Application of the Neural Network in the Study of Skeletal Muscle with Multi-parameters

Author

Shi, Jun ; Yan, Zhuangzhi ; Zheng, Yongping ; Ha, Zhang

Author_Institution

Sch. of Commun. & Inf. Eng., Shanghai Univ.

Volume

1

fYear

0

fDate

0-0 0

Firstpage

2993

Lastpage

2997

Abstract

The mechanical properties of skeletal muscles are always related to its architectural changes. But at present, there are very few documents that report the comprehensive study on skeletal muscle with structure morphology, mechanics, electrophysiology and so on. This paper introduced that the muscle thickness and surface electromyography (SEMG) sampled from the extensor carpi radialis by the ultrasound measurement of motion and elasticity system were used to fit the wrist angle with several regression algorithms. The results illuminate that the wrist angle is well fitted by combining the SMG and SEMG signals together, especially with the neural network algorithm, and the results are better than the results fitted by SMG or SEMG alone. So it is better to use multi-parameters to study the skeletal muscle

Keywords

bioelectric phenomena; biomechanics; electromyography; mechanical properties; medical signal processing; neural nets; SEMG signals; SMG signals; elasticity system; electrophysiology; extensor carpi radialis; motion system; muscle thickness; neural network; regression algorithms; skeletal muscle mechanical properties; structure morphology; surface electromyography; ultrasound measurement; Electromyography; Mechanical factors; Motion measurement; Muscles; Neural networks; Surface fitting; Surface morphology; Thickness measurement; Ultrasonic imaging; Wrist; neural network; sonomyography; surface electromyography; ultrasound;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1712915

Filename

1712915