To enhance the diversity and simplicity of hand gesture recognition, an approach based on ElectroMyoGraphy (EMG) and ACCeleration(ACC) signals was proposed to recognize dynamic gestures. Firstly, the gesture related information was collected by MYO sensors. Then, the dimensionality of ACC signal was reduced and the preprocessing of EMG was done. Finally, to reduce the number of training samples,the posture based on ACC signal was recognized by using Collaborative Sparse Representation (CSR) and the gesture based on EMG signal was classified by using Dynamic Time Warping (DTW) algorithm and the K-Nearest Neighbor ( KNN) Classifier. When the ACC signal was identified by using CSR, the optimal number of samples and the dimensions of the dimensionality reduction were studied to reduce the complexity of gesture recognition. The experimental results show that the average recognition accuracy of the EMG for the hand gesture tested reaches 99.17%; the ACC signal for four postures achieve 96.88%. The recognition accuracy for the 12 dynamic gestures reaches 96.11%. This method has high recognition accuracy and fast calculation speed for dynamic gestures.