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.

翻译：我们提出了一种新颖的时频（TF）分析脊线检测算法，专门针对包含多个非正弦振荡分量的复杂非平稳时间序列进行优化设计。该算法基于非正弦振荡在时频域中呈现的独特几何模式，并将其命名为《形态自适应模式分解多谐波脊线检测》（SAMD-MHRD）。在具备辅助信息时，可实现快速高效的计算。通过应用于实际挑战，我们展示了SAMD-MHRD的实用价值：利用惯性测量单元在不同身体部位采集的加速度计信号，设计了一种先进的步行活动检测算法。