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One-dimensional sonomyography (SMG) for skeletal muscle assessment and prosthetic control

Journal article published in 2010 by Jingyi Guo
This paper was not found in any repository; the policy of its publisher is unknown or unclear.
This paper was not found in any repository; the policy of its publisher is unknown or unclear.

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Abstract

xxvi, 158 p. : ill. ; 30 cm. ; PolyU Library Call No.: [THS] LG51 .H577P HTI 2010 Guo ; As indicators of torque output and motor unit recruitment, both electromyography (EMG) and mechanomyography (MMG) have been widely used to assess muscle fatigue, muscle pathology, control over prosthetic devices, etc. On the other hand, ultrasound imaging has been suggested as a method for viewing muscular architectural changes during contractions. Sonomyography (SMG) is the signal we previously termed to describe muscle contraction using real-time muscle morphological changes extracted from ultrasound images or signals. With the advantages of being less expensive, more compact, A-mode ultrasound was introduced to detect the dynamic thickness change of skeletal muscles during contraction, named as one-dimensional sonomyography (1D SMG). The 1D SMG signal was extracted from the ultrasound signal by automatically tracking the shift of echoes from tissue interfaces and the muscle thickness change was calculated. Compared with surface EMG, 1D SMG could discriminate activity of deep muscles from more superficial muscles. It was also found that 1D SMG signal linearly correlated with the wrist extension angle. Moreover, the least squares support vector machine (LS-SVM) and artificial neural networks (ANN) were used to predict dynamic wrist angles from 1D SMG signals. Synchronized wrist angle and SMG signals from the extensor carpi radialis muscles of nine normal subjects were recorded during the process of wrist extension and flexion at rates of 15, 22.5, and 30 cycles/min, respectively. An LS-SVM model together with back-propagation (BP) and radial basis function (RBF) ANN was trained using the data sets collected at the rate of 22.5 cycles/min for each subject. It was concluded that the wrist angle could be precisely estimated from the thickness changes of the extensor carpi radialis using LS-SVM or ANN models. In this thesis, the potential of 1D SMG in prosthetic control was also investigated. The performances of SMG and surface EMG (SEMG) signal in tracking the guided patterns of wrist extension were evaluated and compared. The subjects (n=16) were instructed to perform the wrist extension under the guidance of displayed sinusoidal, square and triangular waveforms at the movement rates of 20, 30, 50 cycles/min. It was showed that the RMS errors of SMG tracking were significantly smaller than those of SEMG. Significant differences in RMS tracking error of SMG among the three movement rates (p