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

Yegang Du, Yuto Lim, Yasuo Tan

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)

Abstract

�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.

Original languageEnglish
Article number4474
JournalSensors (Switzerland)
Volume19
Issue number20
DOIs
Publication statusPublished - 2019 Oct 2
Externally publishedYes

Keywords

  • Activity prediction
  • Human activity recognition
  • Object usage sensing
  • RFID
  • Smart home

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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