A wearable action recognition system based on acceleration and attitude angles using real-time detection algorithm
| Autor: | Lei Wang, Bo Wang, Cuiju Xiong, Xing Gao, Shengyun Liang, Li Huiqi, Yingnan Ma, Guoru Zhao, Xie Ni |
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| Rok vydání: | 2017 |
| Předmět: |
Male
Engineering 0206 medical engineering Acceleration Wearable computer Monitoring Ambulatory 02 engineering and technology 01 natural sciences Wearable Electronic Devices Moving average Activities of Daily Living Humans Computer vision Sensitivity (control systems) Complementary filter business.industry 010401 analytical chemistry 020601 biomedical engineering 0104 chemical sciences Action (philosophy) Action recognition Accidental Falls Female Artificial intelligence business Raw data Algorithm Algorithms |
| Zdroj: | EMBC |
| ISSN: | 2694-0604 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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