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Activity recognition using conditional random field

Published: 25 June 2015 Publication History

Abstract

Activity Recognition is an integral component of ubiquitous computing. Recognizing an activity is a challenging task since activities can be concurrent, interleaved or ambiguous and can consist of multiple actors (which would require parallel activity recognition). This paper investigates how the discriminative nature of Conditional Random Fields (CRF) can be exploited to enhance the accuracy of recognizing activities when compared to that achieved using generative models. It aims to apply CRF to recognize complex activities, analyze the model trained by CRF and evaluate the performance of CRF against existing models using Stochastic Gradient Descent (which is suitable for online learning).

References

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Qing-Bin Gao, and Shi-Liang Sun. 2012. "Trajectory-Based Human Activity Recognition Using Hidden Conditional Random Fields". In Proceedings of the 2012 International Conference on Machine Learning and Cybernetics, Xian, 15--17 July, 2012. IEEE Xplore. DOI=10.1109/ICMLC.2012.6359507
[2]
Eunju Kim, Sumi Helal, and Diane Cook. 2010. "Human Activity Recognition and Pattern Discovery". IEEE Pervasive Computing 9, 1 (January 2010), 48--53. DOI=10.1109/MPRV.2010.7.
[3]
Machine Learning: Generative and Discriminative Models. 2010. "Machine Learning: Generative and Discriminative Models". Machine Learning Course, http://www.cedar.buffalo.edu/~srihari/CSE574/Discriminative-Generative.pdf
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Hongqing Fang, Raghavendiran Srinivasan and Diane J. Cook, 2012, "Feature selections for human activity recognition in smart home environments". In proceedings of International Journal of Innovative Computing, Information and Control, May 2012.
[5]
Sutton, Charles and Mccallum, Andrew. 2006. "Introduction to Conditional Random Fields for Relational Learning". Getoor, Lise and Taskar, Ben, eds. Introduction to Statistical Relational Learning, MIT Press.
[6]
Charles Elkan. 2012. "Log Linear Models and Conditional Random Fields". University of San Diego, http://cseweb.ucsd.edu/users/elkan/250Bwinter2012/loglinear- CRFs.pdf.
[7]
Douglas L. Vail, Manuela M. Veloso, and John D. Lafferty.2007. "Feature Selection in Conditional Random Fields for Activity Recognition". In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, 2007. IROS 2007. IEEE Xplore, San Diego, CA, pp 3379--3384, DOI=10.1109/IROS.2007.4399441.
[8]
Douglas L. Vail. 2008. "Conditional Random Fields for Activity Recognition". PhD Thesis, Carnegie Mellon University, United States of America, CMU-CS-08-119, April, 2008.
[9]
"Center for Advanced Studies in Adaptive Systems (CASAS)", Washington State University Smart Home Project, Aruba Dataset, 2010--2011, Tulum Dataset, 2009--2010, Two-R (Summer and Spring) Dataset 2009, Kyoto (ADL Normal and ADL Error Dataset). Available at CASAS, WSU website: http://wsucasas.wordpress.com/datasets/
[10]
Naoaki Okazaki, 2007, "CRFsuite: a fast implementation of Conditional Random Fields (CRFs)", http://www.chokkan.org/software/crfsuite
[11]
Megha Agarwal, 2014, "MSc Thesis on Activity Recognition Using Conditional Random Fields", University Of Bristol, Bristol, United Kingdom

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  • (2024)A deep convolutional attention network based on RGB activity images for smart home activity recognitionSignal, Image and Video Processing10.1007/s11760-024-03473-x18:11(8303-8311)Online publication date: 20-Aug-2024
  • (2022)SoHAM: A Sound-Based Human Activity Monitoring Framework for Home Service RobotsIEEE Transactions on Automation Science and Engineering10.1109/TASE.2021.308140619:3(2369-2383)Online publication date: Jul-2022
  • (2022)Sign language recognition based on spatiotemporal convolutional neural network and attention mechanism2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE)10.1109/ICBAIE56435.2022.9985926(519-522)Online publication date: 15-Jul-2022
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    cover image ACM Other conferences
    iWOAR '15: Proceedings of the 2nd international Workshop on Sensor-based Activity Recognition and Interaction
    June 2015
    112 pages
    ISBN:9781450334549
    DOI:10.1145/2790044
    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]

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    Publication History

    Published: 25 June 2015

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    Author Tags

    1. activities of daily living
    2. conditional random fields
    3. online learning
    4. sensor based activity recognition
    5. supervised learning

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    iWOAR '15 Paper Acceptance Rate 15 of 22 submissions, 68%;
    Overall Acceptance Rate 15 of 22 submissions, 68%

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    Cited By

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    • (2024)A deep convolutional attention network based on RGB activity images for smart home activity recognitionSignal, Image and Video Processing10.1007/s11760-024-03473-x18:11(8303-8311)Online publication date: 20-Aug-2024
    • (2022)SoHAM: A Sound-Based Human Activity Monitoring Framework for Home Service RobotsIEEE Transactions on Automation Science and Engineering10.1109/TASE.2021.308140619:3(2369-2383)Online publication date: Jul-2022
    • (2022)Sign language recognition based on spatiotemporal convolutional neural network and attention mechanism2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE)10.1109/ICBAIE56435.2022.9985926(519-522)Online publication date: 15-Jul-2022
    • (2019)Activity Recognition Using Binary Sensors for Elderly People Living Alone: Scanpath Trend Analysis ApproachIEEE Sensors Journal10.1109/JSEN.2019.291502619:17(7575-7582)Online publication date: 1-Sep-2019
    • (2019)Conditional Random Field Feature Generation of Smart Home Sensor Data using Random Forests2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)10.1109/IMBIOC.2019.8777764(1-4)Online publication date: May-2019
    • (2017)An intelligent well-being monitoring system for residents in extra care homesProceedings of the 1st International Conference on Internet of Things and Machine Learning10.1145/3109761.3109769(1-6)Online publication date: 17-Oct-2017
    • (2016)Mouse Movement and Probabilistic Graphical Models Based E-Learning Activity Recognition Improvement Possibilistic ModelArabian Journal for Science and Engineering10.1007/s13369-016-2025-641:8(2847-2862)Online publication date: 2-Feb-2016

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