Intelligent Gait Parameter Analysis System Based on Deep Learning and Human Skeleton Detection in Videos

Abstract

An intelligent gait parameter analysis system is proposed based on deep learning and human skeleton detection in videos. Video of the subject’s whole body while walking along a straight path is recorded, then gait landmark sequences are detected and corrected. After that, the corresponding frame intervals of heel landing are detected and used for calculating four gait parameters, gait speed, stride length, stride duration, and cadence. Experimental results have shown that by comparing each detected gait parameter with its corresponding ground truth, the mean squared error, mean absolute error, and mean absolute percentage error are all small. Moreover, five of six detected gait parameters possess high Pearson correlation coefficients with the corresponding ground truth. Therefore, our proposed system possesses the potential to be a precise and efficient gait analysis approach.