A novel human activity recognition and prediction in smart home based on interaction

Yegang Du, Yuto Lim, Yasuo Tan

研究成果: Article査読

39 被引用数 (Scopus)

抄録

�Smart Homes are generally considered the final solution for living problem, especially for the health care of the elderly and disabled, power saving, etc. Human activity recognition in smart homes is the key to achieving home automation, which enables the smart services to automatically run according to the human mind. Recent research has made a lot of progress in this field; however, most of them can only recognize default activities, which is probably not needed by smart homes services. In addition, low scalability makes such research infeasible to be used outside the laboratory. In this study, we unwrap this issue and propose a novel framework to not only recognize human activity but also predict it. The framework contains three stages: recognition after the activity, recognition in progress, and activity prediction in advance. Furthermore, using passive RFID tags, the hardware cost of our framework is sufficiently low to popularize the framework. In addition, the experimental result demonstrates that our framework can realize good performance in both activity recognition and prediction with high scalability.

本文言語English
論文番号4474
ジャーナルSensors (Switzerland)
19
20
DOI
出版ステータスPublished - 2019 10月 2
外部発表はい

ASJC Scopus subject areas

  • 分析化学
  • 生化学
  • 原子分子物理学および光学
  • 器械工学
  • 電子工学および電気工学

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