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We introduce a novel ridge detection algorithm for time-frequency (TF)analysis, particularly tailored for intricate nonstationary time seriesencompassing multiple non-sinusoidal oscillatory components. The algorithm isrooted in the distinctive geometric patterns that emerge in the TF domain dueto such non-sinusoidal oscillations. We term this method \textit{shape-adaptivemode decomposition-based multiple harmonic ridge detection}(\textsf{SAMD-MHRD}). A swift implementation is available when supplementaryinformation is at hand. We demonstrate the practical utility of\textsf{SAMD-MHRD} through its application to a real-world challenge. We employit to devise a cutting-edge walking activity detection algorithm, leveragingaccelerometer signals from an inertial measurement unit across diverse bodylocations of a moving subject.

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