Real-time simultaneous myoelectric control of multiple degrees of freedom
University of New Brunswick
Natural limb control during activities of daily living involves simultaneous motions of multiple degrees of freedom (DOFs) (such as feeding or grooming, which require simultaneous activation of hand, arm, and wrist in multiple directions). A strategy for simultaneous myoelectric control is to train the system with EMG as the input and the corresponding joint force or position (angle) as the target. For prosthesis control, since it is not possible to measure force/position from an absent limb, a strategy called mirrored bilateral training was proposed (with unilateral amputees) in previous work, in which force/position was recorded from the opposite limb during mirrored contractions. In this work, the effect of alternative feature sets, estimators, feature projection, and coordinate systems on estimation performance was studied with force and position based paradigms. Furthermore, a real-time control test was performed to assess the system usability. It was shown that when the EMG (and effort) levels were similar, no significant difference (p>0.1) was found between the force and position based methods in both offline and real-time control tests. A novel training paradigm for simultaneous control is described, in which users were prompted to synchronize their contractions with a moving target cursor on a computer screen. The cursor displacements were used as targets to train the estimators. The system usability was assessed with a real-time control test, and the performance was found to be equivalent (p>0.1) to that of the mirrored bilateral training. The proposed visual target based training is more practical than mirrored training because it does not require force and position sensing equipment, and can be potentially used by both unilateral and bilateral amputees. Finally, a novel application of a support vector machine (SVM) was evaluated in simultaneous myoelectric real-time control of DOFs. It was shown with able-bodied and amputee subjects that the proposed SVM based method outperformed the widely used multilayer perceptron artificial neural network (ANN) in a Fitts’ law style real-time control test. Moreover, the processing time required for training and estimation with the SVM was significantly lower than that of the ANN. This approach is shown to provide a robust and computationally efficient system for simultaneous and proportional myoelectric control.