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Content Provider | IEEE Xplore Digital Library |
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Author | Connolly, J. Condell, J. Curran, K. Gardiner, P. |
Copyright Year | 2012 |
Description | Author affiliation: Intell. Syst. Res. Centre, Univ. of Ulster, Derry, UK (Connolly, J.; Condell, J.; Curran, K.) || Western Health & Social Care Trust, Altnagelvin Hosp., Derry, UK (Gardiner, P.) |
Abstract | Rheumatoid arthritis affects 0.5 - 1% of the general population. The prediction and prognosis of the disease varies for each individual and its course can detrimentally affect the psychosocial condition of the patient. Clinicians and Therapists aim to quickly diagnose and treat those with this debilitating disease. Detection relies heavily on manual evaluation methods that are dependent on training and can vary between observers. Angle measuring instrument, tape measure and grip strength dynamometer are used to assess the joint range and strength of a patient to determine their hand function. Joint stiffness can be a determining factor when diagnosing the advancement and improvement of Rheumatoid Arthritis (RA). This paper outlines the development of a hand movement measurement tool to accurately quantify patients' flexion, extension, abduction and adduction movement of each finger joint and quantifies the symptom of “early morning stiffness”. It also describes the problems that arise when using a data glove to accurately measure Range Of Movement and discusses alternative methods to overcome these issues. |
Sponsorship | IEEE Eng. Medicine Biol. Soc. |
Starting Page | 6386 |
Ending Page | 6389 |
File Size | 1393004 |
Page Count | 4 |
File Format | |
ISBN | 9781424441198 |
ISSN | 1557170X |
e-ISBN | 9781457717871 |
DOI | 10.1109/EMBC.2012.6347455 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2012-08-28 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Joints Sensors Thumb Read only memory Artificial neural networks Accuracy |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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