| Summary: | 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
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