Non-Ideal sEMG Gesture Recognition Based on DANN Under Electrode Offset

The traditional program control method limits the flexibility of human-computer interaction, especially in robot interaction systems that come into direct contact with the human body. sEMG signal is a relatively flexible control signal, however, sEMG signal sensors may experience positional changes due to human motion, which can affect signal acquisition. To address this issue, this paper proposes a DANN method based on adversarial thinking to solve the problem of non-ideal sEMG gesture recognition on SeNic-Main dataset. Through adversarial thinking, 8 gesture actions under electrode offset were transferred and classified, achieving a maximum improvement of 9%. The results indicate that DANN can effectively solve the problem of non ideal sEMG gesture recognition.