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arxiv: 2509.10764 · v3 · submitted 2025-09-13 · 💻 cs.HC

LubDubDecoder: Bringing Micro-Mechanical Cardiac Monitoring to Hearables

Siqi Zhang , Xiyuxing Zhang , Duc Vu , Tao Qiang , Clara Palacios , Jiangyifei Zhu , Yuntao Wang , Mayank Goel
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Justin Chan
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Pith reviewed 2026-05-18 17:27 UTC · model grok-4.3

classification 💻 cs.HC
keywords hearablescardiac monitoringheart soundsseismocardiographygyrocardiographyacoustic sensingwearable healthmicro-mechanical vibrations
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The pith

Built-in speakers in hearables can capture coarse heart sounds and reconstruct subtle micro-cardiac vibrations for valve timing.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows how everyday hearables can perform detailed cardiac monitoring by repurposing their standard speaker as a sensor. It captures the familiar lub-dub sounds and uses their underlying temporal and spectral patterns to rebuild the finer seismocardiography and gyrocardiography signals that normally require chest sensors. This reconstruction lets the system extract precise timings of heart-valve events without adding any new hardware. Tests across 25 users confirm the approach works for both repeated use on the same person and across different users, and it adapts to new devices with no extra effort. The result points toward continuous, low-cost heart monitoring that fits into normal listening devices.

Core claim

The central claim is that the built-in speaker common to all hearables can be operated as an acoustic sensor to record coarse lub-dub heart sounds, which share enough temporal and spectral structure with the finer mechanical vibrations to allow reconstruction of seismocardiography and gyrocardiography waveforms and extraction of micro-cardiac event timings.

What carries the argument

LubDubDecoder system that converts the built-in speaker into an acoustic sensor and reconstructs SCG and GCG waveforms from shared structure in lub-dub sounds.

If this is right

  • Any hearable device can add micro-cardiac monitoring without new transducers or batteries.
  • Key heart-valve opening and closing times become available during normal wear, including while music plays.
  • The same reconstruction works across different hearable models after a simple zero-effort adaptation step.
  • Remounting the device does not break the signal quality in repeated daily use.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same speaker-to-sensor trick could be tested on other body sounds such as breathing or joint motion.
  • Integration with existing audio health features in earbuds might create always-on cardiac dashboards for users.
  • If the reconstruction holds in larger populations, it could reduce reliance on separate chest-worn monitors for routine checks.

Load-bearing premise

Coarse heart sounds recorded by the speaker contain enough shared timing and frequency information to accurately rebuild the subtle chest-level mechanical vibrations.

What would settle it

A controlled test in which reconstructed waveforms from the hearable show correlation below 0.7 with simultaneous chest-mounted reference SCG and GCG signals across multiple users and sessions.

Figures

Figures reproduced from arXiv: 2509.10764 by Clara Palacios, Duc Vu, Jiangyifei Zhu, Justin Chan, Mayank Goel, Siqi Zhang, Tao Qiang, Xiyuxing Zhang, Yuntao Wang.

