English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 54546/89241 (61%)
造訪人次 : 10577281      線上人數 : 37
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/112739


    題名: Latent feature learning for activity recognition using simple sensors in smart homes
    作者: Guilin Chen;Aiguo Wang;Shenghui Zhao;Li Liu;Chih-Yung Chang
    關鍵詞: Activity recognition Smart home Feature learning Autoencoder Shallow model
    日期: 2018-06
    上傳時間: 2018-03-02 12:11:24 (UTC+8)
    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.
    關聯: Multimedia Tools and Applications, vol. 77,no.12 pp. 15201-15219
    DOI: 10.1007/s11042-017-5100-4
    顯示於類別:[資訊工程學系暨研究所] 期刊論文

    文件中的檔案:

    檔案 大小格式瀏覽次數
    index.html0KbHTML68檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