skip to main content
10.1145/3004010.3004012acmotherconferencesArticle/Chapter ViewAbstractPublication PagesmobiquitousConference Proceedingsconference-collections
research-article

Activity Recognition and Future Prediction in Hospitals

Authors Info & Claims
Published:28 November 2016Publication History

ABSTRACT

In this paper, we firstly introduce our work [9] where we proposed for recognizing whole day activities using prior knowledge, and applied the method for real nursing sensor dataset we have collected. Then, we introduce the method for predicting the near future of nurses by integrating nurse activity data, location data, and medical records, based on our work [10]. For both works, we independently collected real and open nursing datasets with 2 weeks of accelerometers and training labels from 22 nurses for the former work, and nurse activity, location, medical payment, and nursing need data from 35 nurses and 96 patients for 40 days for the latter work.

References

  1. Bao, L., and Intille, S. S. Activity Recognition from User-Annotated Acceleration Data. In Pervasive Computing (2004), 1--17.Google ScholarGoogle ScholarCross RefCross Ref
  2. Chen, L., Hoey, J., Nugent, C. D., Cook, D. J., and Yu, Z. Sensor-based activity recognition, 2012.Google ScholarGoogle Scholar
  3. Farringdon, J., Moore, A., Tilbury, N., Church, J., and Biemond, P. Wearable sensor badge and sensor jacket for context awareness. Digest of Papers. Third International Symposium on Wearable Computers (1999). Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Fawcett, T. ROC Graphs: Notes and Practical Considerations for Researchers. ReCALL 31 (2004), 1--38.Google ScholarGoogle Scholar
  5. Fawcett, T. An introduction to ROC analysis. Pattern Recognition Letters 27 (2006), 861--874. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Gooch, P., and Roudsari, A. Computerization of workflows, guidelines, and care pathways: a review of implementation challenges for process-oriented health information systems. Journal of the American Medical Informatics Association: JAMIA 18 (2011), 738--48.Google ScholarGoogle ScholarCross RefCross Ref
  7. Guyon, I., and Elisseeff, A. An introduction to variable and feature selection. Journal of Machine Learning Research 3 (2003), 1157--1182. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Herdman, T. H. E., and Kamitsuru, S. E. NANDA International nursing diagnoses: definitions and classification 2015-2017, 2014.Google ScholarGoogle Scholar
  9. Inoue, S., Ueda, N., Nohara, Y., and Nakashima, N. Mobile Activity Recognition for a Whole Day: Recognizing Real Nursing Activities with Big Dataset. In ACM Int'l Conf. Pervasive and Ubiquitous Computing (Ubicomp) (Osaka, 2015). Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Inoue S., Isoda T., Shirouzu M., Sugiyama Y., Nohara Y., Nakashima N. Predicting Daily Nursing Load from Nurses' Activity Logs and Patients' Medical Records. In ACM Int'l Conf. Pervasive and Ubiquitous Computing (Ubicomp) Poster (Heidelberg, 2016), 4 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Kelder, T., Conklin, B. R., Evelo, C. T., and Pico, A. R. Finding the right questions: Exploratory pathway analysis to enhance biological discovery in large datasets. PLoS Biology 8 (2010), 11--12.Google ScholarGoogle ScholarCross RefCross Ref
  12. Kim, E., Helal, S., and Cook, D. Human Activity Recognition and Pattern Discovery. Pervasive Computing, IEEE 9 (2010), 48--53. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Kwapisz, J. R., Weiss, G. M., and Moore, S. A. Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter 12 (2010), 74--82. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., and Campbell, A. T. A survey of mobile phone sensing. IEEE Communications Magazine 48 (2010), 140--150. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Lemmens, L., Van Zelm, R., Vanhaecht, K., and Kerkkamp, H. Systematic review: Indicators to evaluate effectiveness of clinical pathways for gastrointestinal surgery, 2008.Google ScholarGoogle Scholar
