Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 5/01/2016 |
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Host publication | Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015 |
Editors | Zhi-Hua Zhou, Charu Aggarwal, Hui Xiong, Alexander Tuzhilin, Xindong Wu |
Publisher | IEEE |
Pages | 1087-1092 |
Number of pages | 6 |
ISBN (electronic) | 9781467395045 |
<mark>Original language</mark> | English |
Event | 15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States Duration: 14/11/2015 → 17/11/2015 |
Conference | 15th IEEE International Conference on Data Mining, ICDM 2015 |
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Country/Territory | United States |
City | Atlantic City |
Period | 14/11/15 → 17/11/15 |
Conference | 15th IEEE International Conference on Data Mining, ICDM 2015 |
---|---|
Country/Territory | United States |
City | Atlantic City |
Period | 14/11/15 → 17/11/15 |
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.