The Human Continuity Activity Semisupervised Recognizing Model for Multiview IoT Network
文献类型：期刊论文
| 作者 | Yuan, Ruiwen1,2; Wang, Junping1,2
|
| 刊名 | IEEE INTERNET OF THINGS JOURNAL
 |
| 出版日期 | 2023-06-01 |
| 卷号 | 10期号:11页码:9398-9410 |
| ISSN号 | 2327-4662 |
| 关键词 | Feature extraction Sensors Human activity recognition Internet of Things Data models Sensor phenomena and characterization Data mining Activity feature extraction deep learning human activity recognition (HAR) semisupervised learning |
| DOI | 10.1109/JIOT.2023.3234053 |
| 通讯作者 | Yuan, Ruiwen(yuanruiwen2021@ia.ac.cn) |
| 英文摘要 | With advances in spatial-temporal Internet of Things (IoT) technologies, human activity recognition (HAR) has played a major role in human safety and medical health. Recently, most works focus on activity deep feature extraction, offering promising alternatives to manually engineered features. However, how to extract the effective and distinguishable continuity activity features and meanwhile reduce the heavy dependence on labels still remains the key challenge for HAR. This article proposes the human continuity activity semisupervised recognizing method in multiview IoT network scenarios. Our innovation combines supervised activity feature extraction with unsupervised encoder-decoder modules, which can capture continuity activity features from sensor data streams. To be more specific, our work applies a convolutional neural network (CNN) to capture the local dependence of sensor data and designs an encoder-decoder architecture to extract temporal features in an unsupervised manner. Then, we fuse these two features to recognize activities and train them with manual labels, thereby refining the temporal feature extraction and training CNN module. Experiments on five public data sets demonstrate the exceptional performance of our proposed method, which can achieve a higher recognition accuracy on almost all the data sets and is more robust and adaptive among different data sets. |
| WOS关键词 | HUMAN ACTIVITY RECOGNITION ; WEARABLE SENSOR ; LEARNING APPROACH ; FEATURE FUSION |
| 资助项目 | National Key Research and Development Program of China[2022YFF0903304] ; National Natural Science Foundation of China[92167109] ; Dadu River Cascade Hydropower Station Safety Early Warning Project |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000991733300015 |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Dadu River Cascade Hydropower Station Safety Early Warning Project |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53537]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 通讯作者 | Yuan, Ruiwen |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yuan, Ruiwen,Wang, Junping. The Human Continuity Activity Semisupervised Recognizing Model for Multiview IoT Network[J]. IEEE INTERNET OF THINGS JOURNAL,2023,10(11):9398-9410. |
| APA | Yuan, Ruiwen,&Wang, Junping.(2023).The Human Continuity Activity Semisupervised Recognizing Model for Multiview IoT Network.IEEE INTERNET OF THINGS JOURNAL,10(11),9398-9410. |
| MLA | Yuan, Ruiwen,et al."The Human Continuity Activity Semisupervised Recognizing Model for Multiview IoT Network".IEEE INTERNET OF THINGS JOURNAL 10.11(2023):9398-9410. |
入库方式： OAI收割
来源：自动化研究所
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