HIERARCHICAL DYNAMIC MODEL FOR HUMAN DAILY ACTIVITY RECOGNITION

Blanca Florentino-Liaño, Niamh O'Mahony, Antonio Artés-Rodríguez

Abstract

This work deals with the task of human daily activity recognition using miniature inertial sensors. The proposed method is based on the development of a hierarchical dynamic model, incorporating both inter-activity and intra-activity dynamics, thereby exploiting the inherently dynamic nature of the problem to aid the classification task. The method uses raw acceleration and angular velocity signals, directly recorded by inertial sensors, bypassing commonly used feature extraction and selection techniques and, thus, keeping all information regarding the dynamics of the signals. Classification results show a competitive performance compared to state-of-the-art methods.

References

  1. Altun, K. and Barshan, B. (2010). Human activity recognition using inertial/magnetic sensor units. In Proceedings of the First International Conference on Human Behavior Understanding, HBU'10, pages 38-51, Berlin, Heidelberg. Springer-Verlag.
  2. Bao, L. and Intille, S. S. (2004). Activity Recognition from User-Annotated Acceleration Data. Pervasive Computing, pages 1-17.
  3. Frank, K., Nadales, M. J. V., Robertson, P., and Angermann, M. (2010). Reliable real-time recognition of motion related human activities using MEMS inertial sensors. In ION GNSS 2010.
  4. Han, C. W., Kang, S. J., and Kim, N. S. (2010). Implementation of HMM-based human activity recognition using single triaxial accelerometers. IEICE Transactions, 93-A:1379-1383.
  5. He, Z. Y. and Jin, L. W. (2008). Activity recognition from acceleration data using AR model representation and SVM. Machine Learning and Cybernetics.
  6. Khan, A. M., Lee, Y.-K., Lee, S. Y., and Kim, T.- S. (2010). A triaxial accelerometer-based physicalactivity recognition via augmented-signal features and a hierarchical recognizer. IEEE Transactions on Information Technology in Biomedicine, 14:1166-1172.
  7. Krishnan, N. C., Colbry, D., Juillard, C., and Panchanathan, S. (2008). Real time human activity recognition using tri-axial accelerometers. In Sensors Signals and Information Processing Workshop (SENSIP).
  8. Moeslund, T. B., Hilton, A., and Krüger, V. (2006). A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding, 104:90-126.
  9. Oliver, N., Horvitz, E., and Garg, A. (2002). Layered representations for human activity recognition. In Proceedings of the 4th IEEE International Conference on Multimodal Interfaces, ICMI 7802, pages 3-, Washington, DC, USA. IEEE Computer Society.
  10. Powell, H., Hanson, M., and Lach, J. (2007). A wearable inertial sensing technology for clinical assessment of tremor. In IEEE Biomedical Circuits and Systems Conference, BIOCAS 2007., pages 9 -12.
  11. Preece, S. J., Goulermas, J. Y., Kenney, L. P. J., and Howard, D. (2009). A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Transactions on Biomedical Engineering, 56(3):871-879.
  12. Rabiner, L. and Juang, B.-H. (1993). Fundamentals of Speech Recognition. Prentice Hall, united states ed edition.
  13. Rabiner, L. R. (1990). Readings in speech recognition. In Waibel, A. and Lee, K.-F., editors, Readings in speech recognition, chapter A tutorial on hidden Markov models and selected applications in speech recognition, pages 267-296. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
  14. Sabatini, A., Martelloni, C., Scapellato, S., and Cavallo, F. (2005). Assessment of walking features from foot inertial sensing. IEEE Transactions on Biomedical Engineering, 52(3):486 -494.
  15. Viterbi, A. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, 13(2):260 - 269.
  16. Winters, J. and Wang, Y. (2003). Wearable sensors and telerehabilitation. IEEE Engineering in Medicine and Biology Magazine, 22(3):56 -65.
  17. Wu, G. and Xue, S. (2008). Portable preimpact fall detector with inertial sensors. IEEE Transactions on Neural Systems and Rehabilitation Engineering [see also IEEE Trans. on Rehabilitation Engineering], 16(2):178-183.
  18. Zhu, C. and Sheng, W. (2010). Recognizing human daily activity using a single inertial sensor. In Proceedings of the 8th World Congress on Intelligent Control and Automation (WCICA), pages 282 -287.
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Paper Citation


in Harvard Style

Florentino-Liaño B., O'Mahony N. and Artés-Rodríguez A. (2012). HIERARCHICAL DYNAMIC MODEL FOR HUMAN DAILY ACTIVITY RECOGNITION . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012) ISBN 978-989-8425-89-8, pages 61-68. DOI: 10.5220/0003781900610068


in Bibtex Style

@conference{biosignals12,
author={Blanca Florentino-Liaño and Niamh O'Mahony and Antonio Artés-Rodríguez},
title={HIERARCHICAL DYNAMIC MODEL FOR HUMAN DAILY ACTIVITY RECOGNITION},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)},
year={2012},
pages={61-68},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003781900610068},
isbn={978-989-8425-89-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)
TI - HIERARCHICAL DYNAMIC MODEL FOR HUMAN DAILY ACTIVITY RECOGNITION
SN - 978-989-8425-89-8
AU - Florentino-Liaño B.
AU - O'Mahony N.
AU - Artés-Rodríguez A.
PY - 2012
SP - 61
EP - 68
DO - 10.5220/0003781900610068