Authors: Mohamed Elgendi, Flavien Picon, Nadia Magnenat-Thalmann, Derek Abbott
Many clinical studies have shown that the arm movement of patients with neurological injury is often slow. In this paper, the speed analysis of arm movement is presented, with the aim of evaluating arm movement automatically using a Kinect camera. The consideration of arm movement appears trivial at rst glance, but in reality it is a very complex neural and biomechanical process that can potentially be used for detecting a neurological disorder. This is a preliminary study, on healthy subjects, which investigates three dierent arm-movement speeds: fast, medium and slow. With a sample size of 27 subjects, our developed algorithm is able to classify the three dierent speed classes (slow, normal, and fast) with overall error of 5.43% for interclass speed classication and 0.49% for intraclass classication. This is the rst step towards enabling future studies that investigate abnormality in arm movement, via use of a Kinect camera.
Comments: 21 Pages.
Download: PDF
[v1] 2013-01-20 11:54:56
[v2] 2013-07-18 19:58:41
[v3] 2014-01-22 09:47:55
Unique-IP document downloads: 585 times
Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.
Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.