Abstract
Diet serves as the primary source of calorie intake for human beings, and maintaining a regular dietary intake is crucial for overall health. The pace or speed of chewing can significantly impact the body's response to food consumption. Traditionally, dietary monitoring has relied on manual assessment by clinicians, a process that is labor-intensive, time-consuming, and susceptible to inaccuracies. In this study, we introduce a novel image processing-based approach for quantitatively evaluating chewing and swallowing capabilities. In this method, facial recognition is employed to detect and calibrate facial features using the Dlib facial landmark model. This enables the precise identification of the mandible's position, facilitating the capture of the subject's chewing movements. Subsequently, signal processing techniques are applied to calculate the number of chewing instances. Experiments was conducted with five subjects of diverse genders and ages. The results indicated a mean absolute error of 6.48% in chewing count calculation. The proposed method offers the advantages of convenience and minimal error in comparison to similar studies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Carbo, A.I., Brown, M., Nakrour, N.: Fluoroscopic swallowing examination: radiologic findings and analysis of their causes and pathophysiologic mechanisms. Radiographics 41(6), 1733–1749 (2021)
Mrzezo. Mechanics of Mandibular Movement. https://pocketdentistry.com/4-mechanics-of-mandibular-movement/
Nishimura, J., Kuroda, T.: Eating habits monitoring using wireless wearable in-ear microphone. In: 2008 3rd International Symposium on Wireless Pervasive Computing, pp. 130–132. IEEE (2008)
Farooq, M., Sazonov, E.: Automatic measurement of chew count and chewing rate during food intake. Electronics 5(4), 62 (2016)
Dlib. http://dlib.net/
O'Shea, K., Nash, R.: An introduction to convolutional neural networks, arXiv preprint arXiv:1511.08458, (2015)
Rosebrock, A.: Facial landmarks with dlib, OpenCV, and Python. https://pyimagesearch.com/2017/04/03/facial-landmarks-dlib-opencv-python/
Acknowledgement
The authors would like to thank the National Science Council in Taiwan R.O.C for supporting this research, which is part of the project numbered MOST 109–2221-E-992 -073 -MY3, NSTC 112–2622-8–992-009 -TD1 and NSTC 112–2221-E-992 -057 -MY3.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tsai, CZ., Lo, CC., Guo, LY., Shieh, CS., Horng, MF. (2024). Chewing Behavior Detection Based on Facial Dynamic Features. In: Pan, JS., Pan, Z., Hu, P., Lin, J.CW. (eds) Genetic and Evolutionary Computing. ICGEC 2023. Lecture Notes in Electrical Engineering, vol 1114. Springer, Singapore. https://doi.org/10.1007/978-981-99-9412-0_37
Download citation
DOI: https://doi.org/10.1007/978-981-99-9412-0_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9411-3
Online ISBN: 978-981-99-9412-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)