Abstract
Intelligent wearable device is currently a hot spot, while the majority of smart wearable devices are concerned about the health of human information monitoring. In the study of the elderly wearable devices, the task is to find the elderly who accidentally fall, and give warning to others immediately. In order to effectively recognize the falling posture, this paper proposes a falling motion recognition algorithm combined with Kinect and wearable sensors. Although acceleration amplitude detected by three-axis accelerometer inside the wearable device can also recognize the falling motion, it is not accurate enough to distinguish the weightlessness and people’s posture after falling. Therefore, Kinect’s skeleton data is used to construct the feature vector of falling posture, and Support Vector Machines (SVM) is used to classify it. The experiments show that the accuracy of falling recognition is over 98 %, the real-time performance has been greatly improved as well.
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Acknowledgement
This paper is a funded by Guangdong provincial science and technology plan projects(No. 2015A020219001). Guangdong Ministry of Education Foundation (No. 2013B090500093). Tianhe District science and technology project (201201YH038), Guangdong Ministry of Education Foundation (No. 2011B090400590). The National Undergraduate Innovative program.
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Nana, P., Min, D., Yue, Z., Xin, C., Sheng, B. (2017). The Elderly’s Falling Motion Recognition Based on Kinect and Wearable Sensors. In: Chen, W., Hosoda, K., Menegatti, E., Shimizu, M., Wang, H. (eds) Intelligent Autonomous Systems 14. IAS 2016. Advances in Intelligent Systems and Computing, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-319-48036-7_83
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DOI: https://doi.org/10.1007/978-3-319-48036-7_83
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