Paper Info
Title
Abnormal gait detection with RGB-D devices using joint motion history features
Abstract
Human gait has become of special interest to health professionals and researchers in recent years, not only due to its relation to a person's quality of life and personal autonomy, but also due to the involved cognitive process, since deviation from normal gait patterns can also be associated to neurological diseases. Vision-based abnormal gait detection can provide support to current human gait analysis procedures providing quantitative and objective metrics that can assist the evaluation of the geriatrician, while at the same time providing technical advantages, such as low intrusiveness and simplified setups. Furthermore, recent advances in RGB-D devices allow to provide low-cost solutions for 3D human body motion analysis. In this sense, this work presents a method for abnormal gait detection relying on skeletal pose representation based on depth data. A novel spatio-temporal feature is presented that provides a representation of a set of consecutive skeletons based on the 3D location of the skeletal joints and the motion's age. The corresponding feature sequences are learned using a machine learning method, namely BagOfKeyPoses. Experimentation with different datasets and evaluation methods shows that reliable detection of abnormal gait is obtained and, at the same time, an outstandingly high temporal performance is provided.
Year
DOI
Venue
2015
10.1109/FG.2015.7284881
2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)
Keywords
Field
DocType
RGB-D devices,joint motion history features,cognitive process,neurological diseases,vision-based abnormal gait detection,geriatrician evaluation,3D human body motion analysis,skeletal pose representation,spatiotemporal feature,3D skeletal joint location,machine learning method
Computer vision,Gait,Computer science,Intrusiveness,Artificial intelligence,RGB color model,Gait (human),Motion analysis,Cognition
Conference
Volume
ISSN
Citations 
07
2326-5396
3
PageRank 
References 
Authors
0.39
9
3
Name
Order
Citations
PageRank
Alexandros André Chaaraoui133812.13
José Ramón Padilla-López21686.48
Francisco Flórez-revuelta348134.95