A Real-time Human Activity Recognition Approach with Generalization Performance
文献类型：会议论文
| 作者 | Wei, Shi-Jie4; Zhang B(张弼)2,3 ; Tan XW(谈晓伟)1,2,3; Zhao XG(赵新刚)2,3; Ye D(叶丹)4
|
| 出版日期 | 2020 |
| 会议日期 | July 27-29, 2020 |
| 会议地点 | Shenyang, China |
| 关键词 | Human Activity Recognition Feature Selection Generalization Performance Real-time Performance Transfer Learning |
| 页码 | 6334-6339 |
| 英文摘要 | The current human activity recognition (HAR) methods need training data from users. The data collection causes discomfort to the users and most of the studies ignore the real-time performance of classification. This paper presents a real-time human activity recognition approach with strong generalization performance. It uses existing dataset to avoid long-term data collection of subjects, so that the machine can be quickly applied to each specific individual. Also, it takes advantage of both combined accuracy and limited feature selection proposed by this paper to implement feature-selection-based transfer learning which improves HAR in both real-time and generalization performance. In view of the recognition time and accuracy, the depth neural network is selected, changeable structure of which is more suitable for feature selection. This approach utilizes four inertial measurement units placed on the outside of human thighs and shanks. A total of seven activities are taken into account that includes level-walking, upstairs, downstairs, uphill, downhill, standing and sitting. The experiments are performed on six healthy male subjects in free-living settings to evaluate the efficacy of the algorithm. This approach achieved a notable activity recognition accuracy of 98.89%, and reported a fast average activity classification time of 28.6 ms. |
| 源文献作者 | Systems Engineering Society of China (SESC) ; Technical Committee on Control Theory (TCCT) of Chinese Association of Automation (CAA) |
| 产权排序 | 2 |
| 会议录 | Proceedings of the 39th Chinese Control Conference, CCC 2020
 |
| 会议录出版者 | IEEE Computer Society |
| 会议录出版地 | Washington, USA |
| 语种 | 英语 |
| ISSN号 | 1934-1768 |
| ISBN号 | 978-9-8815-6390-3 |
| WOS记录号 | WOS:000629243506084 |
| 源URL | [http://ir.sia.cn/handle/173321/27703]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Zhang B(张弼) |
| 作者单位 | 1.University of Chinese Academy of Sciences, Beijing 100049, P. R. China 2.Institutes of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, P. R. China 3.State Key Lab of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P. R. China 4.College of Information Science and Engineering, Northeastern University, Shenyang 110819, P. R. China |
| 推荐引用方式 GB/T 7714 | Wei, Shi-Jie,Zhang B,Tan XW,et al. A Real-time Human Activity Recognition Approach with Generalization Performance[C]. 见:. Shenyang, China. July 27-29, 2020. |
入库方式： OAI收割
来源：沈阳自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。