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.
@article{arxiv.2309.06673,
title = {Ridge detection for nonstationary multicomponent signals with time-varying wave-shape functions and its applications},
author = {Yan-Wei Su and Gi-Ren Liu and Yuan-Chung Sheu and Hau-Tieng Wu},
journal= {arXiv preprint arXiv:2309.06673},
year = {2024}
}