  16. Lymberopoulos, D., Bamis, A., and Savvides, A. Extracting spatiotemporal human activity patterns in assisted living using a home sensor network, 2011.Google ScholarGoogle Scholar
  17. Mannini, A., and Sabatini, A. M. Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10 (2010), 1154--1175.Google ScholarGoogle ScholarCross RefCross Ref
  18. Matsuda, S. {Case mix based payment and DPC from an international perspective}. Gan to kagaku ryoho. Cancer & chemotherapy 31, 8 (2004), 1152--7.Google ScholarGoogle Scholar
  19. Naya, F., Ohmura, R., Takayanagi, F., Noma, H., and Kogure, K. Workers' Routine Activity Recognition using Body Movements and Location Information. 2006 10th IEEE International Symposium on Wearable Computers (2006), 105--108.Google ScholarGoogle ScholarCross RefCross Ref
  20. Osmani, V., Balasubramaniam, S., and Botvich, D. Human activity recognition in pervasive health-care: Supporting efficient remote collaboration. Journal of Network and Computer Applications 31, 4 (nov 2008), 628--655. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Panella, M., Marchisio, S., and Di Stanislao, F. Reducing clinical variations with clinical pathways: Do pathways work? International Journal for Quality in Health Care 15 (2003), 509--521.Google ScholarGoogle ScholarCross RefCross Ref
  22. Roggen, D., Troster, G., Lukowicz, P., Ferscha, a., Millan, J. D. R., and Chavarriaga, R. Opportunistic human activity and context recognition. Computer 46 (2013), 36--45. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Rotter, T., Kinsman, L., James, E., Machotta, A., Gothe, H., Willis, J., Snow, P., and Kugler, J. Clinical pathways: effects on professional practice, patient outcomes, length of stay and hospital costs. The Cochrane database of systematic reviews (2010), CD006632.Google ScholarGoogle Scholar
  24. Saraiya, P., North, C., and Duca, K. Visualizing biological pathways: requirements analysis, systems evaluation and research agenda. Information Visualization 4 (2005), 191--205. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Tapia, E. M., Intille, S. S., Haskell, W., Larson, K., Wright, J., King, A., and Friedman, R. Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In Proceedings - International Symposium on Wearable Computers, ISWC (2007), 37--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Tentori, M., and Favela, J. Monitoring behavioral patterns in hospitals through activity-aware computing. In Proceedings of the 2nd International Conference on Pervasive Computing Technologies for Healthcare 2008, PervasiveHealth (2008), 173--176.Google ScholarGoogle ScholarCross RefCross Ref
  27. Ward, J. A., Lukowicz, P., Tröster, G., and Starner, T. E. Activity recognition of assembly tasks using body-worn microphones and accelerometers. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (2006), 1553--1566. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Nohara Y., Inoue S., Nakashima N., Ueda N., M. K., and Kitsuregawa, M. Large-scale Sensor Dataset in a Hospital. In International Workshop on Pattern Recognition for Healthcare Analytics (Tsukuba, Japan, 2012), 4 pages.Google ScholarGoogle Scholar
  29. Zhang, M., and Sawchuk, A. Motion primitive-based human activity recognition using a bag-of-features approach. Proceedings of the 2nd ACM SIGHIT ..., 1 (2012), 631. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Zhang, M., and Sawchuk, A. A. A feature selection-based framework for human activity recognition using wearable multimodal sensors. In Int. Conf. Body Area Networks (2011), 92--98. Google ScholarGoogle ScholarDigital LibraryDigital Library
  1. Activity Recognition and Future Prediction in Hospitals

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      MOBIQUITOUS 2016: Adjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services
      November 2016
      280 pages
      ISBN:9781450347594
      DOI:10.1145/3004010

      Copyright © 2016 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 28 November 2016

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

      Acceptance Rates

      Overall Acceptance Rate26of87submissions,30%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader