Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-04-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-024-01143-1 |
| _version_ | 1797219543846748160 |
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| author | Christoph Schranz Sebastian Mayr Severin Bernhart Christina Halmich |
| author_facet | Christoph Schranz Sebastian Mayr Severin Bernhart Christina Halmich |
| author_sort | Christoph Schranz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T12:35:19Z |
| format | Article |
| id | doaj.art-9c99d9df3ae14c82ab824e27b71f68c1 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-04-24T12:35:19Z |
| publishDate | 2024-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-9c99d9df3ae14c82ab824e27b71f68c12024-04-07T11:34:09ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802024-04-012024112410.1186/s13634-024-01143-1Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronizationChristoph Schranz0Sebastian Mayr1Severin Bernhart2Christina Halmich3Human Motion Analytics, Salzburg ResearchHuman Motion Analytics, Salzburg ResearchHuman Motion Analytics, Salzburg ResearchHuman Motion Analytics, Salzburg ResearchAbstract 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.https://doi.org/10.1186/s13634-024-01143-1Event-based time-seriesTime delay estimationSynchronizationClock driftCross-correlationKernel cross-correlation |
| spellingShingle | Christoph Schranz Sebastian Mayr Severin Bernhart Christina Halmich Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization EURASIP Journal on Advances in Signal Processing Event-based time-series Time delay estimation Synchronization Clock drift Cross-correlation Kernel cross-correlation |
| title | Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization |
| title_full | Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization |
| title_fullStr | Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization |
| title_full_unstemmed | Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization |
| title_short | Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization |
| title_sort | nearest advocate a novel event based time delay estimation algorithm for multi sensor time series data synchronization |
| topic | Event-based time-series Time delay estimation Synchronization Clock drift Cross-correlation Kernel cross-correlation |
| url | https://doi.org/10.1186/s13634-024-01143-1 |
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