Italy


This paper presents chewing data analysis based on the new approach of chewing detection for diet monitoring applications. The proposed approach is based on chewing detection using a proximity sensor in capturing the temporalis muscle movement during chewing. The aim is to support the development of non-contact-based chewing detection. A wearable device of eye-glass is used with the sensor mounted at the right side of the eyeglass temple using a 3D printed housing. The main activity involved in this study is resting and eating, three test food that represents different food hardness (carrot, banana, and apple) with a portion of one spoonful. Several upper cut-off frequencies (fc2) of bandpass filters were used during the analyses. The signals were evaluated using accuracy and F1-score for classification and the absolute mean of error for chewing count estimation. In the classification stage, using the setting of a 10-fold cross-validation method and a 3s segmented window, fc2 of 6Hz gives the highest accuracy with 97.6%, while, 2.5Hz gives the lowest accuracy of 92.6%. However, in the chewing count estimation stage, which is based on a 240s segmented window, 2.4Hz able to give a smaller percentage absolute error of 2.69%, compare to 6Hz with 12.11%. It can be concluded that the chewing frequency was under 2.5Hz, but, the self-reporting labeling approach used in this study reduced the accuracy of the system as fc2 equal to 2.5Hz is used. Further analysis of chewing count shows that might in relating the total chewing count with different food hardness.

ICPR 2020

Analysis of Chewing Signals Based on Chewing Detection Using Proximity Sensor for Diet Monitoring

temporalis muscle

chewing detection

proximity sensor

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https://d3smihljt9218e.cloudfront.net/transcript/9296/subtitle/0a0f4354936c9ab5f87948f380120042.vtt

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Analysis of Chewing Signals Based on Chewing Detection Using Proximity Sensor for Diet Monitoring

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