Leveraging Training Strategies of Artificial Neural Network for Classification of Multiday Electromyography Signals

It is essential to have an improved classification accuracy of target hand movements for the electronic prosthesis in order to work efficiently. As a result, twelve different artificial neural networks (ANN) training strategies have been analyzed, and their performances have been compared to discover the optimal training approach for Electromyography (EMG) signals. The proposed framework was also tested on multiday EMG data to assess its scalability performance. A Wearable MYO wristband is used to collect EMG data from eight participants. The experimental results demonstrate that resilient backpropagation can achieve a classification accuracy of 95%; however, it takes 24 seconds to execute and has a hidden layer size (HLS) of 16. Scaled conjugate gradient, on the other hand, obtained 87% classification accuracy with a 3-second execution time and an HLS of 8.