Neural network based prediction of Parkinsonian hand tremor using surface electromyography Online publication date: Thu, 15-Nov-2012
by Sourav Chandra; Koushik Bakshi; Amit Konar; D.N. Tibarewala
International Journal of Signal and Imaging Systems Engineering (IJSISE), Vol. 5, No. 4, 2012
Abstract: Tremor is unwanted movement of body part. Tremor model estimations are employed in intelligent tremor prediction and compensation scheme. One way of modelling Parkinsonian tremor is the analysis-synthesis approach, in which theoretically guessed properties of the individual parts are combined mathematically. However, this approach has limited success in predicting certain tremor characteristics. The second method uses different measures of the tremor (frequency, amplitude, effect of loading, etc.) in order to infer the underlying phenomena. Electromyogram (EMG) being a related physiological event, is included in the parameter domain, which helps to estimate certain tremor characteristics. An artificial neural network based study of tremor in Parkinson's disease and its compensation technique is discussed here.
Online publication date: Thu, 15-Nov-2012
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Signal and Imaging Systems Engineering (IJSISE):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org