Research on Movement Intention Recognition Based on CNN-LSTM

Existing methods for recognizing motion intent in lower limb rehabilitation robots focus on spatial feature extraction while neglecting movement continuity, thus failing to extract temporal features. This paper proposes a movement intention recognition model based on a CNN-LSTM parallel dual-stream spatio-temporal neural network, taking surface electromyography (sEMG) signals as the core data. This model concurrently extracts temporal and spatial features from sEMG signals, integrating dual-dimensional information to comprehensively explore deep signal characteristics. By overcoming the limitations of traditional single-feature extraction, it significantly enhances recognition accuracy. Experimental results from movement intention recognition studies involving multiple subjects demonstrate an average recognition accuracy of 97%, providing reliable technical support for precise intent recognition and human–robot collaborative control in lower limb rehabilitation robots.