Ridge detection for nonstationary multicomponent signals with time-varying wave-shape functions and its applications
We introduce a novel ridge detection algorithm for time-frequency (TF) analysis, particularly tailored for intricate nonstationary time series encompassing multiple non-sinusoidal oscillatory components. The algorithm is rooted in the distinctive geometric patterns that emerge in the TF domain due t...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
12.09.2023
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Subjects | |
Online Access | Get full text |
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Summary: | We introduce a novel ridge detection algorithm for time-frequency (TF)
analysis, particularly tailored for intricate nonstationary time series
encompassing multiple non-sinusoidal oscillatory components. The algorithm is
rooted in the distinctive geometric patterns that emerge in the TF domain due
to such non-sinusoidal oscillations. We term this method \textit{shape-adaptive
mode decomposition-based multiple harmonic ridge detection}
(\textsf{SAMD-MHRD}). A swift implementation is available when supplementary
information is at hand. We demonstrate the practical utility of
\textsf{SAMD-MHRD} through its application to a real-world challenge. We employ
it to devise a cutting-edge walking activity detection algorithm, leveraging
accelerometer signals from an inertial measurement unit across diverse body
locations of a moving subject. |
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DOI: | 10.48550/arxiv.2309.06673 |