Heart Rate Monitoring Through ANC Headphones in Unconstrained Environments

Zhenyu Wu, Maanya Shanker, Tao Chen, Xiaoran Fan, Longfei Shangguan

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Keywords: Earphone Sensor

Abstract: This paper introduces CLEAR-APG, a novel acoustic sensing approach that enables reliable heart rate monitoring in unconstrained environments using off-the-shelf active noise cancellation (ANC) headphones. By emitting ultrasonic signals into the user's ear canal via the headphone speaker and analyzing their echoes, which can detect the frequency of a pulsating vein along the canal wall. However, everyday activities such as exercising, speaking, or eating cause jaw movements that deform the ear canal, overwhelming the subtle deformation caused by blood flowing. To overcome this challenge, we employ the ANC headphone’s built-in gyroscope to capture body motion and identify how various motion patterns influence the heartbeat waveform. Building on this insight, we propose a multi-modal method that effectively denoises the heartbeat waveform measurements and further accurately extracts heart rate. We implement CLEAR-APG on ANC earbuds and conduct comprehensive field studies on 14 users. The results show that CLEAR-APG achieves an average heart rate error of 4.01% across seven different activities, satisfying industry-required margin of 10% heart rate error.

Track: 3. Signal processing, machine learning, deep learning, and decision-support algorithms for digital and computational health

NominateReviewer:

Longfei Shangguan
[e-mail]

Tao Chen
[e-mail]

Submission Number: 122