期刊論文
| 學年 | 106 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2018-06-01 |
| 作品名稱 | Latent feature learning for activity recognition using simple sensors in smart homes |
| 作品名稱（其他語言） | |
| 著者 | Guilin Chen；Aiguo Wang；Shenghui Zhao；Li Liu；Chih-Yung Chang |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Multimedia Tools and Applications, vol. 77,no.12 pp. 15201-15219 |
| 摘要 | Activity recognition is an important step towards monitoring and evaluating the functional health of an individual, and it potentially promotes human-centric ubiquitous applications in smart homes particularly for senior healthcare. The nature of human activity characterized by a high degree of complexity and uncertainty, however, poses a great challenge to the design of good feature representations and the optimization of classifiers towards building a robust model for human activity recognition. In this study, we propose to exploit deep learning techniques to automatically learn high-level features from the binary sensor data under the assumption that there exist discriminative latent patterns inherent in the simple low-level features. Specifically, we extract high-level features with a stacked autoencoder that has a deep and hierarchy architecture, and combine feature learning and classifier construction into a unified framework to obtain a jointly optimized activity recognizer. Besides, we investigate two different original feature representations of the sensor data for latent feature learning. To evaluate the performance of the proposed method, we conduct extensive experiments on three publicly available smart home datasets, and compare it with a range of shallow models in terms of time-slice accuracy and class accuracy. Experimental results show that our proposed model achieves better recognition rates and generalizes better across different original feature representations, indicating its applicability to the real-world activity recognition. |
| 關鍵字 | Activity recognition Smart home Feature learning Autoencoder Shallow model |
| 語言 | en |
| ISSN | 1380-7501 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | TWN |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/112739 ) |