Аңдатпа
This study investigates the applicability of Electromyography (EMG) signal classification algorithms with relatively low training time to control prosthetic devices. The perceived quality of control depends on many factors, such as the 1) accuracy of the algorithm, 2) the complexity of the control, and 3) the ability to compensate for the error. The high granularity of control in the time domain reduces the perceived effect of error but also limits the classification accuracy. This work aims to find the borderline for the accuracy of algorithms to be selected as a control strategy for hand prosthetic devices and thus shorten the gap between laboratory devices and commercially available devices. In particular, we compared five classification algorithms and selected one for real-time testing. The results from a test conducted on four subjects showed that the EMG-based control strategy has comparable performances with an IMU-based controller.