skip to main content
10.1145/2674396.2674403acmotherconferencesArticle/Chapter ViewAbstractPublication PagespetraConference Proceedingsconference-collections
research-article

Human activity recognition in smart homes based on passive RFID localization

Published: 27 May 2014 Publication History

Abstract

Modern societies are facing an important ageing of their population leading to arising economical and sociological challenges such as the pressure on health support services for semi-autonomous persons. Smart home technology is considered by many researchers as a promising potential solution to help supporting the needs of elders. It aims to provide cognitive assistance by taking decisions, such as giving hints, suggestions and reminders, with different kinds of effectors (light, sound, screen, etc.) to a resident suffering from cognitive deficits in order to foster their autonomy. To implement such technology, the first challenge we need to overcome is the recognition of the ongoing inhabitant activity of daily living (ADL). Moreover, to assist him correctly, we also need to be able to detect the cognitive errors he performs. Therefore, we present in this paper a new affordable activity recognition system, based on passive RFID technology, able to detect errors related to cognitive impairment in morning routines. The entire system relies on an innovative model of elliptical trilateration with several filters, and on an ingenious representation of activities with spatial zones. This system has been implemented and deployed in a real smart home prototype. We also present the promising results of a first experiment conducted on this new activity recognition system with real cases scenarios about morning routines.

References

[1]
U. Nations, World population ageing 2009: United Nations, Dept. of Economic and Social Affairs, Population Division, 2010.
[2]
C. Ramos, J. C. Augusto, and D. Shapiro, "Ambient Intelligence: the Next Step for Artificial Intelligence," IEEE Intelligent Systems, vol. 23, pp. 15--18, 2008.
[3]
B. Bouchard, S. Giroux, and A. Bouzouane, "A keyhole plan recognition model for Alzheimer's patients: First results," Journal of Applied Artificial Intelligence, vol. 21, pp. 623--658, 2007.
[4]
H. A. Kautz, "A formal theory of plan recognition and its implementation," in Reasoning about plans, ed: Morgan Kaufmann Publishers Inc., 1991, pp. 69--124.
[5]
D. Patterson, H. Kautz, and D. Fox, "Pervasive computing in the home and community," in Pervasive Computing in Healthcare, ed: CRC Press, 2007, pp. 79--103.
[6]
P. Palmes, H. K. Pung, T. Gu, W. Xue, and S. Chen, "Object relevance weight pattern mining for activity recognition and segmentation," Pervasive Mob. Comput., vol. 6, pp. 43--57, 2010.
[7]
L. Fiore, D. Fehr, R. Bodor, A. Drenner, G. Somasundaram, and N. Papanikolopoulos, "Multi-Camera Human Activity Monitoring," Journal of Intelligent & Robotics Systems, vol. 52, pp. 5--43, 2008.
[8]
V. R. Jakkula and D. J. Cook, "Enhancing Smart Home Algorithms Using Temporal Relations," in Technology and Aging. vol. 21, A. Mihailidis, J. Boger, H. Kautz, and L. Normie, Eds., ed Amsterdam: IOS Press, 2008, pp. 3--10.
[9]
K. Bouchard, B. Bouchard, and A. Bouzouane, "Discovery of Topological Relations for Spatial Activity Recognition," in IEEE Symposium Series on Computational Intelligence, Singapore, 2013.
[10]
J. R. Smith, K. P. Fishkin, B. Jiang, A. Mamishev, M. Philipose, A. D. Rea, S. Roy, and K. Sundara-Rajan, "RFID-based techniques for human-activity detection," Communications of the ACM, vol. 48, pp. 39--44, 2005.
[11]
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.
[12]
B.-S. Choi and J.-J. Lee, "Mobile robot localization in indoor environment using RFID and sonar fusion system," presented at the Proceedings of the IEEE/RSJ international conference on Intelligent robots and systems, St. Louis, MO, USA, 2009.
[13]
A. Milella, D. Di Paola, G. Cicirelli, and T. D'Orazio, "RFID tag bearing estimation for mobile robot localization," in Advanced Robotics, 2009. ICAR 2009. International Conference on, 2009, pp. 1--6.
[14]
A. P. Sample, C. Macomber, J. Liang-Ting, and J. R. Smith, "Optical localization of passive UHF RFID tags with integrated LEDs," in RFID (RFID), 2012 IEEE International Conference on, 2012, pp. 116--123.
[15]
A. Parr, R. Miesen, F. Kirsch, and M. Vossiek, "A novel method for UHF RFID tag tracking based on acceleration data," in RFID (RFID), 2012 IEEE International Conference on, 2012, pp. 110--115.
[16]
C. Hekimian-Williams, B. Grant, and P. Kumar, "Accurate localization of RFID tags using phase difference," 2010 IEEE International Conference on RFID IEEE RFID 2010, pp. 89--96, 2010.
[17]
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.
[18]
Y. Lei, C. Jiannong, Z. Weiping, and T. Shaojie, "A hybrid method for achieving high accuracy and efficiency in object tracking using passive RFID," in Pervasive Computing and Communications (PerCom), 2012 IEEE International Conference on, 2012, pp. 109--115.
[19]
K. Chawla and G. Robins, An RFID-based object localisation framework. Genève, SUISSE: Inderscience Publishers, 2011.
[20]
M. S. Mittelman, S. H. Ferris, E. Shulman, G. Steinberg, and B. Levin, "A family intervention to delay nursing home placement of patients with Alzheimer disease: A randomized controlled trial," Journal of American Medical Association, vol. 276, pp. 1725--1731, 1996.
[21]
L. Chen, C. D. Nugent, and H. Wang, "A Knowledge-Driven Approach to Activity Recognition in Smart Homes," IEEE Trans. on Knowl. and Data Eng., vol. 24, pp. 961--974, 2012.
[22]
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.
[23]
M. Buettner, R. Prasad, M. Philipose, and D. Wetherall, "Recognizing daily activities with RFID-based sensors," presented at the Proceedings of the 11th international conference on Ubiquitous computing, Orlando, Florida, USA, 2009.
[24]
T. L. M. van Kasteren, G. Englebienne, and B. J. A. Krose, "Activity recognition using semi-Markov models on real world smart home datasets," Journal of Ambient Intellence and Smart Environments, vol. 2, pp. 311--325, 2010.
[25]
T. Gu, L. Wang, Z. Wu, X. Tao, and J. Lu, "A Pattern Mining Approach to Sensor-Based Human Activity Recognition," IEEE Trans. on Knowl. and Data Eng., vol. 23, pp. 1359--1372, 2011.
[26]
M. J. Egenhofer and R. D. Franzosa, "Point-Set Topological Spatial Relations," International Journal of Geographical Information Systems, vol. 5, pp. 161--174, 1991.
[27]
M. F. Schwartz, M. Segal, T. Veramonti, M. Ferraro, and L. J. Buxbaum, The Naturalistic Action Test: A standardised assessment for everyday action impairment vol. 12. Hove, ROYAUME-UNI: Psychology Press, 2002.

