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A new illness recognition framework using frequent temporal pattern mining

Published:13 September 2014Publication History

ABSTRACT

Living alone in their own residence, older adults are at-risk for late assessment of physical or cognitive changes due to many factors such as their impression that such changes are simply a normal part of aging or their reluctance to admit to a problem. Sensors networks have emerged in the last decade as a possible solution to older adult health monitoring and early illness recognition. Typical early illness recognition approaches are either concentrated on the detection of a given set of activities such as a fall or walks, or on the detection of anomalies such as too many bathroom visits. In this paper we propose a new illness recognition framework, MFA, based on detecting a missing frequent activity from the daily routine. MFA is implemented using a frequent temporal pattern detection algorithm and demonstrated on a pilot dataset collected in TigerPlace, an aging in place community from Columbia, Missouri.

References

  1. Hayes, TL., et al. An unobtrusive in-home monitoring system for detection of key motor changes preceding cognitive decline. IEEE EMBS, 2004, 2480--2483.Google ScholarGoogle Scholar
  2. Mack, D., et al. A passive and portable system for monitoring heart rate and detecting sleep apnea and arousals: Preliminary validation. D2H2, 2006, 51--54.Google ScholarGoogle Scholar
  3. Li, Y., et al. "Efficient source separation algorithms for acoustic fall detection using a Microsoft Kinect", IEEE TBE, vol. 61, no. 3, 2014, 745--755.Google ScholarGoogle ScholarCross RefCross Ref
  4. Popescu, M., Mahnot, A. Early Illness Recognition in Older Adults Using In-Home Monitoring Sensors and Multiple Instance Learning, Methods Inf. Med., 2012, 359--67.Google ScholarGoogle Scholar
  5. Rantz, MJ., et al. "A Technology and Nursing Collaboration to Help Older Adults Age in Place", Nursing Outlook, vol. 53, no. 1, 2005, 40--45.Google ScholarGoogle ScholarCross RefCross Ref
  6. Keogh, E., et al. Finding surprising patterns in a time series database in linear time and space. SIGKDD, 2002, 550--556. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Kalpakis, et al. Distance measures for effective clustering of ARIMA time-series. IEEE ICDM, 2001, 273--280. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Tompa, M. Finding motifs using random projections. Journal of Comput Biol. 2002, 225--242.Google ScholarGoogle Scholar
  9. Guralink, V., Srivastava, J. Event detection from time series data. ACM SIGKDD, 1999, 33--42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Han, J., Pei, j., et al. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Dicov, 2004, 53--87. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Nazerfard, E., Cook, D. Using Bayesian Networks for Daily Activity Prediction, AAAI, 2013, 32--38.Google ScholarGoogle Scholar
  12. Magnusson, M. Discovering hidden time patterns in behavior: T-pattern and their detection. Behavior Research Methods, 2000, Vol. 32, 93--110.Google ScholarGoogle Scholar
  13. Salah, A., Pauwels, E., et al. T-Pattern Revisited: Mining for Temporal Patterns in Sensor Data. Sensors (Basel), 2010, 7496--7513.Google ScholarGoogle Scholar
  14. Hajihashemi, Z., Popescu, M. An Early Illness Recognition Framework Using a Temporal Smith Waterman algorithm and NLP, AMIA, 2013, 548--557.Google ScholarGoogle Scholar
  15. Knuth, D., et al. Fast pattern matching in strings. SIAM Journal on Computing, 1977, 323--350.Google ScholarGoogle Scholar
  16. Zang H, Parker L, "4-Dimensional Local Spatio-Temporal Features for Human Activity Recognition", Proc. of IEEE International Conference on Intelligent Robots and Systems, San Francisco, CA, 2011.Google ScholarGoogle Scholar

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        cover image ACM Conferences
        UbiComp '14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication
        September 2014
        1409 pages
        ISBN:9781450330473
        DOI:10.1145/2638728

        Copyright © 2014 ACM

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        Publication History

        • Published: 13 September 2014

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