Broadband wearable acoustic sensing for continuous cardiopulmonary monitoring.

IF 11.1 1区 工程技术 Q1 INSTRUMENTS & INSTRUMENTATION
Pengfan Wu, Tao Liu, Dongxiao Li, Hanjie Dou, Jiaqian Yang, Hongliang Wang, Hengyu Guo, Xiaojing Mu
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Abstract

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.

用于连续心肺监测的宽带可穿戴声传感。
人体不断产生微弱的宽带声机械信号,编码丰富的心肺信息,但在现实环境中,它们的可靠捕获仍然受到运动伪影、有限带宽和次优机械耦合的限制。在这里,我们提出了一种可穿戴的无线听诊器,通过机械兼容的宽带传感接口,可以高保真地采集心脏和呼吸声音。该系统基于压电微机械超声换能器(PMUT),具有超高灵敏度(-167.5 dB)、宽工作带宽(10 Hz-10 kHz)和平坦频率响应(±0.5 dB),能够在宽光谱范围内精确重建细微的生理信号。集成了柔软的皮肤适形包装和强大的无线读数,最大限度地减少了运动引起的噪音,并在日常活动中支持稳定的长期监测。多名参与者的验证表明,该系统与临床级系统非常一致,包括标准心脏部位的一致听诊和动态运动期间准确的心率跟踪。此外,将传感平台与残差神经网络相结合,可以对五种呼吸状态(清醒、睡眠、呼吸暂停、喘息和喘息)进行自动分类,准确率为98.7%。这项工作建立了一个可扩展的、低成本的框架,用于在现实环境中进行连续、高保真的心肺监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Microsystems & Nanoengineering
Microsystems & Nanoengineering Materials Science-Materials Science (miscellaneous)
CiteScore
12.00
自引率
3.80%
发文量
123
审稿时长
20 weeks
期刊介绍: Microsystems & Nanoengineering is a comprehensive online journal that focuses on the field of Micro and Nano Electro Mechanical Systems (MEMS and NEMS). It provides a platform for researchers to share their original research findings and review articles in this area. The journal covers a wide range of topics, from fundamental research to practical applications. Published by Springer Nature, in collaboration with the Aerospace Information Research Institute, Chinese Academy of Sciences, and with the support of the State Key Laboratory of Transducer Technology, it is an esteemed publication in the field. As an open access journal, it offers free access to its content, allowing readers from around the world to benefit from the latest developments in MEMS and NEMS.
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