The human body continuously generates weak, broadband acoustic-mechanical signals that encode rich cardiopulmonary information, yet their reliable capture in real-world settings remains constrained by motion artifacts, limited bandwidth, and suboptimal mechanical coupling. Here, we present a wearable, wireless stethoscope that enables high-fidelity acquisition of heart and respiratory sounds through a mechanically compliant, broadband sensing interface. The system is built on a piezoelectric micromachined ultrasonic transducer (PMUT) engineered to achieve ultrahigh sensitivity (-167.5 dB), a wide operating bandwidth (10 Hz-10 kHz), and a flat frequency response (±0.5 dB), enabling accurate reconstruction of subtle physiological signals across a broad spectral range. Integration with soft, skin-conformal packaging and a robust wireless readout minimizes motion-induced noise and supports stable, long-term monitoring during daily activities. Validation across multiple participants demonstrates strong agreement with clinical-grade systems, including consistent auscultation at standard cardiac sites and accurate heart-rate tracking during dynamic exercise. Furthermore, coupling the sensing platform with a residual neural network enables automated classification of five respiratory states (awake, asleep, apnea, rhonchi, and wheeze) with an accuracy of 98.7%. This work establishes a scalable, low-cost framework for continuous, high-fidelity cardiopulmonary monitoring in real-world environments.
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Wu et al. (2026) studied this question.
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