Institutional Repository
| A Class Incremental Extreme Learning Machine for Activity Recognition | |
| Zhao, Zhongtang; Chen, Zhenyu; Chen, Yiqiang; Wang, Shuangquan; Wang, Hongan | |
| 2014 | |
| Source | COGNITIVE COMPUTATION
 |
| ISSN | 1866-9956 |
| Volume | 6Issue:3Pages:423-431 |
| English Abstract | Automatic activity recognition is an important problem in cognitive systems. Mobile phone-based activity recognition is an attractive research topic because it is unobtrusive. There are many activity recognition models that can infer a user's activity from sensor data. However, most of them lack class incremental learning abilities. That is, the trained models can only recognize activities that were included in the training phase, and new activities cannot be added in a follow-up phase. We propose a class incremental extreme learning machine (CIELM). It (1) builds an activity recognition model from labeled samples using an extreme learning machine algorithm without iterations; (2) adds new output nodes that correspond to new activities; and (3) only requires labeled samples of new activities and not previously used training data. We have tested the method using activity data. Our results demonstrated that the CIELM algorithm is stable and can achieve a similar recognition accuracy to the batch learning method.; Automatic activity recognition is an important problem in cognitive systems. Mobile phone-based activity recognition is an attractive research topic because it is unobtrusive. There are many activity recognition models that can infer a user's activity from sensor data. However, most of them lack class incremental learning abilities. That is, the trained models can only recognize activities that were included in the training phase, and new activities cannot be added in a follow-up phase. We propose a class incremental extreme learning machine (CIELM). It (1) builds an activity recognition model from labeled samples using an extreme learning machine algorithm without iterations; (2) adds new output nodes that correspond to new activities; and (3) only requires labeled samples of new activities and not previously used training data. We have tested the method using activity data. Our results demonstrated that the CIELM algorithm is stable and can achieve a similar recognition accuracy to the batch learning method. |
| Indexed Type | SCI |
| Keyword | Extreme Learning Machine Incremental Learning Activity Recognition Mobile Device |
| Department | [Zhao, Zhongtang] Zhengzhou Inst Aeronaut Ind Management, Zhengzhou 450015, Peoples R China. [Zhao, Zhongtang; Chen, Zhenyu; Chen, Yiqiang; Wang, Shuangquan] Chinese Acad Sci, Pervas Comp Ctr, Inst Comp Technol, Beijing 100190, Peoples R China. [Wang, Hongan] Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China. |
| Language | 英语 |
| WOS ID | WOS:000341593600012 |
| Citation statistics | |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16826 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Zhao, Zhongtang,Chen, Zhenyu,Chen, Yiqiang,et al. A Class Incremental Extreme Learning Machine for Activity Recognition[J]. COGNITIVE COMPUTATION,2014,6(3):423-431. |
| APA | Zhao, Zhongtang,Chen, Zhenyu,Chen, Yiqiang,Wang, Shuangquan,&Wang, Hongan.(2014).A Class Incremental Extreme Learning Machine for Activity Recognition.COGNITIVE COMPUTATION,6(3),423-431. |
| MLA | Zhao, Zhongtang,et al."A Class Incremental Extreme Learning Machine for Activity Recognition".COGNITIVE COMPUTATION 6.3(2014):423-431. |
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