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Today dietary assessments require manual information sampling in paper or electronic questionnaires on food type and other diet-related details. Low accuracies of 50% are confirmed for self-reporting, which weakens diet coaching effectiveness and is a major limitation for today's diet programs. Automatic Dietary Monitoring (ADM) using ubiquitous sensors was proposed to alleviate this problem. In this work, we present implementation and analysis results of a novel acoustic earpad sensor device to capture air-conducted vibrations of food chewing. In contrast to previous works, our new device reduces ear occlusion compared to laboratory setups by using wearable earpad headphones. We investigate the sensing principle, perform a spectral sound analysis, and compare food classification performance to a classic lab-based sensor setup. We present novel food texture clustering results for 19 foods, spurring the understanding of food texture structure. In addition, we detail findings of a recent exhibition installation, were 375 food samples were analysed using the new sensor prototype.
Oliver Amft (Mon,) studied this question.