  Energy Expenditure Estimation (EEE) is vital for maintaining weight, managing
chronic diseases, achieving fitness goals, and improving overall health and
well-being. Gold standard measurements for energy expenditure are expensive and
time-consuming, hence limiting utility and adoption. Prior work has used
wearable sensors for EEE as a workaround. Moreover, earables (ear-worn sensing
devices such as earbuds) have recently emerged as a sub-category of wearables
with unique characteristics (i.e., small form factor, high adoption) and
positioning on the human body (i.e., robust to motion, high stability, facing
thin skin), opening up a novel sensing opportunity. However, earables with
multimodal sensors have rarely been used for EEE, with data collected in
multiple activity types. Further, it is unknown how earable sensors perform
compared to standard wearable sensors worn on other body positions. In this
study, using a publicly available dataset gathered from 17 participants, we
evaluate the EEE performance using multimodal sensors of earable devices to
show that an MAE of 0.5 MET (RMSE = 0.67) can be achieved. Furthermore, we
compare the EEE performance of three commercial wearable devices with the
earable, demonstrating competitive performance of earables
