Bite-Weight Estimation Using Commercial Ear Buds

TL;DR

This paper explores estimating food bite weight using only audio signals from commercial ear buds, demonstrating promising accuracy improvements over existing methods with minimal hardware requirements.

Contribution

It introduces a novel approach to estimate bite weight solely from ear bud audio signals and evaluates multiple models on a new dataset.

Findings

Mean absolute error less than 1 g for most food types with food-specific models

Achieved 2.1 g error when training on all food types together

Outperforms existing literature approaches

Abstract

While automatic tracking and measuring of our physical activity is a well established domain, not only in research but also in commercial products and every-day life-style, automatic measurement of eating behavior is significantly more limited. Despite the abundance of methods and algorithms that are available in bibliography, commercial solutions are mostly limited to digital logging applications for smart-phones. One factor that limits the adoption of such solutions is that they usually require specialized hardware or sensors. Based on this, we evaluate the potential for estimating the weight of consumed food (per bite) based only on the audio signal that is captured by commercial ear buds (Samsung Galaxy Buds). Specifically, we examine a combination of features (both audio and non-audio features) and trainable estimators (linear regression, support vector regression, and neural-network based estimators) and evaluate on an in-house dataset of 8 participants and 4 food types. Results indicate good potential for this approach: our best results yield mean absolute error of less than 1 g for 3 out of 4 food types when training food-specific models, and 2.1 g when training on all food types together, both of which improve over an existing literature approach.