Skip to main content
View expanded cover

International Conference on Machine Learning, Optimization, and Data Science

LOD 2021: Machine Learning, Optimization, and Data Science pp 553–567Cite as

Estimating Change Intensity and Duration in Human Activity Recognition Using Martingales

Part of the Lecture Notes in Computer Science book series (LNISA,volume 13163)

Abstract

The subject of physical activity is becoming prominent in the healthcare system for the improvement and monitoring of movement disabilities. Existing algorithms are effective in detecting changes in data streams but most of these approaches are not focused on measuring the change intensity and duration. In this paper, we improve on the geometric moving average martingale method by optimising the parameters in the weighted average using a genetic algorithm. The proposed approach enables us to estimate the intensity and duration of transitions that happen in human activity recognition scenarios. Results show that the proposed method makes some improvement over previous martingale techniques.

Keywords

  • Martingales
  • Change detection
  • Human activity recognition

Supported by Ulster University.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-95467-3_40
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-95467-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.

References

  1. Akosa, J.: Predictive accuracy: a misleading performance measure for highly imbalanced data. In: Proceedings of the SAS Global Forum, vol. 12 (2017)

    Google Scholar 

  2. Brawley, L.R., Rejeski, W.J., King, A.C.: Promoting physical activity for older adults: the challenges for changing behavior. Am. J. Prev. Med. 25(3), 172–183 (2003)

    CrossRef  Google Scholar 

  3. Etumusei, J., Martinez Carracedo, J., McClean, S.: Novel martingale approaches for change point detection. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds.) ISDA 2020. AISC, vol. 1351, pp. 457–467. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-71187-0_42

    CrossRef  Google Scholar 

  4. Ho, S.S., Wechsler, H.: A martingale framework for detecting changes in data streams by testing exchangeability. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2113–2127 (2010)

    CrossRef  Google Scholar 

  5. Hosseini, S.S., Noorossana, R.: Performance evaluation of ewma and cusum control charts to detect anomalies in social networks using average and standard deviation of degree measures. Qual. Reliab. Eng. Int. 34(4), 477–500 (2018)

    CrossRef  Google Scholar 

  6. Isaac, E.: Test of hypothesis-concise formula summary

    Google Scholar 

  7. Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)

    CrossRef  Google Scholar 

  8. Khan, N., McClean, S., Zhang, S., Nugent, C.: Optimal parameter exploration for online change-point detection in activity monitoring using genetic algorithms. Sensors 16(11), 1784 (2016)

    CrossRef  Google Scholar 

  9. Kong, X., Bi, Y., Glass, D.H.: Detecting seismic anomalies in outgoing long-wave radiation data. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 8(2), 649–660 (2014)

    CrossRef  Google Scholar 

  10. Kong, X.Z., Bi, Y.X., Glass, D.H.: A Geometric Moving Average Martingale method for detecting changes in data streams. In: Bramer, M., Petridis, M. (eds.) SGAI 2012, pp. 79–92. Springer, London (2012). https://doi.org/10.1007/978-1-4471-4739-8_6

    CrossRef  Google Scholar 

  11. Lampinen, P., Heikkinen, R.L., Ruoppila, I.: Changes in intensity of physical exercise as predictors of depressive symptoms among older adults: an eight-year follow-up. Prev. Med. 30(5), 371–380 (2000)

    CrossRef  Google Scholar 

  12. Leys, C., Ley, C., Klein, O., Bernard, P., Licata, L.: Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49(4), 764–766 (2013)

    CrossRef  Google Scholar 

  13. Lu, Y., Cheung, Y.M., Tang, Y.Y.: Goboost: G-mean optimized boosting framework for class imbalance learning. In: 2016 12th World Congress on Intelligent Control and Automation (WCICA), pp. 3149–3154. IEEE (2016)

    Google Scholar 

  14. Miles, L.: Physical activity and health. Nutr. Bull. 32(4), 314–363 (2007)

    MathSciNet  CrossRef  Google Scholar 

  15. Mirjalili, S.: Genetic algorithm. In: Evolutionary Algorithms and Neural Networks. SCI, vol. 780, pp. 43–55. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-93025-1_4

    CrossRef  MATH  Google Scholar 

  16. Nau, R.: Arima models for time series forecasting (2014)

    Google Scholar 

  17. Nguyen, G.H., Bouzerdoum, A., Phung, S.L.: Learning pattern classification tasks with imbalanced data sets. Pattern Recogn. 36, 193–208 (2009)

    Google Scholar 

  18. Roy, B.: The optimisation problem formulation: criticism and overstepping. J. Oper. Res. Soc. 32(6), 427–436 (1981)

    CrossRef  Google Scholar 

  19. Stokes, M.: Physical management in neurological rehabilitation. Elsevier Health Sciences (2004)

    Google Scholar 

  20. Sugawara, J., Otsuki, T., Tanabe, T., Hayashi, K., Maeda, S., Matsuda, M.: Physical activity duration, intensity, and arterial stiffening in postmenopausal women. Am. J. Hypertens. 19(10), 1032–1036 (2006)

    CrossRef  Google Scholar 

  21. Wang, L., Gu, T., Tao, X., Lu, J.: Sensor-based human activity recognition in a multi-user scenario. In: Tscheligi, M., de Ruyter, B., Markopoulus, P., Wichert, R., Mirlacher, T., Meschterjakov, A., Reitberger, W. (eds.) AmI 2009. LNCS, vol. 5859, pp. 78–87. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-05408-2_10

    CrossRef  Google Scholar 

  22. Xuan, X., Lo, D., Xia, X., Tian, Y.: Evaluating defect prediction approaches using a massive set of metrics: an empirical study. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp. 1644–1647 (2015)

    Google Scholar 

  23. Zhang, S., Galway, L., McClean, S., Scotney, B., Finlay, D., Nugent, C.D.: Deriving relationships between physiological change and activities of daily living using wearable sensors. In: Par, G., Morrow, P. (eds.) S-CUBE 2010. LNICST, vol. 57, pp. 235–250. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23583-2_17

    CrossRef  Google Scholar 

Download references

Acknowledgements

This work was supported by a University of Ulster Vice-Chancellor’s Research Studentship. The authors would like to thank anonymous reviewers for their constructive suggestions.

Author information

Authors and Affiliations

  1. Ulster University, Shore Rd, Newtownabbey, Jordanstown, BT37 0QB, UK

    Jonathan Etumusei, Jorge Martinez Carracedo & Sally McClean

Authors
  1. Jonathan Etumusei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jorge Martinez Carracedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sally McClean
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jonathan Etumusei .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Prof. Giuseppe Nicosia

  2. Department of Computer Science, University of Reading, Reading, UK

    Varun Ojha

  3. Department of Computer Science, University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. Cambridge Judge Business School, University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Giorgio Jansen

  6. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Prof. Panos M. Pardalos

  7. University of Catania, Catania, Italy

    Prof. Giovanni Giuffrida

  8. Department of Informatics, Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Etumusei, J., Carracedo, J.M., McClean, S. (2022). Estimating Change Intensity and Duration in Human Activity Recognition Using Martingales. In: , et al. Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science(), vol 13163. Springer, Cham. https://doi.org/10.1007/978-3-030-95467-3_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-95467-3_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95466-6

  • Online ISBN: 978-3-030-95467-3

  • eBook Packages: Computer ScienceComputer Science (R0)