Earticle

초록

영어

Gesture recognition is a field of research that consists in recognizing patterns of movement performed by a human using his body parts with or without the help of a comprehensive device (a mouse, a laser, etc.). This particular area as attracted a number of researchers over the years that applied such algorithms in a broad range of disciplines. In particular, it was exploited on early research initiatives with pervasive environments to enable simple communication with automation systems. Nowadays, those environments are used for more than automation. Many researchers, in fact, believe it is one of the most promising solutions to the problems related to ageing of the population. Smart homes are seen as an alternative to the full-time support of a semi-autonomous person by healthcare professional and thus also a potentially economically viable solution to the rising cost of such support. However, researchers are still facing many challenges in that regards, such as the comprehension of the context and of the ongoing activity of daily living. In that equation, gesture recognition could help extract more information from the collected data and thus reinforce the context modeling. The knowledge extracted could even help with monitoring of more fine-grained activities and with the understanding of normal or abnormal behaviors. Gesture recognition is often considered as a solved problem since the techniques to perform it work well as soon as we can track accurately. However, in smart environment, tracking is very imprecise and hard to achieve with limited technology (i.e. noninvasive sensors). In this paper, we present a novel gesture recognition algorithm that works under uncertainty, and that is scalable to the precision of the tracking system. The algorithm is based on the tracking of passive RFID tags installed on all everyday life objects in a smart environment. A set of experimentation in simulation and in a real smart home environment is presented. The results are very encouraging despite the very low accuracy of the passive RFID tracking system.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Main Gesture Recognition Models
  2.2. RFID Tracking
  2.3. Gesture Recognition using RFID
 3. Tracking Objects
  3.1. Addressing the false reading
  3.2. Stabilization of the RSSI
  3.3. Elliptical Trilateration
 4. Gesture Recognition
  4.1. Data Processing
  4.2. Segmentation
  4.3. Matching the Gestures
 5. Experiments
  5.1. Generation of Random Gestures
  5.2. Experiments with a Human Subject
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Kevin Bouchard [ DOMUS Laboratory, Universite de Sherbrooke, Canada ]
• Sylvain Giroux [ DOMUS Laboratory, Universite de Sherbrooke, Canada ]
• Bruno Bouchard [ LIARA laboratory, Universite du Quebec a Chicoutimi, Canada ]
• Abdenour Bouzouane [ LIARA laboratory, Universite du Quebec a Chicoutimi, Canada ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605