Abstract: As a new generation of robot human-computer interaction interface equipment, surface EMG sensors have shown great application potential and value in various environments such as aerospace, military applications, rehabilitation medicine, and industrial production. It is found that when it faces problems such as sensor displacement and user changes, the accuracy of action recognition will drop sharply, and the reusability of the model will be poor. In view of this situation, a migration learning modeling method based on small auxiliary sets is proposed. The MMD algorithm is used to evaluate the high-dimensional distance between the source domain data set and the target domain data set, and their edge distribution difference is reduced by the TCA algorithm, and a small auxiliary set is introduced to create pseudo-labels for the data set to be tested, which improves the lack of similarity of data condition distribution in the analysis of migration components. Several subjects were used as research objects to verify the adaptability and rationality of the proposed algorithm. The electromyographic control experiments show that the subjects only need a small amount of training in the new scenario (only 4% of the source field data), and the accuracy of the migration learning fusion model can be increased to more than 80%.

Key words: transfer learning, sEMG, intention recognition, human-computer interaction