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Background <p>Multimodal 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.</p> Methods <p>This 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.</p> Results <p>Validation 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.</p> Conclusion <p>While 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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月18日

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  • Damiano Fruet,
  • Stefano Cimignolo,
  • Giandomenico Nollo

摘要

Background

Multimodal 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.

Methods

This 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.

Results

Validation 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.

Conclusion

While 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.