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

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Freedom: Online activity recognition via dictionary-based sparse representation of RFID sensing data. / Yao, Lina; Sheng, Quan Z.; Li, Xue et al.
Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015. ed. / Zhi-Hua Zhou; Charu Aggarwal; Hui Xiong; Alexander Tuzhilin; Xindong Wu. IEEE, 2016. p. 1087-1092 7373440.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Yao, L, Sheng, QZ, Li, X, Wang, S, Gu, T, Ruan, W & Zou, W 2016, Freedom: Online activity recognition via dictionary-based sparse representation of RFID sensing data. in Z-H Zhou, C Aggarwal, H Xiong, A Tuzhilin & X Wu (eds), Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015., 7373440, IEEE, pp. 1087-1092, 15th IEEE International Conference on Data Mining, ICDM 2015, Atlantic City, United States, 14/11/15. https://doi.org/10.1109/ICDM.2015.102

APA

Yao, L., Sheng, Q. Z., Li, X., Wang, S., Gu, T., Ruan, W., & Zou, W. (2016). Freedom: Online activity recognition via dictionary-based sparse representation of RFID sensing data. In Z-H. Zhou, C. Aggarwal, H. Xiong, A. Tuzhilin, & X. Wu (Eds.), Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015 (pp. 1087-1092). Article 7373440 IEEE. https://doi.org/10.1109/ICDM.2015.102

Vancouver

Yao L, Sheng QZ, Li X, Wang S, Gu T, Ruan W et al. Freedom: Online activity recognition via dictionary-based sparse representation of RFID sensing data. In Zhou Z-H, Aggarwal C, Xiong H, Tuzhilin A, Wu X, editors, Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015. IEEE. 2016. p. 1087-1092. 7373440 doi: 10.1109/ICDM.2015.102

Author

Yao, Lina ; Sheng, Quan Z. ; Li, Xue et al. / Freedom : Online activity recognition via dictionary-based sparse representation of RFID sensing data. Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015. editor / Zhi-Hua Zhou ; Charu Aggarwal ; Hui Xiong ; Alexander Tuzhilin ; Xindong Wu. IEEE, 2016. pp. 1087-1092

Bibtex

@inproceedings{4fd03cf6d05648cab9bc737c03411c64,
title = "Freedom: Online activity recognition via dictionary-based sparse representation of RFID sensing data",
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 = "Activity recognition, Dictionary, Feature selection, RFID, Sensing data, Sparse coding",
author = "Lina Yao and Sheng, {Quan Z.} and Xue Li and Sen Wang and Tao Gu and Wenjie Ruan and Wan Zou",
year = "2016",
month = jan,
day = "5",
doi = "10.1109/ICDM.2015.102",
language = "English",
pages = "1087--1092",
editor = "Zhi-Hua Zhou and Charu Aggarwal and Hui Xiong and Alexander Tuzhilin and Xindong Wu",
booktitle = "Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015",
publisher = "IEEE",
note = "15th IEEE International Conference on Data Mining, ICDM 2015 ; Conference date: 14-11-2015 Through 17-11-2015",

}

RIS

TY - GEN

T1 - Freedom

T2 - 15th IEEE International Conference on Data Mining, ICDM 2015

AU - Yao, Lina

AU - Sheng, Quan Z.

AU - Li, Xue

AU - Wang, Sen

AU - Gu, Tao

AU - Ruan, Wenjie

AU - Zou, Wan

PY - 2016/1/5

Y1 - 2016/1/5

N2 - 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.

AB - 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.

KW - Activity recognition

KW - Dictionary

KW - Feature selection

KW - RFID

KW - Sensing data

KW - Sparse coding

U2 - 10.1109/ICDM.2015.102

DO - 10.1109/ICDM.2015.102

M3 - Conference contribution/Paper

SP - 1087

EP - 1092

BT - Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015

A2 - Zhou, Zhi-Hua

A2 - Aggarwal, Charu

A2 - Xiong, Hui

A2 - Tuzhilin, Alexander

A2 - Wu, Xindong

PB - IEEE

Y2 - 14 November 2015 through 17 November 2015

ER -