DocumentCode :
3724174
Title :
Freedom: Online Activity Recognition via Dictionary-Based Sparse Representation of RFID Sensing Data
Author :
Lina Yao;Quan Z. Sheng;Xue Li;Sen Wang;Tao Gu;Wenjie Ruan;Wan Zou
Author_Institution :
Sch. of Comput. Sci., Univ. of Adelaide, Adelaide, SA, Australia
fYear :
2015
Firstpage :
1087
Lastpage :
1092
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.
Keywords :
"Feature extraction","Dictionaries","Radiofrequency identification","Silicon","Legged locomotion","Correlation","Senior citizens"
Publisher :
ieee
Conference_Titel :
Data Mining (ICDM), 2015 IEEE International Conference on
ISSN :
1550-4786
Type :
conf
DOI :
10.1109/ICDM.2015.102
Filename :
7373440
Link To Document :
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