  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.
