Abstract:Objective To propose a dynamic gesture recognition method based on multi-stream convolutional neural network using
surface electromyography signal. Methods On the basis of surface electromyography signal, acceleration and gyroscope signals
were introduced to characterize the motion characteristics of gestures, and a three-branch multi-flow convolutional neural
network structure adapted to the input signals was presented. Results The experiment of a real self-built database showed that the
multi-stream convolutional neural network had a good recognition effect on dynamic sign language isolated words represented by
multi-category data and would not generate over-fitting phenomenon due to the difference of data categories. Conclusion This
method can effectively identify the dynamic gestures represented by multi-category data and has certain innovation. This structure
can be used as a reference for natural language signals represented by other multi-category data.
王增峥,洪昕. 基于多流卷积神经网络的动态手势识别[J]. 中国医疗设备, 2019, 34(10): 20-22.
WANG Zengzheng, HONG Xin. Dynamic Gesture Recognition Based on Multi-Stream Convolutional Neural Network. China Medical Devices, 2019, 34(10): 20-22.