on physical activities has been used in this work. Linear forward selection method is employed for feature selection. Activity classification is performed using Random forest and decision tree in connection with AdaBoost. The proposed technique outperforms the recent works.
- KSP 제안 키워드
- Activity recognition systems, Decision Tree(DT), Feature selection(FS), Forward selection(FS), Human activity recognition(HAR), Key factor, Machine learning based, Physical activity, Random forest, Real-Time, Remote Patient Monitoring