Human Activity Recognition and Feature Selection for Stroke Early Diagnosis

  • José Ramón Villar
  • Silvia González
  • Javier Sedano
  • Camelia Chira
  • José M. Trejo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8073)


Human Activity Recognition (HAR) refers to the techniques for detecting what a subject is currently doing. A wide variety of techniques have been designed and applied in ambient intelligence -related with comfort issues in home automation- and in Ambient Assisted Living (AAL) -related with the health care of elderly people. In this study, we focus on the diagnosing of an illness that requires estimating the activity of the subject. In a previous study, we adapted a well-known HAR technique to use accelerometers in the dominant wrist. This study goes one step further, firstly analyzing the different variables that have been reported in HAR, then evaluating those of higher relevance and finally performing a wrapper feature selection method. The main contribution of this study is the best adaptation of the chosen technique for estimating the current activity of the individual. The obtained results are expected to be included in a specific device for early stroke diagnosing.


Ambient Assisted Living Human Activity Recognition Genetic Fuzzy Finite State Machine Feature Selection Genetic Algorithms 


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  1. 1.
    Adams, H.P., del Zoppo, G., Alberts, M.J., Bhatt, D.L., Brass, L., Furlan, A., Grubb, R.L., Higashida, R.T., Jauch, E.C., Kidwell, C., Lyden, P.D., Morgenstern, L.B., Qureshi, A.I., Rosenwasser, R.H., Scott, P.A., Wijdicks, E.F.: Guidelines for the early management of adults with ischemic stroke. Stroke 38, 1655–1711 (2007)CrossRefGoogle Scholar
  2. 2.
    Adams, R.D.: Principles of Neurology, 6th edn. McGraw Hill (1997)Google Scholar
  3. 3.
    Allen, F.R., Ambikairajah, E., Lovell, N.H., Celler, B.G.: Classification of a known sequence of motions and postures from accelerometry data using adapted gaussian mixture models. Physiological Measurement 27, 935–951 (2006)CrossRefGoogle Scholar
  4. 4.
    Álvarez-Álvarez, A., Triviño, G., Cordón, O.: Body posture recognition by means of a genetic fuzzy finite state machine. In: IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems, GEFS, pp. 60–65 (2011)Google Scholar
  5. 5.
    Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Casillas, J., Cordón, O., del Jesus, M., Herrera, F.: Genetic feature selection in a fuzzy rule-based classification system learning process. Information Sciences 136(1-4), 135–157 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Chen, Y.P., Yang, J.Y., Liou, S.N., Lee, G.Y., Wang, J.S.: Online classifier construction algorithm for human activity detection using a tri-axial accelerometer. Applied Mathematics and Computation 205(2), 849–860 (2008)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Dromerick, A., Khader, S.A.: Medical complications during stroke rehabilitation. Advances in Neurology 92, 409–413 (2003)Google Scholar
  9. 9.
    Duarte, E., Alonso, B., Fernández, M., Fernández, J., Flórez, M., García-Montes, I., Gentil, J., Hernández, L., Juan, F., Palomino, J., Vidal, J., Viosca, E., Aguilar, J., Bernabeu, M., Bori, I., Carrión, F., Déniz, A., Díaz, I., Fernández, E., Forastero, P., Iñigo, V., Junyent, J., Lizarraga, N., de Munaín, L.L., Máñez, I., Miguéns, X., Sánchez, I., Soler, A.: Stroke rehabilitation: Care model. Rehabilitación 44(1), 60–68 (2010)CrossRefGoogle Scholar
  10. 10.
    González, S., Villar, J.R., Sedano, J., Chira, C.: A preliminary study on early diagnosis of illnesses based on activity disturbances. In: Omatu, S., Neves, J., Rodriguez, J.M.C., Paz Santana, J.F., Gonzalez, S.R. (eds.) Distrib. Computing & Artificial Intelligence. AISC, vol. 217, pp. 521–527. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Győrbiro, N., Fábián, Á., Hományi, G.: An activity recognition system for mobile phones. Mobile Networks and Applications 14, 82–91 (2009)CrossRefGoogle Scholar
  12. 12.
    Hogdson, C.: To fast or not to fast. Stroke 38, 2631–2632 (2007)CrossRefGoogle Scholar
  13. 13.
    Hollands, K.: Whole body coordination during turning while walking in stroke survivors. Ph.D. thesis, School of Health and Population Sciences. Ph.D. thesis, University of Birmingham (2010)Google Scholar
  14. 14.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter 12(2), 74–82 (2010)CrossRefGoogle Scholar
  15. 15.
    Murray, M.P., Drought, A.B., Kory, R.C.: Walking patterns of normal men. Journal of Bone and Joint Surgery 46(2), 335–360 (1964)Google Scholar
  16. 16.
    Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Learning 27(8), 1226–1238 (2005)CrossRefGoogle Scholar
  17. 17.
    Villar, J.R., González, S., Sedano, J., Corchado, E., Puigpinós, L., de Ciurana, J.: Meta-heuristic improvements applied for steel sheet incremental cold shaping. Memetic Computing 4(4), 249–261 (2012)CrossRefGoogle Scholar
  18. 18.
    Wang, S., Yang, J., Chen, N., Chen, X., Zhang, Q.: Human activity recognition with user-free accelerometers in the sensor networks. In: Proceedings of the International Conference on Neural Networks and Brain, ICNN&B 2005, vol. 2, pp. 1212–1217. IEEE Conference Publications (2005)Google Scholar
  19. 19.
    Yang, J.Y., Wang, J.S., Chen, Y.P.: Using acceleration measurements for activity recognition: an effective learning algorithm for constructing neural networks. Pattern Recognition Letters 29, 2213–2220 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • José Ramón Villar
    • 1
  • Silvia González
    • 2
  • Javier Sedano
    • 2
  • Camelia Chira
    • 2
  • José M. Trejo
    • 3
  1. 1.University of OviedoGijónSpain
  2. 2.Instituto Tecnológico de Castilla y LeónBurgosSpain
  3. 3.Neurology DepartmentBurgos hospitalBurgosSpain

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