A User-Specific Hand Gesture Recognition Model Based on Feed-Forward Neural Networks, EMGs, and Correction of Sensor Orientation

Abstract

Hand gesture recognition systems have several applications including medicine and engineering. A gesture recognition system should identify the class, time, and duration of a gesture executed by a user. Gesture recognition systems based on electromyographies (EMGs) produce good results when the EMG sensor is placed on the same orientation for training and testing. However, when the orientation of the sensor changes between training and testing, which is very common in practice, the classification and recognition accuracies degrade significantly. In this work, we propose a system for recognizing, in real time, five gestures of the right hand. These gestures are the same ones recognized by the proprietary system of the Myo armband. The proposed system is based on the use of a shallow artificial feed-forward neural network. This network takes as input the covariances between the channels of an EMG and the result of a bag of five functions applied to each channel of an EMG. To correct the rotation of the EMG sensor, we also present an algorithm based on finding the channel of maximum energy given a set of synchronization EMGs, which for this work correspond to the gesture waveout. The classification and recognition accuracies obtained here show that the recognition system, together with the algorithm for correcting the orientation, allows a user to wear the EMG sensor in different orientations for training and testing, without a significant performance reduction. Finally, to reproduce the results obtained in this paper, we have made the code and the dataset used here publicly available.