  With the soaring adoption of in-ear wearables, the research community has
started investigating suitable in-ear heart rate (HR) detection systems. HR is
a key physiological marker of cardiovascular health and physical fitness.
Continuous and reliable HR monitoring with wearable devices has therefore
gained increasing attention in recent years. Existing HR detection systems in
wearables mainly rely on photoplethysmography (PPG) sensors, however, these are
notorious for poor performance in the presence of human motion. In this work,
leveraging the occlusion effect that enhances low-frequency bone-conducted
sounds in the ear canal, we investigate for the first time \textit{in-ear
audio-based motion-resilient} HR monitoring. We first collected HR-induced
sounds in the ear canal leveraging an in-ear microphone under stationary and
three different activities (i.e., walking, running, and speaking). Then, we
devised a novel deep learning based motion artefact (MA) mitigation framework
to denoise the in-ear audio signals, followed by an HR estimation algorithm to
extract HR. With data collected from 20 subjects over four activities, we
demonstrate that hEARt, our end-to-end approach, achieves a mean absolute error
(MAE) of 3.02 $\pm$ 2.97~BPM, 8.12 $\pm$ 6.74~BPM, 11.23 $\pm$ 9.20~BPM and
9.39 $\pm$ 6.97~BPM for stationary, walking, running and speaking,
respectively, opening the door to a new non-invasive and affordable HR
monitoring with usable performance for daily activities. Not only does hEARt
outperform previous in-ear HR monitoring work, but it outperforms reported
in-ear PPG performance.
