Motion-resilient Heart Rate Monitoring with In-ear Microphones.

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 sound enhancing properties of the occlusion effect, which can be generated by sealing the entrance of the ear canal (something that some existing earphones already do to improve noise cancellation), we investigate for the first time \textit{in-ear audio-based motion-resilient} HR monitoring. This is done by measuring HR-induced sound in the human ear canal with in-ear microphones. Concretely, we develop a novel motion artefact (MA) removal technique based on wavelet transforms, followed by an HR estimation algorithm to extract HR from in-ear audio signals compounded with other activities (e.g., walking, running, and speaking). Unlike existing works, we present a systematic evaluation of our technique under a set of different motion artifacts and while speaking. With data collected from 15 subjects over four activities, we demonstrate that our approach achieves a mean absolute error (MAE) of 0.88$\pm$0.27 BPM, 8.11$\pm$3.89 BPM, 13.79$\pm$5.61 BPM and 7.49$\pm$3.23 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.