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	A Wearable Action Recognition System Based on Acceleration and Attitude Angles Using Real-time Detection Algorithm
	Bo Wang; Ni Xie; Guoru Zhao; Dan Lin; Yingnan Ma; Xing Gao; Cuiju Xiong; Lei Wang; Shengyun Liang; Huiqi Li
	2017
会议日期	2017
会议地点	韩国济州岛
英文摘要	Falls are a main cause of trauma and death. The purpose of this study is to adopt unique resultant acceleration and attitude angles to distinguish falls from activities of daily life before impact. In this study, we developed a wearable action recognition system to acquire action data. The moving average filter was employed to deal with raw data, and then complementary filter was adopted to compromise sensor data for attitude angles. The real-time detection algorithm embedded in this device was applied to recognize six actions based on processed data. Eight subjects (five males, three females) participated in the experiment. The optimal features and related thresholds were extracted. In addition, the real-time action detection results indicated that the real-time action recognition model reached an accuracy of 96.25%, with 98% for male and 93.3% for female. Thus, our device potentially achieves a high sensitivity of fall-related actions recognition.
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/12199]
专题	深圳先进技术研究院_医工所
作者单位	2017
推荐引用方式 GB/T 7714	Bo Wang,Ni Xie,Guoru Zhao,et al. A Wearable Action Recognition System Based on Acceleration and Attitude Angles Using Real-time Detection Algorithm[C]. 见:. 韩国济州岛. 2017.

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