Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/110041
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Type: Conference paper
Title: Freedom: online activity recognition via dictionary-based sparse representation of RFID sensing data
Author: Yao, L.
Sheng, Q.
Li, X.
Wang, S.
Gu, T.
Ruan, W.
Zou, W.
Citation: Proceedings of the 15th IEEE International Conference on Data Mining, 2016 / Aggarwal, C., Zhou, Z., Tuzhilin, A., Xiong, H., Wu, X. (ed./s), vol.2016-January, pp.1087-1092
Publisher: IEEE
Issue Date: 2016
Series/Report no.: IEEE International Conference on Data Mining
ISBN: 9781467395038
ISSN: 1550-4786
Conference Name: IEEE International Conference on Data Mining (ICDM) (14 Nov 2015 - 17 Nov 2015 : Atlantic City, NJ)
Statement of
Responsibility: 
Lina Yao, Quan Z. Sheng, Xue Li, Sen Wang, Tao Gu, Wenjie Ruan, and Wan Zou
Abstract: Understanding and recognizing the activities performed by people is a fundamental research topic for a wide range of important applications such as fall detection of elderly people. In this paper, we present the technical details behind Freedom, a low-cost, unobtrusive system that supports independent living of the older people. The Freedom system interprets what a person is doing by leveraging machine learning algorithms and radio-frequency identification (RFID) technology. To deal with noisy, streaming, unstable RFID signals, we particularly develop a dictionary-based approach that can learn dictionaries for activities using an unsupervised sparse coding algorithm. Our approach achieves efficient and robust activity recognition via a more compact representation of the activities. Extensive experiments conducted in a real-life residential environment demonstrate that our proposed system offers a good overall performance (e.g., achieving over 96% accuracy in recognizing 23 activities) and has the potential to be further developed to support the independent living of elderly people.
Keywords: Activity recognition; RFID; sparse coding; dictionary; feature selection; sensing data
Rights: © 2015 IEEE
RMID: 0030046943
DOI: 10.1109/ICDM.2015.102
Appears in Collections:Computer Science publications

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