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

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.