Bidirectional Mamba-Based Continuous Prediction of Human Motion Intention Using Multisource Information Fusion

This paper proposes a Bidirectional Mamba-based selective state space model (SSM) for continuously predicting human motion intention using multisource information fusion of surface electromyogram (sEMG) and mechanomyography (MMG). In the proposed prediction model, the bidirectional canning mechanism and the dynamic selection mechanism make the prediction model have global context modeling capability and optimal allocation of computing resources, and the SSM makes the prediction model maintain linear computational complexity. By integrating the complementary features of sEMG and MMG through multimodal signal fusion, the deep information of motion intention is deeply explored, significantly enhancing the model’s intention prediction ability. The effectiveness and superiority of the proposed Bidirectional Mamba-based human motion prediction model are validated in comparison with the information fusion models based on recurrent neural network (RNN), long short-term memory network (LSTM), and temporal convolutional network (TCN).