Cited By

View all
  • (2024)(Re)Defining Smart Home Through an HCI Perspective: A Systematic Review of over Two Decades of Smart Home Conceptualization and ResearchInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2437112(1-24)Online publication date: 13-Dec-2024
  • (2024)A Deep Learning Based System For a Long-term Elderly Behavioral Drift DetectionSN Computer Science10.1007/s42979-024-03207-35:7Online publication date: 27-Sep-2024
  • (2024)A review on devices and learning techniques in domestic intelligent environmentJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-024-04759-115:4(2361-2380)Online publication date: 13-Mar-2024
  • 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. activity recognition
  2. passive RFID
  3. smart home
  4. spatial reasoning

Qualifiers

  • Research-article

Funding Sources

  • Canadian Foundation for Innovation
  • Quebec Research Fund on Nature and Technologies
  • 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)16
  • Downloads (Last 6 weeks)2
Reflects downloads up to 23 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)(Re)Defining Smart Home Through an HCI Perspective: A Systematic Review of over Two Decades of Smart Home Conceptualization and ResearchInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2437112(1-24)Online publication date: 13-Dec-2024
  • (2024)A Deep Learning Based System For a Long-term Elderly Behavioral Drift DetectionSN Computer Science10.1007/s42979-024-03207-35:7Online publication date: 27-Sep-2024
  • (2024)A review on devices and learning techniques in domestic intelligent environmentJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-024-04759-115:4(2361-2380)Online publication date: 13-Mar-2024
  • (2023)A Survey on Technical Challenges of Assistive Robotics for Elder People in Domestic Environments: The ASPiDA ConceptIEEE Transactions on Medical Robotics and Bionics10.1109/TMRB.2023.32613425:2(196-205)Online publication date: May-2023
  • (2022)Biosensors toward behavior detection in diagnosis of alzheimer’s diseaseFrontiers in Bioengineering and Biotechnology10.3389/fbioe.2022.103183310Online publication date: 19-Oct-2022
  • (2022)A Measurement Study of RFID-based Face Recognition2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS)10.1109/MASS56207.2022.00026(140-147)Online publication date: Oct-2022
  • (2020)A Framework for Detecting and Analyzing Behavior Changes of Elderly People over Time Using Learning TechniquesSensors10.3390/s2024711220:24(7112)Online publication date: 11-Dec-2020
  • (2019)Passive RFID Localization in the Internet of ThingsRecent Trends and Advances in Wireless and IoT-enabled Networks10.1007/978-3-319-99966-1_7(73-81)Online publication date: 23-Jan-2019
  • (2018)A Systematic Literature Review on Devices and Systems for Ambient Assisted Living: Solutions and Trends from Different User Perspectives2018 International Conference on eDemocracy & eGovernment (ICEDEG)10.1109/ICEDEG.2018.8372367(59-66)Online publication date: Apr-2018
  • (2018)Action detection fusing multiple Kinects and a WIMUMachine Vision and Applications10.1007/s00138-018-0931-129:5(765-788)Online publication date: 1-Jul-2018
  • 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