A Consecutive Motion and Situation Recognition Mechanism to Detect a Vulnerable Condition Based on Android Smartphone
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
Abstract
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.
참고문헌 (17)
- Liming Chen, "Sensor-Based Activity Recognition", IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), pp.790~808 (2012), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Seon-Woo Lee, "Activity and location recognition using wearable sensors", IEEE Pervasive Computing, 1(3), pp.24~32 (2002), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Y. Kawahara, "Recognizing user context using mobile handsets with acceleration sensors", Proc. of the 2007 IEEE Int. Conf. on Portable Information Devices, pp.1~5 (2007)
- L. Bedogni, "By train or by car? Detecting the user's motion type through smartphone sensors data", Proc. of the 2012 IFIP Wireless Days, pp.1~6 (2012)
- M. Mobark, "Improving the accuracy of complex activities recognition using accelerometer-embedded mobile phone classifiers", Proc. of the 2017 2nd Int. Conf. on Informatics and Computing (2017)
- "NXP® Sensor Fusion"[URL]
- F. De Backere, "Towards a social and context-aware multi-sensor fall detection and risk assessment platform", Computers in Biology and Medicine, 64, pp.307~320 (2015), Elsevier BV[DOI]
- Kabalan Chaccour, "From Fall Detection to Fall Prevention: A Generic Classification of Fall-Related Systems", IEEE Sensors Journal, 17(3), pp.812~822 (2017), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Jong Soo Kim, "Prediction of the Location of the Glottis in Laryngeal Images by Using a Novel Deep-Learning Algorithm", IEEE Access, 7, pp.79545~79554 (2019), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- "Weka 3: Machine Learning Software in Java"[URL]
- Wanmin Wu, "Classification Accuracies of Physical Activities Using Smartphone Motion Sensors", Journal of Medical Internet Research, 14(5), pp.e130~ (2012), JMIR Publications Inc[DOI]
- X. Pang, "Motion recognition based on Kinect for human-computer intelligent interaction", Journal of Physics : Conference Series, 1187(3), pp.1~7 (2019)[DOI]
- M. P. Kumar, "Kinect Sensor Based Human Fall Detection System Using Skeleton Detection Algorithm", Proc. of the Int. Conf. on Engineering Innovations and Solutions (ICEIS-2016), pp.15~18 (2016)
- A.K. Bourke, "A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor", Medical Engineering & Physics, 30(1), pp.84~90 (2008), Elsevier BV[DOI]
- Q. Li, "Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information", Proc. of the 2009 Sixth Int. Workshop on Wearable and Implantable Body Sensor Networks, IEEE Computer Society, 9, pp.138~143 (2009)
- C. Xu, "Recurrent transformation of prior knowledge based model for human motion recognition", Computational intelligence and neuroscience, 2018, pp.1~12 (2018)[DOI]
- Jennifer R. Kwapisz, "Activity recognition using cell phone accelerometers", ACM SIGKDD Explorations Newsletter, 12(2), pp.74~82 (2011), Association for Computing Machinery (ACM)[DOI]