skip to main content
10.1145/2674396.2674405acmotherconferencesArticle/Chapter ViewAbstractPublication PagespetraConference Proceedingsconference-collections
research-article

Gesture recognition in smart home using passive RFID technology

Published: 27 May 2014 Publication History

Abstract

Gesture recognition is a well-establish topic of research that is widely adopted for a broad range of applications. For instance, it can be exploited for the command of a smart environment without any remote control unit or even for the recognition of human activities from a set of video cameras deployed in strategic position. Many researchers working on assistive smart home, such as our team, believe that the intrusiveness of that technology will prevent the future adoption and commercialization of smart homes. In this paper, we propose a novel gesture recognition algorithm that is solely based on passive RFID technology. This technology enables the localization of small tags that can be embedded in everyday life objects (a cup or a book, for instance) while remaining non intrusive. However, until now, this technology has been largely ignored by researchers on gesture recognition, mostly because it is easily disturbed by noise (metal, human, etc.) and offer limited precision. Despite these issues, the localization algorithms have improved over the years, and our recent efforts resulted in a real-time tracking algorithm with a precision approaching 14cm. With this, we developed a gesture recognition algorithm able to perform segmentation of gestures and prediction on a spatio-temporal data series. Our new model, exploiting works on qualitative spatial reasoning, achieves recognition of 91%. Our goal is to ultimately use that knowledge for both human activity recognition and errors detection.

References

[1]
K. Bouchard, B. Bouchard, and A. Bouzouane, "Guideline to Efficient Smart Home Design for Rapid AI Prototyping: A Case Study," in International Conference on PErvasive Technologies Related to Assistive Environments, Crete Island, Greece, 2012.
[2]
J. C. Augusto and C. D. Nugent, "Smart homes can be smarter," in Designing Smart Homes: Role of Artificial Intelligence. vol. 4008, ed Berlin: Springer-Verlag Berlin, 2006, pp. 1--15.
[3]
D. J. Patterson, D. Fox, H. Kautz, and M. Philipose, "Fine-Grained Activity Recognition by Aggregating Abstract Object Usage," presented at the Proceedings of the Ninth IEEE International Symposium on Wearable Computers, 2005.
[4]
K. Bouchard, B. Bouchard, and A. Bouzouane, "Spatial recognition of activities for cognitive assistance: realistic scenarios using clinical data from Alzheimer's patients," Journal of Ambient Intelligence and Humanized Computing, pp. 1--16, 2013/09/13 2013.
[5]
V. Jakkula and D. J. Cook, "Mining Sensor Data in Smart Environment for Temporal Activity Prediction," in KDD'07, San Jose, California, USA, 2007.
[6]
J. C. Augusto, J. Liu, P. McCullagh, and H. Wang, "Management of uncertainty and spatio-temporal aspects for monitoring and diagnosis in a Smart Home," International Journal of Computational Intelligence Systems vol. 1, pp. 361--378, 2008.
[7]
T. Westeyn, H. Brashear, A. Atrash, and T. Starner, "Georgia tech gesture toolkit: supporting experiments in gesture recognition," in Proceedings of the 5th international conference on Multimodal interfaces, 2003, pp. 85--92.
[8]
S. Mitra and T. Acharya, "Gesture recognition: A survey," Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 37, pp. 311--324, 2007.
[9]
J. Liu, L. Zhong, J. Wickramasuriya, and V. Vasudevan, "uWave: Accelerometer-based personalized gesture recognition and its applications," Pervasive and Mobile Computing, vol. 5, pp. 657--675, 2009.
[10]
K. Mäkelä, S. Belt, D. Greenblatt, and J. Häkkilä, "Mobile interaction with visual and RFID tags: a field study on user perceptions," presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, San Jose, California, USA, 2007.
[11]
L. M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil, "LANDMARC: Indoor Location Sensing Using Active RFID," ACM Wireless Networks, vol. 10, pp. 701--710, 2004.
[12]
P. Vorst, S. Schneegans, Y. Bin, and A. Zell, "Self-Localization with RFID snapshots in densely tagged environments," in Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on, 2008, pp. 1353--1358.
[13]
D. Joho, C. Plagemann, and W. Burgard, "Modeling RFID signal strength and tag detection for localization and mapping," presented at the Proceedings of the 2009 IEEE international conference on Robotics and Automation, Kobe, Japan, 2009.
[14]
F.-S. Chen, C.-M. Fu, and C.-L. Huang, "Hand gesture recognition using a real-time tracking method and hidden Markov models," Image and vision computing, vol. 21, pp. 745--758, 2003.
[15]
C. Shan, Y. Wei, T. Tan, and F. Ojardias, "Real time hand tracking by combining particle filtering and mean shift," in Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on, 2004, pp. 669--674.
[16]
F. Samaria and S. Young, "HMM-based architecture for face identification," Image and vision computing, vol. 12, pp. 537--543, 1994.
[17]
L. Bretzner, I. Laptev, and T. Lindeberg, "Hand gesture recognition using multi-scale colour features, hierarchical models and particle filtering," in Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference on, 2002, pp. 423--428.
[18]
P. Hong, M. Turk, and T. S. Huang, "Gesture modeling and recognition using finite state machines," in Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on, 2000, pp. 410--415.
[19]
C. Y. Chen, J. P. Yang, G. J. Tseng, Y. H. Wu, and R. C. Hwang, "An Indoor positioning technique based on fuzzy logic," in MultiConference of Engineers and Computer Scientists, Hong Kong, 2010.
[20]
P. Asadzadeh, L. Kulik, and E. Tanin, "Gesture recognition using RFID technology," Personal and Ubiquitous Computing, vol. 16, pp. 225--234, 2012/03/01 2012.
[21]
D. Fortin-Simard, K. Bouchard, S. Gaboury, B. Bouchard, and A. Bouzouane, "Accurate Passive RFID Localization System for Smart Homes," presented at the 3th IEEE International Conference on Networked Embedded Systems for Every Application, Liverpool, UK, 2012.
[22]
E. Clementini, P. D. Felice, and D. Hernández, "Qualitative representation of positional information," Artificial Intelligence, vol. 95, pp. 317--356, 1997.

