Evidence map›Paper›PMID 42805949›Full record

ArticleMicrosystems & nanoengineering2026

Broadband wearable acoustic sensing for continuous cardiopulmonary monitoring.

Pengfan Wu, Tao Liu, Dongxiao Li, Hanjie Dou, Jiaqian Yang, Hongliang Wang, Hengyu Guo, Xiaojing Mu

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Pengfan Wu *Key Laboratory of Optoelectronic Technology & Systems Ministry of Education, International R & D center of Micro-nano Systems and New Materials Technology, Chongqing University, Chongqing, 400044, P.R. China.
Tao Liu *Science and Technology on Electronic Test and Measurement Laboratory, State key Laboratory of Extreme Environment Optoelectronic Dynamic Measurement Technology and Instrument, North University of China, Taiyuan, 030051, P.R. China. liutao@nuc.edu.cn.
Dongxiao Li *Key Laboratory of Optoelectronic Technology & Systems Ministry of Education, International R & D center of Micro-nano Systems and New Materials Technology, Chongqing University, Chongqing, 400044, P.R. China.
Hanjie DouKey Laboratory of Optoelectronic Technology & Systems Ministry of Education, International R & D center of Micro-nano Systems and New Materials Technology, Chongqing University, Chongqing, 400044, P.R. China.ORCID http://orcid.org/0009-0002-0882-156X
Jiaqian YangKey Laboratory of Optoelectronic Technology & Systems Ministry of Education, International R & D center of Micro-nano Systems and New Materials Technology, Chongqing University, Chongqing, 400044, P.R. China.
Hongliang WangScience and Technology on Electronic Test and Measurement Laboratory, State key Laboratory of Extreme Environment Optoelectronic Dynamic Measurement Technology and Instrument, North University of China, Taiyuan, 030051, P.R. China.
Hengyu GuoCollege of Physics, Chongqing University, Chongqing, 400044, China.
Xiaojing MuKey Laboratory of Optoelectronic Technology & Systems Ministry of Education, International R & D center of Micro-nano Systems and New Materials Technology, Chongqing University, Chongqing, 400044, P.R. China. mxjacj@cqu.edu.cn.ORCID http://orcid.org/0000-0003-2024-2595

Funding

National Science Foundation of China | Key Programme U22B2089
6 · The paper itself

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.

Identifiers

PMID42805949
PMCPMC13619561

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.