Detection of Chewing Motion Using a Glasses Mounted Accelerometer Towards Monitoring of Food Intake Events in the Elderly

  • Gert Mertes
  • Hans Hallez
  • Tom Croonenborghs
  • Bart Vanrumste
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 64)


A novel way to detect food intake events using a wearable accelerometer is presented in this paper. The accelerometer is mounted on wearable glasses and used to capture the movements of the head. During meals, a person’s chewing motion is clearly visible in the time domain of the captured accelerometer signal. Features are extracted from this signal and a forward feature selection algorithm is used to determine the optimal set of features. Support Vector Machine and Random Forest classifiers are then used to automatically classify between epochs of chewing and non-chewing. Data was collected from 5 volunteers. The Support Vector Machine approach with linear kernel performs best with a detection accuracy of 73.98% \(\pm\) 3.99.



This work was funded by internal KU Leuven grant IMP/14/038 with support from COST Action IC1303: AAPELE.

Conflict of Interest

The authors declare that they have no conflict of interest.


  1. 1.
    L. Donini, P. Scardella, L. Piombo, B. Neri, R. Asprino, A. Proietti, S. Carcaterra, E. Cava, S. Cataldi, D. Cucinotta, G. Di Bella, M. Barbagallo, and A. Morrone, “Malnutrition in elderly: Social and economic determinants,” The Journal of Nutrition, Health & Aging, vol. 17, pp. 9–15, 2013.CrossRefGoogle Scholar
  2. 2.
    Nutricia, “Results of the NutriAction II study,” 2013.Google Scholar
  3. 3.
    L. Donini, C. Savina, M. Piredda, D. Cucinotta, A. Fiorito, E. Inelmen, G. Sergi, L. Dominguez, M. Barbagallo, and C. Cannella, “Senile anorexia in acute-ward and rehabilitation settings,” The Journal of Nutrition Health and Aging, vol. 12, no. 8, pp. 511–517, 2008.CrossRefGoogle Scholar
  4. 4.
    R. DiMaria-Ghalili and E. Amella, “Nutrition in older adults: Intervention and assessment can help curb the growing threat of malnutrition.” American Journal of Nursing, vol. 105, pp. 40–50, 2005.CrossRefGoogle Scholar
  5. 5.
    E. Cereda, C. Pedrolli, A. Zagami, A. Vanotti, S. Piffer, A. Opizzi, M. Rondanelli, and R. Caccialanza, “Nutritional screening and mortality in newly institutionalised elderly: a comparison between the geriatric nutritional risk index and the mini nutritional assessment,” Clinical Nutrition, vol. 30, no. 6, pp. 793–798, 2011.CrossRefGoogle Scholar
  6. 6.
    D. Volkert, L. Pauly, P. Stehle, and C. C. Sieber, “Prevalence of malnutrition in orally and tube-fed elderly nursing home residents in Germany and its relation to health complaints and dietary intake,” Gastroenterology research and practice, 2011.Google Scholar
  7. 7.
    H. Lochs, C. Pichard, and S. Allison, “Evidence supports nutritional support,” Clinical Nutrition, vol. 25, no. 2, pp. 177–179, 2006.CrossRefGoogle Scholar
  8. 8.
    L. Burke, M. Warziski, T. Starrett, J. Choo, E. Music, S. Sereika, S. Stark, and M. Sevick, “Self-monitoring dietary intake: Current and future practices,” Journal of Renal Nutrition, pp. 281–290, 2005.CrossRefGoogle Scholar
  9. 9.
    J.-M. Wu, H.-J. Yu, T.-W. Ho, X.-Y. Su, M.-T. Lin, and F. Lai, “Tablet pc-enabled application intervention for patients with gastric cancer undergoing gastrectomy,” Computer methods and programs in biomedicine, vol. 119, no. 2, pp. 101–109, 2015.CrossRefGoogle Scholar
  10. 10.
    O. Bouillanne, G. Morineau, C. Dupont, I. Coulombel, J.-P. Vincent, I. Nicolis, S. Benazeth, L. Cynober, and C. Aussel, “Geriatric nutritional risk index: a new index for evaluating at-risk elderly medical patients,” The American journal of clinical nutrition, vol. 82, no. 4, pp. 777–783, 2005.CrossRefGoogle Scholar
  11. 11.
    P. A. Parmelee, P. D. Thuras, I. R. Katz, and M. P. Lawton, “Validation of the cumulative illness rating scale in a geriatric residential population.” Journal of the American Geriatrics Society, 1995.Google Scholar
  12. 12.
    G. Mertes, G. Baldewijns, P.-J. Dingenen, T. Croonenborghs, and B. Vanrumste, “Automatic fall risk estimation using the nintendo wii balance board,” in Biomedical Engineering Systems and Technologies, 2015.Google Scholar
  13. 13.
    E. Sazonov and J. Fontana, “A sensor system for automatic detection of food intake through non-invasive monitoring of chewing,” IEEE Journal of Sensors, vol. 12, pp. 1340–1348, 2012.CrossRefGoogle Scholar
  14. 14.
    J. Fontana, M. Farooq, and E. Sazonov, “Automatic ingestion monitor: A novel wearable device for monitoring of ingestive behavior,” IEEE Transactions on Biomedical Engineering, pp. 1772–1779, 2014.CrossRefGoogle Scholar
  15. 15.
    M. Puri, Z. Zhiwei, Y. Qian, A. Divakaran, and H. Sawhney, “Recognition and volume estimation of food intake using a mobile device,” in Workshop on Applications of Computer Vision, 2009.Google Scholar
  16. 16.
    S. Passler and W.-J. Fischer, “Food intake activity detection using a wearable microphone system,” in Intelligent Environments (IE), 2011 7th International Conference on. IEEE, 2011, pp. 298–301.Google Scholar
  17. 17.
    O. Amft, “A wearable earpad sensor for chewing monitoring,” in Sensors, 2010 IEEE. IEEE, 2010, pp. 222–227.Google Scholar
  18. 18.
    N. Z. Hamilton, “Correlation-based feature subset selection for machine learning,” 1998.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Gert Mertes
    • 1
    • 2
    • 3
  • Hans Hallez
    • 4
    • 5
  • Tom Croonenborghs
    • 1
    • 5
  • Bart Vanrumste
    • 1
    • 2
    • 3
  1. 1.KU Leuven, Technology Campus Geel, AdvISeGeelBelgium
  2. 2.KU Leuven, ESAT-STADIUSLeuvenBelgium
  3. 3.iMinds Medical Information Technology DepartmentLeuvenBelgium
  4. 4.KU Leuven, Technology Campus Oostende, ReMILeuvenBelgium
  5. 5.Department of Computer ScienceKU LeuvenLeuvenBelgium

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