Cited By

View all
  • (2023)Robust Identification System for Spanish Sign Language Based on Three-Dimensional Frame InformationSensors10.3390/s2301048123:1(481)Online publication date: 2-Jan-2023
  • (2023)Fine-Grained and Real-Time Gesture Recognition by Using IMU SensorsIEEE Transactions on Mobile Computing10.1109/TMC.2021.312047522:4(2177-2189)Online publication date: 1-Apr-2023
  • (2023)RF-Ray: Sensing Objects in the Package via RFID SystemsIEEE Systems Journal10.1109/JSYST.2022.319646217:1(558-568)Online publication date: Mar-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
PETRA '14: Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments
May 2014
408 pages
ISBN:9781450327466
DOI:10.1145/2674396
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • iPerform Center: iPerform Center for Assistive Technologies to Enhance Human Performance
  • CSE@UTA: Department of Computer Science and Engineering, The University of Texas at Arlington
  • HERACLEIA: HERACLEIA Human-Centered Computing Laboratory at UTA
  • U of Tex at Arlington: U of Tex at Arlington
  • NCRS: Demokritos National Center for Scientific Research
  • Fulbrigh, Greece: Fulbright Foundation, Greece

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2014

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. gesture recognition
  2. passive RFID
  3. smart home
  4. spatio-temporal series
  5. trilateration

Qualifiers

  • Research-article

Funding Sources

  • Quebec Research Fund on Nature and Technologies
  • Canadian Foundation for Innovation
  • Natural Sciences and Engineering Research Council of Canada

Conference

PETRA '14
Sponsor:
  • iPerform Center
  • CSE@UTA
  • HERACLEIA
  • U of Tex at Arlington
  • NCRS
  • Fulbrigh, Greece

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)9
  • Downloads (Last 6 weeks)1
Reflects downloads up to 23 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Robust Identification System for Spanish Sign Language Based on Three-Dimensional Frame InformationSensors10.3390/s2301048123:1(481)Online publication date: 2-Jan-2023
  • (2023)Fine-Grained and Real-Time Gesture Recognition by Using IMU SensorsIEEE Transactions on Mobile Computing10.1109/TMC.2021.312047522:4(2177-2189)Online publication date: 1-Apr-2023
  • (2023)RF-Ray: Sensing Objects in the Package via RFID SystemsIEEE Systems Journal10.1109/JSYST.2022.319646217:1(558-568)Online publication date: Mar-2023
  • (2021)Sign in with Face Recognition and Gesture Recognition Based on Raspberry Pi2021 3rd World Symposium on Artificial Intelligence (WSAI)10.1109/WSAI51899.2021.9486343(20-23)Online publication date: 18-Jun-2021
  • (2020)Perspective and Evolution of Gesture Recognition for Sign Language: A ReviewSensors10.3390/s2012357120:12(3571)Online publication date: 24-Jun-2020
  • (2019)Are RFID Sensing Systems Ready for the Real World?Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services10.1145/3307334.3326084(366-377)Online publication date: 12-Jun-2019
  • (2019)Grouping Strategy for RFID-Based Activity Recognition in Smart Home2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00038(96-101)Online publication date: Jul-2019
  • (2019)Close-Proximity Detection for Hand Approaching Using Backscatter CommunicationIEEE Transactions on Mobile Computing10.1109/TMC.2018.287255818:10(2285-2297)Online publication date: 1-Oct-2019
  • (2019)In-Air Gesture Interaction: Real Time Hand Posture Recognition Using Passive RFID TagsIEEE Access10.1109/ACCESS.2019.2928318(1-1)Online publication date: 2019
  • (2017)SeleConProceedings of the Second International Conference on Internet-of-Things Design and Implementation10.1145/3054977.3054981(47-58)Online publication date: 18-Apr-2017
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media