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.
Keywords
- Fractal Dimension
- Eating Disorder
- Anorexia Nervosa
- Eating Disorder
- Bulimia Nervosa
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2015 Springer International Publishing Switzerland
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Papapanagiotou, V., Diou, C., Lingchuan, Z., van den Boer, J., Mars, M., Delopoulos, A. (2015). Fractal Nature of Chewing Sounds. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds) New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops. ICIAP 2015. Lecture Notes in Computer Science(), vol 9281. Springer, Cham. https://doi.org/10.1007/978-3-319-23222-5_49
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DOI: https://doi.org/10.1007/978-3-319-23222-5_49
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