Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18314
Title: Prediction of wrist angle from sonomyography signals with artificial neural networks technique
Authors: Shi, J
Zheng, YP 
Yan, ZZ
Keywords: Backpropagation
Biomechanics
Biomedical ultrasonics
Bone
Medical computing
Muscle
Recurrent neural nets
Issue Date: 2006
Publisher: IEEE
Source: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006 : EMBS '06, August 30 2006-September 3 2006, New York, NY, p. 3549-3552 How to cite?
Abstract: Surface electromyography (SEMG) is widely used for the functional assessment of skeletal muscles, while sonography has been commonly used to detect its morphological information. We defined the signal about the continuous change of the morphological parameters of muscles detected by ultrasound as sonomyography (SMG). In this study, we continuously sampled the ultrasound image, SEMG signals on the extensor carpi radialis muscle together with the wrist angle simultaneously during the whole process of wrist extension and flexion from 7 normal subjects. A three-layer feed-forward artificial neural network with BP learning algorithm was used to predict the wrist angle with the muscle deformation SMG and root mean square of SEMG signals as inputs. The overall mean R value was 0.96plusmn0.02, the mean standard root mean square error was 7.26plusmn1.98, and the mean relative root mean square errors was 0.160plusmn0.037. The results demonstrated that the wrist angle could be well predicted by combining the SMG and SEMG signals with ANN. Our result suggested that the combination of the information of SMG and SEMG could provide more comprehensive assessment of the skeletal muscle
URI: http://hdl.handle.net/10397/18314
ISBN: 1-4244-0032-5
1-4244-003303 (E-ISBN)
ISSN: 1557-170X
DOI: 10.1109/IEMBS.2006.259708
Appears in Collections:Conference Paper

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