DETECTION OF TOOTHBRUSHING ACTIVITY USING FREE-LIVING ACCELERATION DATA

Rüdiger Zillmer

Abstract

The present paper discusses the characterisation of toothbrushing activity, using acceleration data collected for 50 subjects in free-living conditions. The data logging is triggered by super-threshold values of acceleration, which can give rise to false activations by non-brushing activities. Due to large intra and inter individual variations, it is not possible to obtain an exhaustive training-set of all activities that trigger the logging. Thus, a structural analysis of appropriate data features is performed, which reveals a clustering of the data. The comparison with brushing activity traces from laboratory experiments allows the identification of toothbrushing activity, while the remainder corresponds to various false activation events like electronic noise or brush handling. The distribution of the resulting toothbrushing activity shows distinct peaks for morning and night brushing activity.

References

  1. Bao, L. and Intille, S. (2004). Activity Recognition from User-Annotated Acceleration Data, Lecture Notes in Computer Science, volume 3001. Springer, Berlin.
  2. Claessen, J. P., Bates, S., Sherlock, K., Seeparsand, F., and Wright, R. (2008a). Designing interventions to improve tooth brushing. International Dental Journal, 58.
  3. Claessen, J. P., Seeparsand, F., and Wright, R. (2008b). Brushing up on behaviour measurement: Validation study of new technology. PEF-IADR, London.
  4. 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 acceleration data. IEEE Transactions on Biomedical Engineering, 56:871.
  5. Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. (2007). Numerical Recipies 3rd Edition: The Art of Scientific Computing. Cambridge University Press, 3rd edition.
  6. Van Someren, E. J. W., Lazerona, R. H. C., Vonk, B. F. M., Mirmirana, M., and Swaab, D. F. (1996). Gravitational artefact in frequency spectra of movement acceleration: implications for actigraphy in young and elderly subjects. J Neurosci Methods, 65(1):55-62.
  7. Vega-Gonzalez, A., Bain, B. J., Dall, P. M., and Granat, M. H. (2007). Continuous monitoring of upper-limb activity in a free-living environment: a validation study. Medical and Biological Engineering and Computing, 45(10):947-956.
  8. Welk, G. W., McClain, J. J., Eisenmann, J. C., and Wickel, E. E. (2007). Field validation of the mti actigraph and bodymedia armband monitor using the ideea monitor. Obesity, 15(4):918-928.
  9. Zhu, L., Petersen, P. E., and Wang, H. Y. (2005). Oral health knowledge, attitudes and behaviour of adults in china. Int Dent J, 55:231-241.
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Paper Citation


in Harvard Style

Zillmer R. (2010). DETECTION OF TOOTHBRUSHING ACTIVITY USING FREE-LIVING ACCELERATION DATA . In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010) ISBN 978-989-674-018-4, pages 377-380. DOI: 10.5220/0002592403770380


in Bibtex Style

@conference{biosignals10,
author={Rüdiger Zillmer},
title={DETECTION OF TOOTHBRUSHING ACTIVITY USING FREE-LIVING ACCELERATION DATA},
booktitle={Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)},
year={2010},
pages={377-380},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002592403770380},
isbn={978-989-674-018-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)
TI - DETECTION OF TOOTHBRUSHING ACTIVITY USING FREE-LIVING ACCELERATION DATA
SN - 978-989-674-018-4
AU - Zillmer R.
PY - 2010
SP - 377
EP - 380
DO - 10.5220/0002592403770380