Gait modelling is essential for many applications including animation, activity recognition, medical diagnosis, and robotics. Many researchers have worked on mathematically express the movement of human bodies. At the current stage, the reconstructed waveforms from the mathematical expressions either represent smoothened waveforms, noisy, or require a high number of computations. In this study, the thigh and shank angle waveforms are time and amplitude scaled before performing a discrete Fourier transform (DFT). By doing so, the correlation coefficient between the original and reconstructed waveforms can be improved without increasing the number of harmonics. The shank's angular velocity is also recalculated from the reconstructed shank's angle waveform for gait phase detection, and shows accurate results in heel and toe strikes estimation when compared to the original shank's angular velocity. Additionally, the harmonic components of the waveforms are used for gait recognition. Experimental results show that it is useful to time and amplitude-scale the angle waveforms to ‘enlarge’ the distinctive regions of the angle waveforms for better classification accuracy.

中文翻译：

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基于DFT之前的波形缩放的精确步态建模

步态建模对于许多应用至关重要，包括动画，活动识别，医学诊断和机器人技术。许多研究人员已经在数学上表达了人体的运动。在当前阶段，从数学表达式重建的波形要么表示平滑的波形，有噪声，要么需要大量计算。在这项研究中，在执行离散傅立叶变换（DFT）之前，对大腿和小腿角度波形进行时间和幅度缩放。通过这样做，可以在不增加谐波数量的情况下改善原始波形和重构波形之间的相关系数。还可以从重构的柄角波形中重新计算出柄的角速度，以进行步态相位检测，与原始小腿的角速度相比，在脚跟和脚趾撞击估计中显示出准确的结果。另外，波形的谐波分量用于步态识别。实验结果表明，对角度波形进行时间和幅度缩放以“扩大”角度波形的独特区域对于提高分类精度非常有用。