Human Activity Recognition Using Inertial/Magnetic Sensor Units

  • Kerem Altun
  • Billur Barshan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6219)


This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Daily and sports activities are classified using five sensor units worn by eight subjects on the chest, the arms, and the legs. Each sensor unit comprises a triaxial gyroscope, a triaxial accelerometer, and a triaxial magnetometer. Principal component analysis (PCA) and sequential forward feature selection (SFFS) methods are employed for feature reduction. For a small number of features, SFFS demonstrates better performance and should be preferable especially in real-time applications. The classifiers are validated using different cross-validation techniques. Among the different classifiers we have considered, BDM results in the highest correct classification rate with relatively small computational cost.


inertial sensors magnetometers human activity recognition and classification feature selection and reduction 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aggarwal, J.K., Cai, Q.: Human motion analysis: a review. Comput. Vis. Image Und. 73(3), 428–440 (1999)CrossRefGoogle Scholar
  2. 2.
    Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Comput. Vis. Image Und. 81(3), 231–268 (2001)CrossRefzbMATHGoogle Scholar
  3. 3.
    Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Und. 104(2-3), 90–126 (2006)CrossRefGoogle Scholar
  4. 4.
    Kern, N., Schiele, B., Schmidt, A.: Multi-sensor activity context detection for wearable computing. In: Aarts, E., Collier, R.W., van Loenen, E., de Ruyter, B. (eds.) EUSAI 2003. LNCS, vol. 2875, pp. 220–232. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Zijlstra, W., Aminian, K.: Mobility assessment in older people: new possibilities and challenges. Eur. J. Ageing 4(1), 3–12 (2007)CrossRefGoogle Scholar
  6. 6.
    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. Physiol. Meas. 25(2), R1–R20 (2004)CrossRefGoogle Scholar
  7. 7.
    Sabatini, A.M.: Inertial sensing in biomechanics: a survey of computational techniques bridging motion analysis and personal navigation. In: Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques, pp. 70–100. Idea Group Publishing, USA (2006)Google Scholar
  8. 8.
    Mathie, M.J., Celler, B.G., Lovell, N.H., Coster, A.C.F.: Classification of basic daily movements using a triaxial accelerometer. Med. Biol. Eng. Comput. 42(5), 679–687 (2004)CrossRefGoogle Scholar
  9. 9.
    Lindemann, U., Hock, A., Stuber, M., Keck, W., Becker, C.: Evaluation of a fall detector based on accelerometers: a pilot study. Med. Biol. Eng. Comput. 43(5), 548–551 (2005)CrossRefGoogle Scholar
  10. 10.
    Kangas, M., Konttila, A., Lindgren, P., Winblad, I., Jämsä, T.: Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait Posture 28(2), 285–291 (2008)CrossRefGoogle Scholar
  11. 11.
    Wu, W.H., Bui, A.A.T., Batalin, M.A., Liu, D., Kaiser, W.J.: Incremental diagnosis method for intelligent wearable sensor system. IEEE T. Inf. Technol. B. 11(5), 553–562 (2007)CrossRefGoogle Scholar
  12. 12.
    Jovanov, E., Milenkovic, A., Otto, C., de Groen, P.: A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation. J. Neuroeng. Rehabil. 2(6) (2005)Google Scholar
  13. 13.
    Pärkkä, J., Ermes, M., Korpipää, P., Mäntyjärvi, J., Peltola, J., Korhonen, I.: Activity classification using realistic data from wearable sensors. IEEE T. Inf. Technol. B. 10(1), 119–128 (2006)CrossRefGoogle Scholar
  14. 14.
    Ermes, M., Pärkkä, J., Mäntyjärvi, J., Korhonen, I.: Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE T. Inf. Technol. B. 12(1), 20–26 (2008)CrossRefGoogle Scholar
  15. 15.
    Aylward, R., Paradiso, J.A.: Sensemble: A wireless, compact, multi-user sensor system for interactive dance. In: Proc. Conf. New Interfaces Musical Expression, Paris, France, June 4-8, pp. 134–139 (2006)Google Scholar
  16. 16.
    Shiratori, T., Hodgins, J.K.: Accelerometer-based user interfaces for the control of a physically simulated character. ACM T. Graphic. 27(5) (2008)Google Scholar
  17. 17.
    Aminian, K., Robert, P., Buchser, E.E., Rutschmann, B., Hayoz, D., Depairon, M.: Physical activity monitoring based on accelerometry: validation and comparison with video observation. Med. Biol. Eng. Comput. 37(1), 304–308 (1999)CrossRefGoogle Scholar
  18. 18.
    Roetenberg, D., Slycke, P.J., Veltink, P.H.: Ambulatory position and orientation tracking fusing magnetic and inertial sensing. IEEE T. Bio-med. Eng. 54(5), 883–890 (2007)CrossRefGoogle Scholar
  19. 19.
    Najafi, B., Aminian, K., Paraschiv-Ionescu, A., Loew, F., Büla, C.J., Robert, P.: Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly. IEEE T. Bio-med. Eng. 50(6), 711–723 (2003)CrossRefGoogle Scholar
  20. 20.
    Tao, Y., Hu, H., Zhou, H.: Integration of vision and inertial sensors for 3D arm motion tracking in home-based rehabilitation. Int. J. Robot. Res. 26(6), 607–624 (2007)CrossRefGoogle Scholar
  21. 21.
    Zhu, R., Zhou, Z.: A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package. IEEE T. Neur. Sys. Reh. 12(2), 295–302 (2004)MathSciNetCrossRefGoogle Scholar
  22. 22.
    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
  23. 23.
    Karantonis, D.M., Narayanan, M.R., Mathie, M., Lovell, N.H., Celler, B.G.: Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE T. Inf. Technol. B. 10(1), 156–167 (2006)CrossRefGoogle Scholar
  24. 24.
    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. Physiol. Meas. 27(10), 935–951 (2006)CrossRefGoogle Scholar
  25. 25.
    Xsens Technologies B.V. Enschede, Holland: MTi and MTx User Manual and Technical Documentation (2009),
  26. 26.
    Webb, A.: Statistical Pattern Recognition. John Wiley & Sons, New York (2002)CrossRefzbMATHGoogle Scholar
  27. 27.
    Tunçel, O., Altun, K., Barshan, B.: Classifying human leg motions with uniaxial piezoelectric gyroscopes. Sensors 9(11), 8508–8546 (2009)CrossRefGoogle Scholar
  28. 28.
    Altun, K., Barshan, B., Tunçel, O.: Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recogn. 43(10), 3605–3620 (2010), doi:10.1016/j.patcog.2010.04.019CrossRefzbMATHGoogle Scholar
  29. 29.
    Preece, S.J., Goulermas, J.Y., Kenney, L.P.J., Howard, D., Meijer, K., Crompton, R.: Activity identification using body-mounted sensors—a review of classification techniques. Physiol. Meas. 30(4), R1–R33 (2009)Google Scholar
  30. 30.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at
  31. 31.
    Preece, S.J., Goulermas, J.Y., Kenney, L.P.J., Howard, D.: A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE T. Bio-med. Eng. 56(3), 871–879 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kerem Altun
    • 1
  • Billur Barshan
    • 1
  1. 1.Department of Electrical and Electronics EngineeringBilkent UniversityAnkaraTurkey

Personalised recommendations