sEMG-Based Gesture Classifier Through DTW and Enhanced Muscle Activity Detection

Prosthetic hands are of paramount importance in the rehabilitation of upper limbs amputees. Gesture recognition using surface electromyography (sEMG) data has emerged as a great option for controlling prosthetic devices, since these data are acquired by non-intrusive sensors. This work presents a real-time classification system based on artificial neural networks with individualized muscle activity segmentation and using dynamic time warping (DTW) based features. For real-time classification, we modify the size of the sliding windows so that their length is sufficient to fully capture the muscle activity signal. For data segmentation, we propose an enhanced muscle activity detection in which validation is used to fine-tune the thresholds needed to determine the beginning and end of muscle contraction. We used two validation methods: cross-validation and multi-holdout. Moreover, we propose a post-processing technique to choose the most representative class when there are multiple classifications for a given data. By combining all the proposed techniques, the accuracy of the resulting system was (97.2 ± 0.3)% in the classification of 6 hand gestures from 10 healthy people, representing an increase of 7.1% in the mean accuracy compared with the baseline model.