HearT : Enabling Acoustic Sensing of Ingested-Liquid Temperature via Hydrodynamic Vibration Modeling

Abstract

Drinking water temperature is linked to gastrointestinal diseases such as gastroesophageal reflux disease, esophageal cancer, and functional dyspepsia. This paper presents HearT , a headphone-deployable system that leverages fluid dynamics–based vibration modeling to infer the temperature of ingested fluids from bone-conducted swallowing sounds. The system first examines the stability and dominant source components of swallowing across different phases. It then models temperature-dependent vibration parameters for each source and maps acoustic features to these parameters. This physics-driven framework achieves strong performance even with classical machine learning models. HearT attains average errors lower than typical human subjective estimation and the oral thermal perception threshold. Compared with contact-based temperature measurements or ambient sensing, HearT enables continuous tracking of local temperature fluctuations within the body’s broader thermal field, while benefiting from the accessibility, robustness, and comfort of commercial headphones.