Automated Recognition of Human Movement States using Body Acceleration Signals

Md. Rafiul Hassan, Rezaul K. Begg, Ahsan H. Khandoker, Robert Stokes

Abstract

Automated recognition of human activity states has many advantages, e.g., applications in the smart home environment for the monitoring of physical activity levels, detection of accidental falls in the older adults in the home environment or assessment of the recovery phase of patients living independently at home. In this paper, we describe an accelerometer-based system to recognize three activity states, e.g., steady state gait or walking, sitting and simulated sudden accidental falls. The recorded 3D movement accelerations of the trunk were processed using wavelets, and the features were extracted for recognition of movement states through the use of a fuzzy inference system. The system was trained and tested using 58 different data segments representing the three states. Cross-validation test results indicated an overall recognition accuracy by the machine classifier to be 89.7% with an ROC area of 0.83. The results suggest good potential for the system to be applied for various situations involving activity monitoring as well as gait and posture recognition. Further tests are required using various population groups.

References

  1. Fildes B. 1994. Injuries among older people. Melbourne: Collins Dove.
  2. Fahrenberg, J., Foerster, F., Smeja, M., Muller, W. (1997), ”Assessment of posture and motion by multichannel piezoresistive accelerometer recordings” , Psychophysiology. Vol : 34(5), pp. 607-612.
  3. Mantyjarvi, J., Himberg, J., Seppanen, T. (2001), “Recognizing human motion with multiple acceleration sensors”, Proceedings of IEEE international conference on Systems, Man and Cybernetics, pp. 747-752.
  4. Sekine, M., Tamura, T., Akay, M., Fujimoto, T., Togawa, T., and Fukui, Y. (2002),”Discrimination of Walking Patterns Using Wavelet-Based Fractal Analysis”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol : 10 (3), pp. 188-196.
  5. Cho, S. Y., Park, C.G., and Jee, G.I., (2002),”Measurement system of walking distance using low-cost accelerometers”, Proceedings of the 4th Asian Control Conference, pp. 1799-1803.
  6. M. N. Nyan, Tay, F.E.H., Seah, K.H.W., Sitoh, Y.Y. (2005),”Classification of gait patterns in the time-frequency domain”, Journal of Biomechanics, (In press).
  7. Aminian, K., Robert, P., Jequier, E., and Schutz, Y. (1995), ”Estimation of Speed and incline of walking using neural network”, IEEE Transaction on Instrumentation and Measurement, Vol : 44(3), pp 743-746 .
  8. Hassan, M.R., Begg, R., and Taylor, S. (2005), “Fuzzy Logic-based recognition of gait changes due to trip-related falls”, Proceedings of the 2005 IEEE Annual Conference on Engineering in Medicine and Biology(EMBS'05).
  9. Debnath, L. (2001), Wavelet Transforms & Their Applications, Birkhäuser , Boston ,USA.
  10. Misiti, M., Misiti, Y., Oppenheim, G., Poggi, J-M. (2005), Wavelet Toolbox User's GuideFor use with Matlab, The Mathworks Inc.
  11. Meunier, B. B, Yager, R. R., and Zadeh, L. A. (Eds)(2000), Uncertainty in Intelligent and Information Systems, World Scientific Publishing Company, Singapore.
  12. Pedrycz, W. (1995) , Fuzzy Sets Engineering, CRC Press.
  13. Zadeh, L. A. (1965), Fuzzy Sets, Information and Control, Vol : 8, pp. 338-353.
  14. Chiu. S.L.(1997), Extracting Fuzzy Rules from Data for Function Approximation and Pattern Classification, Chapter 9 in Fuzzy Information Engineering: A guided Tour of Applications (eds) D. Dubois, H. Prade, and R. Yager, John Wiley & Sons.
  15. Morlet, J. , Aerens, G., Fourgeau, E., ans Giard, D. (1982) ,”Wave propagation and sampling theory, Part I: Complex signal land scattering in multilayer media”, Journal of Geophysics, Vol : 47, pp. 203-221.
  16. Abry, P. (1997), Ondelettes et turbulence. Multirésolutions,algorithmes de décomposition, invariance d'échelles, Diderot Editeur, Paris.
Download


Paper Citation


in Harvard Style

Rafiul Hassan M., K. Begg R., H. Khandoker A. and Stokes R. (2006). Automated Recognition of Human Movement States using Body Acceleration Signals . In Proceedings of the 2nd International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2006) ISBN 978-972-8865-67-2, pages 135-143. DOI: 10.5220/0001225601350143


in Bibtex Style

@conference{bpc06,
author={Md. Rafiul Hassan and Rezaul K. Begg and Ahsan H. Khandoker and Robert Stokes},
title={Automated Recognition of Human Movement States using Body Acceleration Signals},
booktitle={Proceedings of the 2nd International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2006)},
year={2006},
pages={135-143},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001225601350143},
isbn={978-972-8865-67-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2006)
TI - Automated Recognition of Human Movement States using Body Acceleration Signals
SN - 978-972-8865-67-2
AU - Rafiul Hassan M.
AU - K. Begg R.
AU - H. Khandoker A.
AU - Stokes R.
PY - 2006
SP - 135
EP - 143
DO - 10.5220/0001225601350143