Department of Computer Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan.
Sensors (Basel). 2021 Oct 13;21(20):6806. doi: 10.3390/s21206806.
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.
已有多项研究表明，咀嚼方式的正确性以及咀嚼速度对人体健康的重要性，包括热量摄入甚至认知功能。本研究旨在设计一种从进食过程的视频记录中计算咀嚼次数的算法。我们开发了一种基于图像处理和信号处理技术的新型算法，用于从视频片段中连续捕捉感兴趣区域，确定面部地标，生成咀嚼信号，并使用两种方法（低通滤波器和离散小波分解）处理信号：峰值检测用于从处理后的咀嚼信号输出中确定咀嚼次数。该系统在没有对性别、肤色、胡须或环境进行任何限制的情况下，对 100 名受试者在三种不同咀嚼速度（即慢、中、快）下的录像进行了测试。低通滤波器算法在慢、中、快三种咀嚼速度下的平均绝对百分比误差分别为 6.48%、7.76%和 8.38%。我们还使用 Bland-Altman 图对性能进行了评估，结果表明大多数点都落在一致性线内。然而，该算法在快速咀嚼时仍需改进，但它超越了相关文献的性能。本研究为确定咀嚼次数提供了一种可靠、准确的方法。所提出的方法有利于在自然环境中研究咀嚼行为，而无需使用可能影响结果的繁琐硬件。这项工作可以促进在使用智能设备时对咀嚼行为的研究。
