  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.
