Evidence map›Paper›PMID 42316384›Full record

ArticleBMC biomedical engineering2026

Multimodal data synchronization: a high-level software methodology for heterogeneous devices.

Damiano Fruet, Stefano Cimignolo, Giandomenico Nollo

Abstract read
In one paragraph

Article in BMC biomedical engineering, 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
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1 · What the graph read from it

What it found

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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

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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

3 authors.

Damiano FruetDepartment of Industrial Engineering, University of Trento, Trento, Italy. damiano.fruet@unitn.it.ORCID http://orcid.org/0000-0003-2956-7652
Stefano CimignoloDepartment of Industrial Engineering, University of Trento, Trento, Italy.
Giandomenico NolloDepartment of Industrial Engineering, University of Trento, Trento, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMultimodal data acquisition from heterogeneous devices is frequently compromised by synchronization errors arising from clock skew and offset. Current solutions often necessitate complex external hardware or shared reference signals, which complicates deployment and limits system scalability.

methodsThis research introduces a high-level software methodology that utilizes a dedicated data acquisition protocol to record device-specific timestamps alongside a master PC timestamp. An offline linear regression model is then employed to convert internal timestamps to a common reference, compensating for clock discrepancies. The method was validated using electrocardiographic and Inertial Measurement Unit devices compared against a gold-standard reference. The ECG R-peaks fiducial points from the synchronized devices and the gold standard were compared.

resultsValidation showed an average R-peak synchronization delay of 20.1 ms over a one-hour acquisition (a 0.03% difference). The method successfully eliminates the need for external synchronization hardware, allowing scaling to be limited only by PC connectivity.

conclusionWhile dependent on device Software Development Kits, this methodology provides a robust, scalable foundation for precise multimodal synchronization. This proof-of-concept demonstrates that high-level software control can achieve high accuracy for dynamic clock adjustment.

Indexed as

Clock compensationMultimodal signalsPhysiological signal monitoringSynchronizationTimestamp synchronization

Identifiers

PMID42316384
PMCPMC13277054

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LicenceCC BY-NC-ND
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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.