Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization

Estimating time delays in event-based time-series is a crucial task in signal processing as it affects the data quality and is a prerequisite for many subsequent analyses. In particular, data acquired from wearable devices often suffer from a low timestamp precision or clock drift. Current state-of-...

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Bibliographic Details
Published in:EURASIP journal on advances in signal processing Vol. 2024; no. 1; pp. 46 - 24
Main Authors: Schranz, Christoph, Mayr, Sebastian, Bernhart, Severin, Halmich, Christina
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.12.2024
Springer
Springer Nature B.V
SpringerOpen
Subjects:
Algorithms
Clock drift
Cross correlation
Data acquisition
Datasets
Engineering
Estimation
Event-based time-series
Information management
Kernel cross-correlation
Quantum Information Technology
Run time (computers)
Sensors
Signal processing
Signal,Image and Speech Processing
Spintronics
Synchronization
Time delay estimation
Time lag
Time series
Time synchronization
Wearable technology
Event-based time-series
Clock drift
Dynamic time warping
Kernel cross-correlation
Time delay estimation
Synchronization
Wearable devices
Cross-correlation
ISSN:1687-6180, 1687-6172, 1687-6180
Online Access:Get full text
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Summary:Estimating time delays in event-based time-series is a crucial task in signal processing as it affects the data quality and is a prerequisite for many subsequent analyses. In particular, data acquired from wearable devices often suffer from a low timestamp precision or clock drift. Current state-of-the-art methods such as Pearson Cross-Correlation are sensitive to typical data quality issues, e.g. misdetected events, and Dynamic Time Warping is computationally expensive. To overcome these limitations, we propose Nearest Advocate, a novel event-based time delay estimation method for multi-sensor time-series data synchronisation. We evaluate its performance using three independent datasets acquired from wearable sensor systems, demonstrating its superior precision, particularly for short, noisy time-series with missing events. Additionally, we introduce a sparse variant that balances precision and runtime. Finally, we demonstrate how Nearest Advocate can be used to solve the problem of linear as well as non-linear clock drifts. Thus, Nearest Advocate offers a promising opportunity for time delay estimation and post-hoc synchronization for challenging datasets across various applications.
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ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-024-01143-1