关键词: 实时识别, 表面肌电信号, 柔性神经树, 均方根, 方均根误差

Abstract: Traditional hand gesture recognition methods require designers to use digital signal processing hardware or ensemble methods as tools to solve real time hand gesture classification. These methods are easy to result in complicated computational models, complex circuit connection and lower online recognition rate. A real-time hand gesture recognition model by combining Flexible Neural Trees（FNT） and surface Electromyography（sEMG） signals is proposed. sEMG is non-invasive, easy to record signal of superficial muscles from the skin surface, which is applied widely in many fields of human motion recognition and diagnosis. The FNT model is generated and evolved based on the pre-defined simple instruction sets, which can solve highly structure dependent problem of the Artificial Neural Network（ANN）. FNT method not only can avoid complicated computation and inconvenience of circuit connection but also has a higher real-time recognition rate and lower Root Mean Square Error（RMSE）. Testing is conducted with six participants to test six hand gestures. The results indicate that the model is a practicable method and has a higher real-time recognition rate.

Key words: real-time recognition, surface Electromyography（sEMG）, Flexible Neural Trees（FNT）, root mean square, root mean square error