Fuzzy classifier based ingestive monitor

  • K. Thirupura Sundari Sri Sairam Engineering College, India
Keywords: EMG electrode, fuzzy classifier, labView

Abstract

The observation of food intake and ingestive behavior remains an open problem that has significant implications in the study and treatment of obesity and eating disorders. A novel method of fusing a sensor and pattern recognition method was developed to detect periods of food intake based on non-invasive monitoring of chewing. A surface-type EMG electrode was used to capture the movement of the lower jaw from volunteers during periods of quiet sitting, and food consumption. These signals were processed to extract the most relevant features, identifying from 4 to 10 features most critical for classifying the type of food consumed. Fuzzy classifiers were trained to create food intake, detection models. The simplicity of the sensor may result in a less intrusive and simpler way to detect food intake. The proposed system is implemented using LabVIEW. The proposed methodology could lead to the development of a wearable sensor system to assess eating behaviors of individuals and also to calculate the quantity of food intake.

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References

Amft, O. (2010, November). A wearable earpad sensor for chewing monitoring. In SENSORS, 2010 IEEE (pp. 222-227). IEEE. https://doi.org/10.1109/ICSENS.2010.5690449

Amft, O., & Troster, G. (2009). On-body sensing solutions for automatic dietary monitoring. IEEE pervasive computing, 8(2), 62-70. https://doi.org/10.1109/MPRV.2009.32

Amft, O., Stäger, M., Lukowicz, P., & Tröster, G. (2005, September). Analysis of chewing sounds for dietary monitoring. In International Conference on Ubiquitous Computing (pp. 56-72). Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551201_4

Beigl, M., Intille, S., Rekimoto, J., & Tokuda, H. (2005). UbiComp 2005: Ubiquitous Computing. Springer Berlin/Heidelberg..

BROWN, W. E. (1994). Method to investigate differences in chewing behaviour in humans: I. Use of electromyography in measuring chewing. Journal of Texture Studies, 25(1), 1-16. https://doi.org/10.1111/j.1745-4603.1994.tb00751.x

Dacremont, C. (1995). Spectral composition of eating sounds generated by crispy, crunchy and crackly foods. Journal of texture studies, 26(1), 27-43. https://doi.org/10.1111/j.1745-4603.1995.tb00782.x

Fontana, J. M., & Sazonov, E. S. (2012, August). A robust classification scheme for detection of food intake through non-invasive monitoring of chewing. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 4891-4894). IEEE.

Fontana, J. M., Farooq, M., & Sazonov, E. (2014). Automatic ingestion monitor: A novel wearable device for monitoring of ingestive behavior. IEEE Transactions on Biomedical Engineering, 61(6), 1772-1779. https://doi.org/10.1109/TBME.2014.2306773

Lee, W. E. (1988). Analysis of food crushing sounds during mastication: frequency-time studies. J. Texture Stud., 19, 27-38.

Marimuthu, G., & Ramesh, G. (2016). On moderate fuzzy analytic hierarchy process pairwise comparison model with sub-criteria. International Research Journal of Engineering, IT & Scientific Research, 2(3), 33-42.

Paßler, S., & Fischer, W. J. (2011, July). Food intake activity detection using a wearable microphone system. In 2011 Seventh International Conference on Intelligent Environments(pp. 298-301). IEEE.

Sazonov, E., Schuckers, S., Lopez-Meyer, P., Makeyev, O., Sazonova, N., Melanson, E. L., & Neuman, M. (2008). Non-invasive monitoring of chewing and swallowing for objective quantification of ingestive behavior. Physiological measurement, 29(5), 525.

Vickers, Z. M. (1985). The relationships of pitch, loudness and eating technique to judgments of the crispness and crunchiness of food sounds 2. Journal of Texture Studies, 16(1), 85-95.

Walker, W. P., & Bhatia, D. K. (2013). Automated ingestion detection for a health monitoring system. IEEE journal of biomedical and health informatics, 18(2), 682-692. https://doi.org/10.1109/JBHI.2013.2279193

Published
2019-07-05
How to Cite
Sundari, K. T. (2019). Fuzzy classifier based ingestive monitor. International Journal of Engineering & Computer Science, 2(1), 12-19. https://doi.org/10.31295/ijecs.v2n1.61
Section
Articles