Identify Arm Motions Using EMG Signals and Deep Learning.

This example employs sequence-to-sequence classification with an LSTM network to detect arm motions from EMG signals, achieving an 84% accu

This example employs sequence-to-sequence classification with an LSTM network to detect arm motions from EMG signals, achieving an 84% accuracy. Misclassifications primarily occurred between hand open and wrist extension, and hand close and wrist flexion, attributed to overlapping muscle activation patterns and electrode placement bias towards muscles involved in wrist flexion.