Unsupervised discovery of abnormal activity occurrences in multi-dimensional time series, with applications in wearable systems

Proceedings of the 10th SIAM International Conference on Data Mining, SDM 2010

Volumn , Issue , 2010, Pages 641-652

  Vahdatpour, Alireza ^a   Sarrafzadeh, Majid ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EMBEDDED SYSTEMS; TIME SERIES; WEARABLE COMPUTERS;

ACTIVITY DISCOVERIES; ACTIVITY STRUCTURE; EMPIRICAL EVALUATIONS; HUMAN ACTIVITY MONITORING; MULTI-DIMENSIONAL TIME-SERIES DATA; SUPERVISED METHODS; TRAINING DATA SETS; WEARABLE COMPUTING;

DATA MINING;

EID: 79951751125     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972801.56     Document Type: Conference Paper

References (20)

1. Javed Aslam, Katya Pelekhov, and Daniela Rus. A practical clustering algorithm for static and dynamic information organization. In SODA '99: Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms, pages 51-60, Philadelphia, PA, USA, 1999. Society for Industrial and Applied Mathematics.
2. Donald J. Berndt and James Clifford. Using dynamic time warping to find patterns in time series. In AAAI-94: Workshop on Knowledge Discovery in Databases (KDD-94), 1994.
3. Xiao-yun Chen and Yanyan Zhan. Multi-scale anomaly detection algorithm based on infrequent pattern of time series. Journal of Computational and Applied Mathematics, 214(1):227-237, 2008.
4. Bill Chiu, Eamonn Keogh, and Stefano Lonardi. Probabilistic discovery of time series motifs. In KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 493-498, NY, USA, 2003. ACM.
5. Foad Dabiri, Alireza Vahdatpour, Hyduke Noshadi, Hagop Hagopian, and Majid Sarrafzadeh. Ubiquitous personal assistive system for neuropathy. In HealthNet '08: The 2nd International Workshop on Systems and Networking Support for Healthcare and Assisted Living Environments in conjunction with ACM MobiSys, 2008.
6. Amit Kale, Aravind Sundaresan, A. N. Rajagopalan, N. Cuntoor, A. Roychowdhury, and V. Krger. Identification of humans using gait. IEEE Transactions on Image Processing, 13:1163-1173, 2004.
7. Eamonn Keogh, Jessica Lin, and Ada Fu. Hot sax: Efficiently finding the most unusual time series subsequence. In ICDM '05: Proceedings of the Fifth IEEE International Conference on Data Mining, pages 226-233, Washington, DC, USA, 2005. IEEE Computer Society.
8. Eamonn Keogh, Stefano Lonardi, and Bill 'Yuan-chi' Chiu. Finding surprising patterns in a time series database in linear time and space. In KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 550-556, New York, NY, USA, 2002. ACM.
9. Jessica Lin, Eamonn Keogh, Ada Fu, and Helga Van Herle. Approximations to magic: Finding unusual medical time series. In CBMS '05: Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems, pages 329-334, Washington, DC, USA, 2005. IEEE Computer Society.
10. Jessica Lin, Eamonn Keogh, Stefano Lonardi, and Bill Chiu. A symbolic representation of time series, with implications for streaming algorithms. In DMKD '03: Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, pages 2-11, New York, NY, USA, 2003. ACM.
11. Junshui Ma and Simon Perkins. Online novelty detection on temporal sequences. In KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 613-618, New York, NY, USA, 2003. ACM.
12. Heikki Mannila, Hannu Toivonen, and A. Inkeri Verkamo. Discovery of frequent episodes in event sequences. Data Mining Knowledge Discovery, 1(3):259-289, 1997.
13. David Minnen, Thad Starner, Irfan Essa, and Charles Isbell. Discovering characteristic actions from on-body sensor data. In Wearable Computers, 2006 10th IEEE International Symposium on, pages 11-18, Oct. 2006.
14. David Minnen, Thad Starner, Irfan A. Essa, and Charles Lee Isbell Jr. Improving activity discovery with automatic neighborhood estimation. In IJCAI '07: Twenty-first International Joint Conference on Artificial Intelligence, pages 2814-2819, 2007.
15. Abdullah Mueen, Eamonn J. Keogh, Qiang Zhu, Sydney Cash, and M. Brandon Westover. Exact discovery of time series motifs. In SDM '09: 2009 Siam International Conference on Data Mining, pages 473-484, 2009.
16. Yoshiki Tanaka, Kazuhisa Iwamoto, and Kuniaki Uehara. Discovery of time-series motif from multidimensional data based on mdl principle. Mach. Learn., 58(2-3):269-300, 2005.
17. Heng Tang and Stephen Shaoyi Liao. Discovering original motifs with different lengths from time series. Knowledge-Based Systems, 21(7):666-671, 2008.
18. Alireza Vahdatpour, Navid Amini, and Majid Sarrafzadeh. Toward unsupervised activity discovery using multi dimensional motif detection in time series. In IJCAI '09: Twenty-first International Joint Conference on Artificial Intelligence, 2009.
19. Winston Wu, L. Au, B. Jordan, T. Stathopoulos, M. Batalin, W. Kaiser, Alireza Vahdatpour, M. Sarrafzadeh, M. Fang, and J. Chodosh. The smartcane system: an assistive device for geriatrics. In BodyNets '08: The third International Conference on Body Area Networks, pages 1-4, Belgium, 2008. ICST.
20. Dragomir Yankov, Eamonn Keogh, Jose Medina, Bill Chiu, and Victor Zordan. Detecting time series motifs under uniform scaling. In KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 844-853, New York, NY, USA, 2007. ACM.