Abstract
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 shape-adaptive mode decomposition-based multiple harmonic ridge detection (SAMD-MHRD). A swift implementation is available when supplementary information is at hand. We demonstrate the practical utility of 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.
Original language | English (US) |
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Pages (from-to) | 4843-4854 |
Number of pages | 12 |
Journal | IEEE Transactions on Signal Processing |
Volume | 72 |
DOIs | |
State | Published - 2024 |
Keywords
- Time-frequency analysis
- curve extraction
- ridge detection
- synchrosqueezing transform
- wave-shape function
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering