Online Hand Gesture Recognition Using Surface Electromyography Based on Flexible Neural Trees

Normal hand gesture recognition methods using surface Electromyography (sEMG) signals require designers to use digital signal processing hardware or ensemble methods as tools to solve real time hand gesture classification. These ways are easy to result in complicated computation models, inconvenience of circuit connection and lower online recognition rate. Therefore it is imperative to have good methods which can avoid the problems mentioned above as more as possible. An online hand gesture recognition model by using Flexible Neural Trees (FNT) and based on sEMG signals is proposed in this paper. The sEMG is easy to record electrical activity of superficial muscles from the skin surface which has applied in many fields of treatment and rehabilitation. The FNT model can be created using the existing or modified tree- structure- based approaches and the parameters are optimized by the PSO algorithm. The results indicate that the model is able to classify six different hand gestures up to 97.46% accuracy in real time.