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

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  • Lina Yao
  • Quan Z. Sheng
  • Xue Li
  • Sen Wang
  • Tao Gu
  • Wenjie Ruan
  • Wan Zou
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Publication date5/01/2016
Host publicationProceedings - 15th IEEE International Conference on Data Mining, ICDM 2015
EditorsZhi-Hua Zhou, Charu Aggarwal, Hui Xiong, Alexander Tuzhilin, Xindong Wu
PublisherIEEE
Pages1087-1092
Number of pages6
ISBN (Electronic)9781467395045
<mark>Original language</mark>English
Event15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States
Duration: 14/11/201517/11/2015

Conference

Conference15th IEEE International Conference on Data Mining, ICDM 2015
Country/TerritoryUnited States
CityAtlantic City
Period14/11/1517/11/15

Conference

Conference15th IEEE International Conference on Data Mining, ICDM 2015
Country/TerritoryUnited States
CityAtlantic City
Period14/11/1517/11/15

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 livingof the older people. The Freedom system interprets what aperson is doing by leveraging machine learning algorithmsand radio-frequency identification (RFID) technology. To dealwith noisy, streaming, unstable RFID signals, we particularlydevelop a dictionary-based approach that can learn dictionariesfor activities using an unsupervised sparse coding algorithm. Our approach achieves efficient and robust activity recognitionvia a more compact representation of the activities. Extensiveexperiments conducted in a real-life residential environmentdemonstrate that our proposed system offers a good overallperformance (e.g., achieving over 96% accuracy in recognizing23 activities) and has the potential to be further developed tosupport the independent living of elderly people.