Ridge detection for nonstationary multicomponent signals with time-varying wave-shape functions and its applications

TL;DR

This paper presents a new ridge detection algorithm for complex nonstationary signals with non-sinusoidal oscillations, enabling improved time-frequency analysis and practical applications like activity detection from accelerometer data.

Contribution

The paper introduces SAMD-MHRD, a novel shape-adaptive ridge detection method based on geometric patterns in the TF domain for nonstationary multicomponent signals.

Findings

Effective in detecting ridges in complex nonstationary signals

Applied successfully to walking activity detection using accelerometer data

Provides a fast implementation for practical use

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 \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.