A CW-CNN regression model-based real-time system for virtual hand control.

For upper limb amputees, wearing a myoelectric prosthetic hand is the only way for them to continue normal life. Even until now, the proposal of a high-precision and natural performance real-time control system based on surface electromyography (sEMG) signals is still challenging. Researchers have proposed many strategies for motion classification or regression prediction tasks based on sEMG signals. However, most of them have been limited to offline analysis only. There are even few papers on real-time control based on deep learning models, almost all of which are about motion classification. Rare studies tried to use deep learning-based regression models in real-time control systems for multi-joint angle estimation via sEMG signals. This paper proposed a CW-CNN regression model-based real-time control system for virtual hand control. We designed an Adaptive Kalman Filter to smooth the joint angles output before sending them as control commands to control a virtual hand. Eight healthy participants were invited, and three sessions experiments were conducted on two different days for all of them. During the real-time experiment, we analyzed the joint angles estimation accuracy and computational latency. Moreover, target achievement control (TAC) test was applied to emphasize motion regression in real-time. The experimental results show that the proposed control system has high precision for 3-DOFs motion regression in simultaneously, and the system remains stable and low computational latency. In the future, the proposed real-time control system can be applied to actual prosthetic hand.

Evaluation of Computer-Based Target Achievement Tests for Myoelectric Control.

Real-time evaluation of novel prosthetic control schemes is critical for translational research on artificial limbs. Recently, two computer-based, real-time evaluation tools, the target achievement control (TAC) test and the Fitts' law test (FLT), have been proposed to assess real-time controllability. Whereas TAC tests provides an anthropomorphic visual representation of the limb at the cost of confusing visual feedback, FLT clarifies the current and target locations by simplified non-anthropomorphic representations. Here, we investigated these two approaches and quantified differences in common performance metrics that can result from the chosen method of visual feedback. Ten able-bodied and one amputee subject performed target achievement tasks corresponding to the FLT and TAC test with equivalent indices of difficulty. Able-bodied subjects exhibited significantly (p <0.05) better completion rate, path efficiency, and overshoot when performing the FLT, although no significant difference was seen in throughput performance. The amputee subject showed significantly better performance in overshoot at the FLT, but showed no significant difference in completion rate, path efficiency, and throughput. Results from the FLT showed a strong linear relationship between the movement time and the index of difficulty (R2 = 0.96), whereas TAC test results showed no apparent linear relationship (R2 = 0.19). These results suggest that in relatively similar conditions, the confusing location of virtual limb representation used in the TAC test contributed to poorer performance. Establishing an understanding of the biases of various evaluation protocols is critical to the translation of research into clinical practice.