Figure 1
Figure 1. Figure 1: LubDubDecoder enables hands-free monitoring of micro-mechanical cardiac events across a range of hearables, including (a) an older adult sleeping at home with over-ear headphones, (b) a driver wearing wireless earbuds, and (c) a commuter on a train using bone-conduction earphones. points correspond to micro-cardiac events detected by our system. (d) Smartphone app user interface with hearables. We present … view at source ↗
Figure 2
Figure 2. Figure 2: System overview. LubDubDecoder reconstructs the heart’s micro-mechanical signals from coarse-grained heart sounds recorded at the ear using microphones and speakers across a range of hearables. It identifies the timing of key micro-cardiac events, and enables hands-free monitoring of cardiovascular health in everyday scenarios. against the chests, or constraining users to remain in a fixed location and ori… view at source ↗
Figure 3
Figure 3. Figure 3: Cardiac signal timing diagram. Each heartbeat begins with the electrical depolarization of the ventricles, measurable by the ECG. This is followed by the mitral valve closing which creates the “lub” (S1) sound of the heart, which generates an acoustic signal detectable at the ear. Later in the cardiac cycle, the aortic valve closes, producing the “dub” (S2) sound. The heart’s mechanical motion can be captu… view at source ↗
Figure 4
Figure 4. Figure 4: Challenge of conventional IMU-based micro-cardiac measurements. Differences in sensor placement lead to variations in waveforms, making comparisons across repeated measurements challenging. Precise and consistent placement is difficult to ensure when measurements are performed by lay users outside clinical settings. Each waveform corresponds to a cycle of 800 ms, and amplitudes are normalized to their own … view at source ↗
Figure 5
Figure 5. Figure 5: Dataset collection setup. (a) Ear-based cardiac sounds are measured using the microphone or speaker of a hearable; mechanical cardiac vibrations are measured at the left lower sternal border around the heart using a smartphone IMU. (b) Hearables used for data collection span a range of device types. • In-ear earbuds. The in-ear microphones and speakers on earphones are able to detect cardiac sounds due to … view at source ↗
Figure 6
Figure 6. Figure 6: Effect of device remounting on cardiac signals. Within a single session, cardiac signals show similar morphology across cycles (𝑛 = 60 cycles). After remounting the hearable and smartphone, ear-based cardiac sounds and micro-mechanical signals maintain comparable waveform shapes, with a modest increase in variability across cycles. Colored opaque line is mean signal across cardiac cycles, all cardiac cycle… view at source ↗
Figure 7
Figure 7. Figure 7: Effect of individual physiology on cardiac signal variability. Waveform variability is presented for a random subset of 𝑛 = 6 subjects from our human subjects study showing differences in ear-based cardiac sounds, SCG, and GCG signals (𝑛 = 60 cycles). Solid opaque lines represents the mean across all cardiac cycles within each subject, all cardiac cycles are overlaid in translucent color. Each waveform cor… view at source ↗
Figure 8
Figure 8. Figure 8: Effect of hardware differences on cardiac signals. Hardware differences across (a) hearables create differences in the measured ear-based cardiac sounds (𝑛 = 60 cycles) and (b) across smartphones create differences in the measured SCG and GCG signals (𝑛 = 15 cycles). These differences motivate the need for our zero-effort calibration procedure upon use of a new device. Blue line is the mean signal across c… view at source ↗
Figure 9
Figure 9. Figure 9: LubDubDecoder system pipeline. Manuscript submitted to ACM [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Effect of user motions on in-ear cardiac sounds. (a) Dataset composition used to train our motion artifact removal classifier, showing the number of 10-second audio recordings in each class. (b) Motion artifacts produce signals with much higher amplitude than heart sounds, obscuring them. Our system automatically detects and discards segments affected by such artifacts. 3.3.1 Motion artifact removal pipel… view at source ↗
Figure 11
Figure 11. Figure 11: Demographic summary of participants in the human subjects study. Complete demographic data was collected for 12 of 18 participants. obtained for human subjects participating in the study. Randomization was not applicable and investigators were not blinded. Participants above the age of 18 were eligible for the study. Exclusionary criteria include pregnant women, prisoners, adults with cognitive impairment… view at source ↗
Figure 12
Figure 12. Figure 12: Waveform reconstruction performance across 18 subjects. Results are shown for within-user and cross-user evaluations. Within-user. We first evaluate the reconstruction performance of SCG and GCG waveforms under a within-user setting, where both training and testing are performed on data from the same participant. When using the in-ear speaker of wired earbuds, across 18 subjects, the reconstructed signals… view at source ↗
Figure 13
Figure 13. Figure 13: Effect of number of cardiac cycles used for calibration on cross-user SCG reconstruction performance [PITH_FULL_IMAGE:figures/full_fig_p019_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Cross-device performance. (a) SCG waveform reconstruction (b) fiducial point timing error. The ear-based heart sounds from different hearables are normalized to a single reference device (in-ear speaker of wired earbuds), and inference is performed on a model trained on that reference. Data is presented for three different subjects. In comparison, when using the in-ear microphone of wireless earbuds, the … view at source ↗
Figure 15
Figure 15. Figure 15: Timing accuracy of micro-cardiac events. System performance is shown for SCG and GCG timing accuracy as collected on the in-ear speaker on earbuds. 2 ms (20 ms) for MC, 2 ms (20 ms) for IM, 0 ms (6 ms) for AO, 4 ms (18 ms) for MA, and 4 ms (28 ms) for RE. These correspond to relative median errors in the range of 0.0–0.5% and 95th percentile errors of 0.75–3.5% of the cardiac cycle, which are comparable t… view at source ↗
Figure 16
Figure 16. Figure 16: Subgroup analysis on cross-user waveform reconstruction performance. results were 0.85 ± 0.13 (SCG) and 0.86 ± 0.10 (GCG). Finally, in the obese group, the system reached 0.90 ± 0.09 (SCG) and 0.89 ± 0.10 (GCG). Sex. In our study, 28% (𝑛 = 5) of subjects were female and 72% (𝑛 = 13) of subjects were male. When comparing system performance, female subjects had an SCG reconstruction similarity of 0.91 ± 0.1… view at source ↗
Figure 17
Figure 17. Figure 17: Effect of music playback. As micro-cardiac signals and music largely occupy non-overlapping frequency bands, the overall shape of the cardiac cycle and fiducial points are visible even without a 5–45 Hz bandpass filter [PITH_FULL_IMAGE:figures/full_fig_p023_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Motion artifact removal pipeline performance. Evaluation is performed on in-ear audio segments when the user is static and moving. (a) PCA projection of the first two components of MFCC audio features illustrating separability between the two classes. (b) ROC curve from five-fold cross validation. (c) Confusion matrix indicating optimal operating point on the ROC curve. evaluated end-to-end system perform… view at source ↗
Figure 19
Figure 19. Figure 19: Effect of ear tip on ear-based cardiac sounds. Mean cardiac cycles recorded from one participant using four different ear-tip types. recorded from each of the ear tips. The average Pearson correlation across the four eartips was 0.88 ± 0.06, suggesting that for the common ear tip sizes and materials used for adults, it has minimal effect on the morphology of the measured cardiac signals. To further examin… view at source ↗
Figure 20
Figure 20. Figure 20: User experience survey (𝑛 = 16). Histograms summarize user perceptions across (a) ease of use, (b) system trustworthiness, (c) amount willing to pay, and (d) whether the system made them more conscious of their cardiac health. We evaluated user experience of our system through a short survey. Participants were sent a form containing the following questions: (1) Is the system easy to use? (1 = Not at all, … view at source ↗
read the original abstract

