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

Jing Yi Guo, Yongping Zheng, Hong Bo Xie, Terry K. Koo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

16 Citations (Scopus)

Abstract

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). 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.
Original languageEnglish
Pages (from-to)43-49
Number of pages7
JournalProsthetics and Orthotics International
Volume37
Issue number1
DOIs
Publication statusPublished - 11 Mar 2013

Keywords

  • Artificial neural network (ANN)
  • back-propagation (BP)
  • one-dimensional sonomyography (1D SMG)
  • radial basis function (RBF)
  • skeletal muscles
  • support vector machine (SVM)
  • surface electromyography (SEMG)
  • ultrasound
  • wrist angle

ASJC Scopus subject areas

  • Health Professions (miscellaneous)
  • Rehabilitation

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