Paper Info
Title
Markerless gait analysis in stroke survivors based on computer vision and deep learning: a pilot study
Abstract
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.
Year
DOI
Venue
2020
10.1145/3341105.3373963
SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing Brno Czech Republic March, 2020
Keywords
DocType
ISBN
Computer Vision, Markerless Gait Analysis, Deep Learning, Stroke Survivors
Conference
978-1-4503-6866-7
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Matteo Moro101.35
Giorgia Marchesi200.34
Francesca Odone341145.90
Maura Casadio42210.03