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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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
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Abstract	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.
AbstractList	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. Abstract 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.
ArticleNumber	46
Audience	Academic
Author	Bernhart, Severin Halmich, Christina Schranz, Christoph Mayr, Sebastian
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Snippet	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... Abstract 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...
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SubjectTerms	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
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Title	Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization
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Volume	2024
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