Ridge Detection for Nonstationary Multicomponent Signals With Time-Varying Wave-Shape Functions and its Applications

Yan Wei Su, Gi Ren Liu, Yuan Chung Sheu, Hau Tieng Wu

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
Pages (from-to)4843-4854
Number of pages12
JournalIEEE Transactions on Signal Processing
Volume72
DOIs
StatePublished - 2024

Keywords

  • Time-frequency analysis
  • curve extraction
  • ridge detection
  • synchrosqueezing transform
  • wave-shape function

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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