sEMG signal of prosthetic arm control system

this is dataset of sEMG. raw data include four columns, which are channel 1, channel 2, channel 3, and the reference channel. corresponding thesis proposes the integration of a dynamic time regularization algorithm to enhance gesture recognition detection accuracy and real-time system performance. The application of the dynamic time warping algorithm allows the fusion of three sEMG signals, enabling the calculation of similarity between the sample and the model. This process facilitates gesture recognition and ensures effective communication between individuals and the 3D printed prosthesis. Utilizing this algorithm, the best feature model was generated by amalgamating six types of gesture classification models. Experimental tests demonstrate that the accuracy of gesture recognition and prosthetic limb control using the temporal dynamic regularization.