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

Abstract

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<0.01) for all the guiding waveforms were also observed using one-way analysis of variance (ANOVA). The results suggest that SMG signal has great potential to be an alternative method to SEMG to evaluate muscle function and control prostheses. We further compared subjects' performance using 1D SMG and surface EMG in a series of discrete tracking tasks, both with and without a concurrent auditory attention task. The performances of subjects (n=10) were evaluated under isometric contraction and wrist extension using the extensor carpi radiali muscle. Using SMG generated significantly lower numbers of "E" wrongly canceled than using EMG for both isometric contraction and wrist extension (p<0.001) controls. It also demonstrated that there was no significant difference of performances of canceling "E" between the single and dual tasks by using any of the control signals (p=1.0). The SMG control provided more consistent performances under the single and dual tasks in comparison with EMG control. In addition, the feasibility of using 1D SMG signal for controlling a powered prosthesis was investigated by evaluating the performances of subjects (n=16) in tracking guided motion patterns of wrist extension. The RMS tracking error between the guiding waveform and the signal representing the degree of the prosthetic hand's open-close level, which was measured by an electronic goniometer, was calculated to evaluate the control performance. The results suggest that SMG signal, based on further improvement, has potentials to be an alternative method for prosthetic control. Finally, an amputee subject was recruited to perform the tasks of tracking the guided patterns of wrist extension, discrete tracking task and controlling a powered prosthesis. The performance of the subject using 1D SMG and EMG signals were evaluated to see whether 1D SMG signal could really used by the amputee. The feasibility of using 1D SMG by the amputee was demonstrated. To sum up, we have successfully demonstrated that SMG (related to muscle architectural properties) can provide complementary information about muscle function in comparison with EMG (related to muscle bioelectrical properties), which has been commonly used for muscle activity assessment and prosthetic control.