Multiwindow Fusion for Wearable Activity Recognition

  • Oresti Banos
  • Juan-Manuel Galvez
  • Miguel Damas
  • Alberto Guillen
  • Luis-Javier Herrera
  • Hector Pomares
  • Ignacio Rojas
  • Claudia Villalonga
  • Choong Seon Hong
  • Sungyoung Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9095)

Abstract

The recognition of human activity has been extensively investigated in the last decades. Typically, wearable sensors are used to register body motion signals that are analyzed by following a set of signal processing and machine learning steps to recognize the activity performed by the user. One of the most important steps refers to the signal segmentation, which is mainly performed through windowing approaches. In fact, it has been proved that the choice of window size directly conditions the performance of the recognition system. Thus, instead of limiting to a specific window configuration, this work proposes the use of multiple recognition systems operating on multiple window sizes. The suggested model employs a weighted decision fusion mechanism to fairly leverage the potential yielded by each recognition system based on the target activity set. This novel technique is benchmarked on a well-known activity recognition dataset. The obtained results show a significant improvement in terms of performance with respect to common systems operating on a single window size.

Keywords

Activity recognition Segmentation Windowing Wearable sensors Ensemble methods Data fusion 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alshurafa, N., Xu, W., Liu, J.J., Huang, M.-C., Mortazavi, B., Roberts, C.K., Sarrafzadeh, M.: Designing a robust activity recognition framework for health and exergaming using wearable sensors. IEEE Journal of Biomedical and Health Informatics 18(5), 1636–1646 (2014)CrossRefGoogle Scholar
  2. 2.
    Banos, O., Bilal-Amin, M., Ali-Khan, W., Afzel, M., Ali, T., Kang, B.-H., Lee, S.: Mining minds: an innovative framework for personalized health and wellness support. In: Int. Conf. on Pervasive Computing Technologies for Healthcare (2015)Google Scholar
  3. 3.
    Banos, O., Damas, M., Guillen, A., Herrera, L.-J., Pomares, H., Rojas, I., Villalonga, C.: Multi-sensor fusion based on asymmetric decision weighting for robust activity recognition. Neural Processing Letters, 1–22 (2014)Google Scholar
  4. 4.
    Banos, O., Damas, M., Pomares, H., Prieto, A., Rojas, I.: Daily living activity recognition based on statistical feature quality group selection. Expert Systems with Applications 39(9), 8013–8021 (2012)CrossRefGoogle Scholar
  5. 5.
    Banos, O., Damas, M., Pomares, H., Rojas, F., Delgado-Marquez, B., Valenzuela, O.: Human activity recognition based on a sensor weighting hierarchical classifier. Soft Computing 17, 333–343 (2013)CrossRefGoogle Scholar
  6. 6.
    Banos, O., Damas, M., Pomares, H., Rojas, I.: On the use of sensor fusion to reduce the impact of rotational and additive noise in human activity recognition. Sensors 12(6), 8039–8054 (2012)CrossRefGoogle Scholar
  7. 7.
    Banos, O., Damas, M., Pomares, H., Rojas, I., Toth, M.A., Amft, O.: A benchmark dataset to evaluate sensor displacement in activity recognition. In: Proceedings of the ACM Conference on Ubiquitous Computing, pp. 1026–1035 (2012)Google Scholar
  8. 8.
    Banos, O., Galvez, J.-M., Damas, M., Pomares, H., Rojas, I.: Window size impact in human activity recognition. Sensors 14(4), 6474–6499 (2014)CrossRefGoogle Scholar
  9. 9.
    Banos, O., Toth, M.A., Damas, M., Pomares, H., Rojas, I.: Dealing with the effects of sensor displacement in wearable activity recognition. Sensors 14(6), 9995–10023 (2014)CrossRefGoogle Scholar
  10. 10.
    Bulling, A., Blanke, U., Schiele, B.: A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput. Surv. 46(3), 33:1–33:33 (2014)CrossRefGoogle Scholar
  11. 11.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)MATHCrossRefGoogle Scholar