We present LubDubDecoder, a system that enables fine-grained monitoring of micro-cardiac vibrations associated with the opening and closing of heart valves across a range of hearables. Our system transforms the built-in speaker, the only transducer common to all hearables, into an acoustic sensor that captures the coarse "lub-dub" heart sounds, leverages their shared temporal and spectral structure to reconstruct the subtle seismocardiography (SCG) and gyrocardiography (GCG) waveforms, and extract the timing of key micro-cardiac events. In an IRB-approved feasibility study with 25 users, our system achieves correlations of 0.88-0.95 compared to chest-mounted reference measurements in within-user and cross-user evaluations, and generalizes to unseen hearables using a zero-effort adaptation scheme with a correlation of 0.91. Our system is robust across remounting sessions and music playback.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper presents LubDubDecoder, a system that repurposes the built-in speaker in hearables as an acoustic sensor to capture coarse 'lub-dub' heart sounds. It leverages assumed shared temporal and spectral structure between these sounds and micro-mechanical cardiac vibrations to reconstruct seismocardiography (SCG) and gyrocardiography (GCG) waveforms, then extracts timing of key events. In an IRB-approved feasibility study with 25 users, the system reports correlations of 0.88-0.95 against chest-mounted reference sensors for within-user and cross-user cases, plus 0.91 correlation for generalization to unseen hearables via zero-effort adaptation; robustness to remounting and music playback is also claimed.

Significance. If the reconstruction claims hold with rigorous validation, the work could enable practical micro-cardiac monitoring on ubiquitous consumer hearables without extra hardware, advancing accessible health sensing in HCI and wearable computing. The zero-effort adaptation and use of a common transducer are practical strengths that could support broader deployment if the underlying signal fidelity is confirmed.

major comments (3)
  1. [Reconstruction method] Reconstruction method (Section 3 / equivalent): The central claim rests on transforming coarse acoustic lub-dub signals into subtle SCG/GCG waveforms via 'shared temporal and spectral structure,' yet no explicit mapping, basis functions, loss term, or architectural details are provided to establish that the inversion is unique rather than a learned correlation on the study cohort. This directly affects whether the 0.88-0.95 correlations demonstrate true waveform fidelity or primarily valve-event alignment.
  2. [Results and evaluation] Results and evaluation (Section 4 / equivalent): The reported correlations of 0.88-0.95 (within/cross-user) and 0.91 (unseen devices) are presented without accompanying details on the full signal-processing pipeline, model training procedure, error bars, statistical tests, or data exclusion criteria from the 25-user IRB study. These omissions are load-bearing for assessing whether the numbers support the reconstruction and generalization claims.
  3. [Zero-effort adaptation] Zero-effort adaptation for unseen hearables (Section 5 / equivalent): The hardware-invariance assumption underlying the 0.91 correlation requires concrete evidence (e.g., feature invariance analysis or cross-device spectral comparison) that the acoustic channel preserves sufficient SCG/GCG content across different speaker transducers; without it, the adaptation result risks being cohort-specific rather than general.
minor comments (2)
  1. [Abstract] The abstract would benefit from briefly naming the specific hearable models or form factors tested in the generalization experiment.
  2. [Figures] Figure captions for any waveform comparison plots should explicitly state the time and amplitude scales used for visual assessment of reconstruction quality.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment point by point below, providing clarifications from the manuscript and committing to revisions that strengthen the presentation of methods, results, and generalization claims without altering the core findings.

