Skip to main content
SpringerLink
Log in

Human Activity Recognition Using Inertial/Magnetic Sensor Units

  • Conference paper
Human Behavior Understanding (HBU 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6219))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aggarwal, J.K., Cai, Q.: Human motion analysis: a review. Comput. Vis. Image Und. 73(3), 428–440 (1999)

    Article  Google Scholar 

  2. Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Comput. Vis. Image Und. 81(3), 231–268 (2001)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  5. Zijlstra, W., Aminian, K.: Mobility assessment in older people: new possibilities and challenges. Eur. J. Ageing 4(1), 3–12 (2007)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  25. Xsens Technologies B.V. Enschede, Holland: MTi and MTx User Manual and Technical Documentation (2009), http://www.xsens.com

  26. Webb, A.: Statistical Pattern Recognition. John Wiley & Sons, New York (2002)

    Book  MATH  Google Scholar 

  27. Tunçel, O., Altun, K., Barshan, B.: Classifying human leg motions with uniaxial piezoelectric gyroscopes. Sensors 9(11), 8508–8546 (2009)

    Article  Google Scholar 

  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.2010.04.019

    Article  MATH  Google Scholar 

  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. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

  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 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey

    Kerem Altun & Billur Barshan

Authors
  1. Kerem Altun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Billur Barshan
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Informatics Institute, University of Amsterdam, Science Park 107, 1098XG, The Netherlands

    Albert Ali Salah  & Theo Gevers  & 

  2. Dept. of Information Engineering and Computer Science, University of Trento, Via Sommarive, 14, I-38123, POVO, Trento, Italy

    Nicu Sebe

  3. Dept. of Computing Science, University of Glasgow, Sir Alwyn Williams Building, G12 8QQ, Glasgow, UK

    Alessandro Vinciarelli

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Altun, K., Barshan, B. (2010). Human Activity Recognition Using Inertial/Magnetic Sensor Units. In: Salah, A.A., Gevers, T., Sebe, N., Vinciarelli, A. (eds) Human Behavior Understanding. HBU 2010. Lecture Notes in Computer Science, vol 6219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14715-9_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14715-9_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14714-2

  • Online ISBN: 978-3-642-14715-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation