Yao, Lina and Sheng, Quan Z. and Li, Xue and Wang, Sen and Gu, Tao and 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. IEEE, USA, pp. 1087-1092. ISBN 9781467395045
Full text not available from this repository.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.