A Wearable Action Recognition System Based on Acceleration and Attitude Angles Using Real-time Detection Algorithm
文献类型：会议论文
| 作者 | Bo Wang; Ni Xie; Guoru Zhao; Dan Lin; Yingnan Ma; Xing Gao; Cuiju Xiong; Lei Wang; Shengyun Liang; Huiqi Li |
| 出版日期 | 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 |
| 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. |
入库方式： OAI收割
来源：深圳先进技术研究院
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