지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.9
- 수록면
- 1 - 17 (17page)
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초록· 키워드
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Human motion recognition is essential for user-centric services such as surveillance-based security, elderly condition monitoring, exercise tracking, daily calories expend analysis, etc. It is typically based on the movement data analysis such as the acceleration and angular velocity of a target user. The existing motion recognition studies are only intended to measure the basic information (e.g., user’s stride, number of steps, speed) or to recognize single motion (e.g., sitting, running, walking). Thus, a new mechanism is required to identify the transition of single motions for assessing a user’s consecutive motion more accurately as well as recognizing the user’s body and surrounding situations arising from the motion. Thus, in this paper, we collect the human movement data through Android smartphones in real time for five targeting single motions and propose a mechanism to recognize a consecutive motion including transitions among various motions and an occurred situation, with the state transition model to check if a vulnerable (life-threatening) condition, especially for the elderly, has occurred or not. Through implementation and experiments, we demonstrate that the proposed mechanism recognizes a consecutive motion and a user’s situation accurately and quickly. As a result of the recognition experiment about mix sequence likened to daily motion, the proposed adoptive weighting method showed 4% (Holding time=15 sec), 88% (30 sec), 6.5% (60 sec) improvements compared to static method.
목차
Abstract
1. Introduction
2. Related Work
3. Consecutive Motion and Situation Recognition Mechanism
4. Performance Evaluation Results
5. Conclusions
References
참고문헌 (17)
참고문헌 신청
Liming Chen / 2012 / Sensor-Based Activity Recognition / IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42(6):790~808
Seon-Woo Lee / 2002 / Activity and location recognition using wearable sensors / IEEE Pervasive Computing 1(3):24~32
Y. Kawahara / 2007 / Recognizing user context using mobile handsets with acceleration sensors / Proc. of the 2007 IEEE Int. Conf. on Portable Information Devices :1~5
L. Bedogni / 2012 / By train or by car? Detecting the user's motion type through smartphone sensors data / Proc. of the 2012 IFIP Wireless Days :1~6
M. Mobark / 2017 / Improving the accuracy of complex activities recognition using accelerometer-embedded mobile phone classifiers / Proc. of the 2017 2nd Int. Conf. on Informatics and Computing
Seon-Woo Lee / 2002 / Activity and location recognition using wearable sensors / IEEE Pervasive Computing 1(3):24~32
Y. Kawahara / 2007 / Recognizing user context using mobile handsets with acceleration sensors / Proc. of the 2007 IEEE Int. Conf. on Portable Information Devices :1~5
L. Bedogni / 2012 / By train or by car? Detecting the user's motion type through smartphone sensors data / Proc. of the 2012 IFIP Wireless Days :1~6
M. Mobark / 2017 / Improving the accuracy of complex activities recognition using accelerometer-embedded mobile phone classifiers / Proc. of the 2017 2nd Int. Conf. on Informatics and Computing
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