Markerless gait analysis in stroke survivors based on computer vision and deep learning: A pilot study

Recent advances on markerless pose estimation based on computer vision and deep neural networks are opening the possibility of adopting efficient methods for extracting precise human pose and movement information from video data. In this paper we report the results of a pilot study carried out on a clinical gait analysis study-case, where we compare 2D parameters computed with a reference marker-based technique with the ones obtained with a markerless pipeline. The results we report are encouraging as they show there are no statistically significant differences between a set of selected parameters computed with the standard approach and the markerless one. Our study opens to a wide range of application of the approach on the variety of clinical domains, with countless benefits in terms of simplicity, unobtrusiveness, and computational efficiency.