Quantifying pattern recognition-based myoelectric control of multifunctional transradial prostheses - PubMed (original) (raw)

Quantifying pattern recognition-based myoelectric control of multifunctional transradial prostheses

Guanglin Li et al. IEEE Trans Neural Syst Rehabil Eng. 2010 Apr.

Abstract

We evaluated real-time myoelectric pattern recognition control of a virtual arm by transradial amputees. Five unilateral patients performed 10 wrist and hand movements using their amputated and intact arms. In order to demonstrate the value of information from intrinsic hand muscles, this data was included in EMG recordings from the intact arm. With both arms, motions were selected in approximately 0.2 s on average, and completed in less than 1.25 s. Approximately 99% of wrist movements were completed using either arm; however, the completion rate of hand movements was significantly lower for the amputated arm (53.9% +/- 14.2%) than for the intact arm ( 69.4% +/- 13.1%). For the amputated arm, average classification accuracy for only 6 movements-including a single hand grasp-was 93.1% +/- 4.1%, compared to 84.4% +/- 7.2% for all 10 movements. Use of 6 optimally-placed electrodes only reduced this accuracy to 91.5% +/- 4.9%. These results suggest that muscles in the residual forearm produce sufficient myoelectric information for real-time wrist control, but not for performing multiple hand grasps. The outcomes of this study could aid the development of a practical multifunctional myoelectric prosthesis for transradial amputees, and suggest that increased EMG information-such as made available through targeted muscle reinnervation-could improve control of these prostheses.

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Figures

Fig. 1

Fig. 1

Placement of 12 bipolar electrodes for EMG recordings on (a) an amputated arm and (b) an intact arm.

Fig. 2

Fig. 2

The graphical user interface used for real-time testing. The prompted movement is shown on the right and the virtual arm is shown on the left.

Fig. 3

Fig. 3

Calculation of real-time performance metrics. Movement onset, motion-selection time, and motion-completion time were measured with respect to classifier decisions. Movement onset was also related to the magnitude of the mean absolute (abs.) value of the recorded EMG signals. Each target movement started from a state of rest (no movement). The classifier made a motion prediction every 100 ms.

Fig. 4

Fig. 4

Classification accuracy for 11 movement classes. (a) The classification accuracies for five transradial subjects from three trials. (b) Classification accuracies for wrist and hand movements for the amputated (amp.) arm (trial 3). Error bars denote standard deviation.

Fig. 5

Fig. 5

Time histograms for (a) motion-selection time and (b) motion-completion time for the amputated arms (left panels) and intact arms (right panels). The vertical axes represent the percentage of attempted movements selected or completed within times bins of 0.1 s and 0.5 s, respectively.

Fig. 6

Fig. 6

Motion-completion rate versus time for (a) amputated and intact arms and (b) wrist and hand of amputated arms.

Fig. 7

Fig. 7

Classification accuracy versus number of surface electrodes for 6 or 10 movement classes as measured on (a) the amputated arms and (b) the intact arms.

References

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