Nowadays, gestures are being adopted as a new modality in the field of Human-Computer Interaction (HMI), where the physical movements of the whole body can perform unlimited actions. Soundpainting is a language of artistic composition used for more than forty years. However, the work on the recognition of SoundPainting gestures is limited and they do not take into account the movements of the fingers and the hand in the gestures which constitute an essential part of SoundPainting. In this context, we conducted a study to explore the combination of 3D postures and muscle activity for the recognition of SoundPainting gestures. In order to carry out this study, we created a SoundPainting database of 17 gestures with data from two sensors (Kinect® and Myo™). We formulated four hypotheses concerning the accuracy of recognition. The results allowed to characterize the best sensor according to the typology of the gesture, to show that a "simple" combination of the two sensors does not necessarily improves the recognition, that a combination of features is not necessarily more efficient than taking into account a single well chosen feature, finally, that changing the frequency of the data acquisition provided by these sensors does not have a significant impact on the recognition of gestures.