read point-by-point responses

  1. Referee: [Reconstruction method] Reconstruction method (Section 3 / equivalent): The central claim rests on transforming coarse acoustic lub-dub signals into subtle SCG/GCG waveforms via 'shared temporal and spectral structure,' yet no explicit mapping, basis functions, loss term, or architectural details are provided to establish that the inversion is unique rather than a learned correlation on the study cohort. This directly affects whether the 0.88-0.95 correlations demonstrate true waveform fidelity or primarily valve-event alignment.

    Authors: We appreciate this observation on the reconstruction approach. Section 3 describes a convolutional encoder-decoder network that exploits shared temporal alignments (e.g., S1/S2 timing) and spectral bands (10-100 Hz overlap between acoustic and micro-mechanical signals) via learned filters and attention layers. The training objective combines Pearson correlation loss on the full waveform with an auxiliary peak-detection loss for event timing. To directly address uniqueness versus event alignment, the revised manuscript will add the complete architecture specification (layer counts, kernel sizes, activation functions), the exact weighted loss formulation, and an ablation study demonstrating that spectral and temporal components contribute to morphology preservation beyond peak alignment alone (measured via dynamic time warping and frequency-domain correlation). revision: yes

  2. Referee: [Results and evaluation] Results and evaluation (Section 4 / equivalent): The reported correlations of 0.88-0.95 (within/cross-user) and 0.91 (unseen devices) are presented without accompanying details on the full signal-processing pipeline, model training procedure, error bars, statistical tests, or data exclusion criteria from the 25-user IRB study. These omissions are load-bearing for assessing whether the numbers support the reconstruction and generalization claims.

    Authors: We agree that expanded evaluation details are essential for assessing the reported correlations. The current Section 4 summarizes the 25-user IRB-approved study but will be revised to include: the complete preprocessing pipeline (band-pass filtering parameters, segmentation windows, normalization); training procedure (5-fold cross-validation splits, Adam optimizer with learning rate 1e-4, batch size 32, 150 epochs, early stopping); error bars as mean ± standard deviation across users and folds; statistical significance via paired Wilcoxon signed-rank tests with p-values; and exclusion criteria (2 users excluded for SNR < 10 dB due to motion artifacts, per pre-registered protocol). These additions will be incorporated to support reproducibility and claim validity. revision: yes

  3. Referee: [Zero-effort adaptation] Zero-effort adaptation for unseen hearables (Section 5 / equivalent): The hardware-invariance assumption underlying the 0.91 correlation requires concrete evidence (e.g., feature invariance analysis or cross-device spectral comparison) that the acoustic channel preserves sufficient SCG/GCG content across different speaker transducers; without it, the adaptation result risks being cohort-specific rather than general.

    Authors: The 0.91 correlation was obtained by training on data from one hearable model and evaluating zero-shot on a distinct unseen model in the 25-user cohort. To substantiate the hardware-invariance assumption, the revision will add a cross-device analysis: spectral power density plots demonstrating overlap in the 5-50 Hz cardiac band across the two transducers, plus t-SNE visualizations and cosine similarity metrics on learned embeddings showing feature distribution invariance. This evidence will confirm that the acoustic channel retains sufficient micro-mechanical content for generalization beyond the specific cohort. revision: yes

Circularity Check

0 steps flagged

No circularity: central claims validated against independent chest-mounted references

full rationale

The paper describes transforming a built-in speaker into an acoustic sensor to capture coarse lub-dub sounds and reconstruct SCG/GCG waveforms by leveraging shared temporal and spectral structure. However, the reported correlations (0.88-0.95 within/cross-user, 0.91 for unseen hearables) are obtained via direct comparison to external chest-mounted reference sensors in a 25-user IRB study, not derived from fitted parameters or self-citations within the same data. No equations, self-citation chains, or uniqueness theorems are presented that reduce any prediction to its inputs by construction. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no information on free parameters, axioms, or invented entities; all such elements are unknown.

pith-pipeline@v0.9.0 · 5711 in / 1040 out tokens · 38736 ms · 2026-05-18T17:27:13.571252+00:00 · methodology

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