Towards the application of one-dimensional sonomyography for powered upper-limb prosthetic control using machine learning models.
BACKGROUND
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).


METHODS
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.


CONCLUSIONS
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.
New York Chiropractic College, New York, USA. jguo@nycc.edu
Jing-Yi
JY
Guo
Yong-Ping
YP
Zheng
Hong-Bo
HB
Xie
Terry K
TK
Koo
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence / Q000639:trends*; D001186:Artificial Limbs; D011211:Electric Power Supplies; D004576:Electromyography / Q000379:methods*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D011474:Prosthesis Design; D015203:Reproducibility of Results; D014463:Ultrasonography / Q000379:methods*; D014953:Wrist / Q000000981:diagnostic imaging / Q000502:physiology