  12. 12.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience (2000)Google Scholar
  13. 13.
    Figo, D., Diniz, P.C., Ferreira, D.R., Cardoso, J.M.P.: Preprocessing techniques for context recognition from accelerometer data. Personal and Ubiquitous Computing 14(7), 645–662 (2010)CrossRefGoogle Scholar
  14. 14.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. Conference on Knowledge Discovery and Data Mining 12(2), 74–82 (2011)Google Scholar
  15. 15.
    Lam, W., Keung, C.-K., Ling, C.X.: Learning good prototypes for classification using filtering and abstraction of instances. Pattern Recognition 35(7), 1491–1506 (2002)MATHCrossRefGoogle Scholar
  16. 16.
    Laudanski, A., Brouwer, B., Li, Q.: Activity classification in persons with stroke based on frequency features. Medical Engineering & Physics 37(2), 180–186 (2015)CrossRefGoogle Scholar
  17. 17.
    Mannini, A., Intille, S.S., Rosenberger, M., Sabatini, A.M., Haskell, W.: Activity recognition using a single accelerometer placed at the wrist or ankle. Medicine and Science in Sports and Exercise 45(11), 2193–2203 (2013)CrossRefGoogle Scholar
  18. 18.
    Mathie, M.J., Coster, A.C.F., Lovell, N.H., Celler, B.G.: Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological Measurement 25(2), 1–20 (2004)CrossRefGoogle Scholar
  19. 19.
    Maurer, U., Smailagic, A., Siewiorek, D.P., Deisher, M.: Activity recognition and monitoring using multiple sensors on different body positions. In: International Workshop on Wearable and Implantable Body Sensor Networks, pp. 113–116 (2006)Google Scholar
  20. 20.
    Mazilu, S., Blanke, U., Hardegger, M., Tröster, G., Gazit, E., Hausdorff, J.M.: Gaitassist: a daily-life support and training system for parkinson’s disease patients with freezing of gait. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2531–2540 (2014)Google Scholar
  21. 21.
    Pirttikangas, S., Fujinami, K., Nakajima, T.: Feature selection and activity recognition from wearable sensors. In: Youn, H.Y., Kim, M., Morikawa, H. (eds.) UCS 2006. LNCS, vol. 4239, pp. 516–527. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  22. 22.
    Ravi, N., Mysore, P., Littman, M.L.: Activity recognition from accelerometer data. In: Proceedings of the Conference on Innovative Applications of Artificial Intelligence, pp. 1541–1546 (2005)Google Scholar
  23. 23.
    Sama, A., Perez-Lopez, C., Romagosa, J., Rodriguez-Martin, D., Catala, A., Cabestany, J., Perez-Martinez, D.A., Rodriguez-Molinero, A.: Dyskinesia and motor state detection in parkinson’s disease patients with a single movement sensor. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1194–1197 (2012)Google Scholar
  24. 24.
    Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Information Processing & Management 45(4), 427–437 (2009)CrossRefGoogle Scholar
  25. 25.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press (2008)Google Scholar
  26. 26.
    Weiss, G.M., Lockhart, J.W., Pulickal, T.T., McHugh, P.T., Ronan, I.H., Timko, J.L.: Actitracker: a smartphone-based activity recognition system for improving health and well-being. SIGKDD Exploration Newsletter (2014)Google Scholar
  27. 27.
    Zappi, P., Roggen, D., Farella, E., Tröster, G., Benini, L.: Network-level power-performance trade-off in wearable activity recognition: A dynamic sensor selection approach. ACM Trans. Embed. Comput. Syst. 11(3), 68:1–68:30 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Oresti Banos
    • 1
  • Juan-Manuel Galvez
    • 2
  • Miguel Damas
    • 2
  • Alberto Guillen
    • 2
  • Luis-Javier Herrera
    • 2
  • Hector Pomares
    • 2
  • Ignacio Rojas
    • 2
  • Claudia Villalonga
    • 2
  • Choong Seon Hong
    • 1
  • Sungyoung Lee
    • 1
  1. 1.Department of Computer EngineeringKyung Hee UniversityYongin-siKorea
  2. 2.Department of Computer Architecture and Computer TechnologyUniversity of GranadaGranadaSpain

Personalised recommendations