Freedom: online activity recognition via dictionary-based sparse representation of RFID sensing data

Yao, Lina, Sheng, Quan Z., Li, Xue, Wang, Sen, Gu, Tao, Ruan, Wenjie and Zou, Wan (2016). Freedom: online activity recognition via dictionary-based sparse representation of RFID sensing data. In: Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015. 15th IEEE International Conference on Data Mining, ICDM 2015, Atlantic City, United States, (1087-1092). 14-17 November 2015. doi:10.1109/ICDM.2015.102


Author Yao, Lina
Sheng, Quan Z.
Li, Xue
Wang, Sen
Gu, Tao
Ruan, Wenjie
Zou, Wan
Title of paper Freedom: online activity recognition via dictionary-based sparse representation of RFID sensing data
Conference name 15th IEEE International Conference on Data Mining, ICDM 2015
Conference location Atlantic City, United States
Conference dates 14-17 November 2015
Convener IEEE
Proceedings title Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015   Check publisher's open access policy
Journal name 2015 Ieee International Conference On Data Mining (Icdm)   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2016
Year available 2016
Sub-type Fully published paper
DOI 10.1109/ICDM.2015.102
Open Access Status Not Open Access
ISBN 9781467395038
ISSN 1550-4786
Volume 2016-January
Start page 1087
End page 1092
Total pages 6
Collection year 2017
Language eng
Abstract/Summary 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.
Keyword Activity recognition
Dictionary
Feature selection
RFID
Sensing data
Sparse coding
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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