International Conference on Image Analysis and Processing

ICIAP 2015: New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops pp 401-408 | Cite as

Fractal Nature of Chewing Sounds

  • Vasileios Papapanagiotou
  • Christos Diou
  • Zhou Lingchuan
  • Janet van den Boer
  • Monica Mars
  • Anastasios Delopoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)

Abstract

In the battle against Obesity as well as Eating Disorders, non-intrusive dietary monitoring has been investigated by many researchers. For this purpose, one of the most promising modalities is the acoustic signal captured by a common microphone placed inside the outer ear canal. Various chewing detection algorithms for this type of signals exist in the literature. In this work, we perform a systematic analysis of the fractal nature of chewing sounds, and find that the Fractal Dimension is substantially different between chewing and talking. This holds even for severely down-sampled versions of the recordings. We derive chewing detectors based on the the fractal dimension of the recorded signals that can clearly discriminate chewing from non-chewing sounds. We experimentally evaluate snacking detection based on the proposed chewing detector, and we compare our approach against well known counterparts. Experimental results on a large dataset of 10 subjects and total recordings duration of more than 8 hours demonstrate the high effectiveness of our method. Furthermore, there exists indication that discrimination between different properties (such as crispness) is possible.

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References

  1. 1.
    Amft, O., Kusserow, M., Troster, G.: Bite weight prediction from acoustic recognition of chewing. IEEE Transactions on Biomedical Engineering 56(6), 1663–1672 (2009)CrossRefGoogle Scholar
  2. 2.
    Amft, O., Stäger, M., Lukowicz, P., Tröster, G.: Analysis of chewing sounds for dietary monitoring. In: Beigl, M., Intille, S.S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 56–72. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  3. 3.
    Gonzalez, R.C., Woods, R.E.: Digital image processing (2002)Google Scholar
  4. 4.
    Mandelbrot, B.B.: The fractal geometry of nature/revised and enlarged ed., 495p., 1. WH Freeman and Co., New York (1983)Google Scholar
  5. 5.
    Maragos, P.: Fractal signal analysis using mathematical morphology. Advances in electronics and electron physics 88, 199–246 (1994)CrossRefGoogle Scholar
  6. 6.
    Maragos, P., Potamianos, A.: Fractal dimensions of speech sounds: Computation and application to automatic speech recognition. The Journal of the Acoustical Society of America 105(3), 1925–1932 (1999)CrossRefGoogle Scholar
  7. 7.
    Maragos, P., Sun, F.-K.: Measuring the fractal dimension of signals: morphological covers and iterative optimization. IEEE Transactions on signal Processing 41(1), 108–121 (1993)CrossRefMATHGoogle Scholar
  8. 8.
    Päßler, S., Fischer, W.-J.: Evaluation of algorithms for chew event detection. In: Proceedings of the 7th International Conference on Body Area Networks, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 20–26 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Vasileios Papapanagiotou
    • 1
  • Christos Diou
    • 1
  • Zhou Lingchuan
    • 2
  • Janet van den Boer
    • 3
  • Monica Mars
    • 3
  • Anastasios Delopoulos
    • 1
  1. 1.Aristotle University of ThessalonikiThessalonikiGreece
  2. 2.CSEM SALandquartSwitzerland
  3. 3.Wagenigen UniversityWagenigenNetherlands

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