Acoustoscillogram: Unlocking Arterial Health Insights via Acoustic Sensing with Low-cost Wired Earphones
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Liang, Xiaoxuan, Zhaolong Wei, Shirui Cao, et al. “Acoustoscillogram: Unlocking Arterial Health Insights via Acoustic Sensing with Low-Cost Wired Earphones.” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 9, no. 4 (2025): 194:1-194:27. https://doi.org/10.1145/3770690.
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Abstract
Cardiovascular disease is a leading cause of mortality worldwide and a major contributor to rising healthcare costs. Early detection of arterial stiffness through metrics such as Pulse Wave Velocity (PWV) and Augmentation Index (AIx) is essential for cardiovascular disease prevention and treatment. However, conventional measurement methods typically require expensive, specialized equipment and clinical settings. In this paper, we explore the use of everyday earphones to monitor PWV and AIx in daily life by capturing skin displacement waveforms induced by arterial pulsation. However, several key challenges must be addressed. First, detecting subtle skin displacement is difficult due to strong self-interference from the speaker to microphone. To address this, we propose a Doppler shift-based displacement estimation approach to isolate dynamic movements from static interference. Second, estimating PWV requires simultaneous two-point measurements, which is non-trivial with standard earphones. We address this with a hardware-software co-design that connects two earphone pairs via a commercial audio splitter and uses orthogonal frequency division multiplexing (OFDM)-based signal separation to extract displacement signals from both sites. Third, enabling general public use without medical training requires thoughtful system design. To this end, we develop a user-friendly mobile application that provides real-time feedback, along with a 3D-printed enclosure to facilitate ease of use and wide accessibility. We conducted IRB-approved clinical studies with 32 participants, comparing our measurements against ground truths from medical devices. Results show that our method achieves a PWV error of less than 0.5 m/s and an AIx error of less than 4%, meeting established medical standards. Please find the demo of our system here.
