Article

The inherent properties of surface electromyography limit its
potential for multi-degrees of freedom control. Our previous studies demonstrated
that wrist angle could be predicted by muscle thickness measured from B-mode
ultrasound, and hence, it could be an alternative signal for prosthetic control.
However, an ultrasound imaging machine is too bulky and expensive. OBJECTIVE : We
aim to utilize a portable A-mode ultrasound system to examine the feasibility of
using one-dimensional sonomyography (i.e. muscle thickness signals detected by
A-mode ultrasound) to predict wrist angle with three different machine learning
models - (1) support vector machine (SVM), (2) radial basis function artificial
neural network (RBF ANN), and (3) back-propagation artificial neural network (BP
ANN). STUDY DESIGN: Feasibility study using nine healthy subjects. METHODS: Each
subject performed wrist extension guided at 15, 22.5, and 30 cycles/minute,
respectively. Data obtained from 22.5 cycles/minute trials was used to train the
models and the remaining trials were used for cross-validation. Prediction
accuracy was quantified by relative root mean square error (RMSE) and correlation
coefficients (CC). RESULTS: Excellent prediction was noted using SVM (RMSE = 13%,
CC = 0.975), which outperformed the other methods. CONCLUSION: It appears that
one-dimensional sonomyography could be an alternative signal for prosthetic
control. Clinical relevance Surface electromyography has inherent limitations
that prohibit its full functional use for prosthetic control. Research that
explores alternative signals to improve prosthetic control (such as the
one-dimensional sonomyography signals evaluated in this study) may revolutionize
powered prosthesis design and ultimately benefit amputee patients.

Langue : ANGLAIS