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Article

Determination of Chewing Count from Video Recordings Using Discrete Wavelet Decomposition and Low Pass Filtration

Department of Computer Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan
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Author to whom correspondence should be addressed.
Academic Editors: Ayman El-baz, Guruprasad A. Giridharan, Ahmed Shalaby, Ali H. Mahmoud and Mohammed Ghazal
Sensors 2021, 21(20), 6806; https://doi.org/10.3390/s21206806
Received: 5 September 2021 / Revised: 2 October 2021 / Accepted: 7 October 2021 / Published: 13 October 2021
(This article belongs to the Special Issue Computer Aided Diagnosis Sensors)
Several studies have shown the importance of proper chewing and the effect of chewing speed on the human health in terms of caloric intake and even cognitive functions. This study aims at designing algorithms for determining the chew count from video recordings of subjects consuming food items. A novel algorithm based on image and signal processing techniques has been developed to continuously capture the area of interest from the video clips, determine facial landmarks, generate the chewing signal, and process the signal with two methods: low pass filter, and discrete wavelet decomposition. Peak detection was used to determine the chew count from the output of the processed chewing signal. The system was tested using recordings from 100 subjects at three different chewing speeds (i.e., slow, normal, and fast) without any constraints on gender, skin color, facial hair, or ambience. The low pass filter algorithm achieved the best mean absolute percentage error of 6.48%, 7.76%, and 8.38% for the slow, normal, and fast chewing speeds, respectively. The performance was also evaluated using the Bland-Altman plot, which showed that most of the points lie within the lines of agreement. However, the algorithm needs improvement for faster chewing, but it surpasses the performance of the relevant literature. This research provides a reliable and accurate method for determining the chew count. The proposed methods facilitate the study of the chewing behavior in natural settings without any cumbersome hardware that may affect the results. This work can facilitate research into chewing behavior while using smart devices. View Full-Text
Keywords: chewing; smart devices; discrete wavelet decomposition; low pass filter; number of chews chewing; smart devices; discrete wavelet decomposition; low pass filter; number of chews
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MDPI and ACS Style

Alshboul, S.; Fraiwan, M. Determination of Chewing Count from Video Recordings Using Discrete Wavelet Decomposition and Low Pass Filtration. Sensors 2021, 21, 6806. https://doi.org/10.3390/s21206806

AMA Style

Alshboul S, Fraiwan M. Determination of Chewing Count from Video Recordings Using Discrete Wavelet Decomposition and Low Pass Filtration. Sensors. 2021; 21(20):6806. https://doi.org/10.3390/s21206806

Chicago/Turabian Style

Alshboul, Sana, and Mohammad Fraiwan. 2021. "Determination of Chewing Count from Video Recordings Using Discrete Wavelet Decomposition and Low Pass Filtration" Sensors 21, no. 20: 6806. https://doi.org/10.3390/s21206806

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