Quantifying normal and parkinsonian gait features from home movies: Practical application of a deep learning–based 2D pose estimator
Fig 3
Short-time ACF used to calculate cadence of a normal gait sequence from CASIA Dataset-B.
(A) The filtered LRdiff sequence of control gait movies from CASIA Dataset-B. For these normal gait movies, we applied ST-ACF with a window length of 2 seconds and a shift-length of 0.01 second (A). In ST-ACF (D), the reciprocal of the lag at the peak of the second positive phase after the initial negative peak (white arrow in D) was selected as the representative frequency. When the sequential ST-ACF matrix is plotted on a heatmap (E), the selected lag is seen as the second from the top and most red horizontal line (white arrow in E). STFT (B, C) is shown as a corresponding example to the ST-ACF: sequential STFT is plotted as a heatmap (C), where the minimum value is white and the maximum color is most-red. The selected frequency just corresponds to the height of the horizontal most-red line (filled arrow in C).