期刊論文
| 學年 | 109 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2021-02-15 |
| 作品名稱 | Activities of Daily Living Recognition with Binary Environment Sensors Using Deep Learning: A Comparative Study |
| 作品名稱（其他語言） | |
| 著者 | Aiguo Wang; Shenghui Zhao; Chundi Zheng; Jing Yang; Guilin Chen; Chih-Yung Chang |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | IEEE Sensors Journal 21(4), p.5423-5433 |
| 摘要 | The power of end-to-end deep learning techniques to automatically learn latent high-level features from raw signals has been demonstrated in numerous application fields, however, few studies systematically investigate how to properly encode the time-series firings of binary environment sensors that typically work in an event-triggering scheme and have irregular sampling rates for in-home human activity recognition. To this end, we here propose two different methods to process the streaming sensor readings and accordingly evaluate their combinations with deep learning models. Specifically, we divide the multichannel sensor events into segments and encode each segment into either a vector or two-dimensional matrix. Particularly, three different feature representations are presented for the vector form. Afterwards, we combine the encoded features with four typical deep learning models and optimize corresponding activity recognizers to study their sensitivity to different feature encodings. Furthermore, we include seven commonly used shallow classification models for comparison purposes. Finally, we conduct extensive experiments on three publicly available smart home datasets. Results indicate that the performance of both deep learning and shallow models is closely associated with the raw signal encodings and demonstrate the superiority of one-dimensional convolutional neural networks over its competitors in terms of generalization across scenarios. Besides, we preliminarily explore the influence of NULL class on an activity recognizer and experimentally show its negative impact on overall accuracy, enlightening relevant studies to consider it in developing a practical activity recognition system. |
| 關鍵字 | Feature encoding;feature learning;activity recognition;smart home |
| 語言 | en |
| ISSN | 1558-1748 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/120039 ) |