Motion intention recognition using surface electromyography and arrayed flexible thin-film pressure sensors

Motion intention recognition is considered the key technology for enhancing the training effectiveness of upper limb rehabilitation robots for stroke patients, but traditional recognition systems are difficult to simultaneously balance real-time performance and reliability. To achieve real-time and accurate upper limb motion intention recognition, a multi-modal fusion method based on surface electromyography (sEMG) signals and arrayed flexible thin-film pressure (AFTFP) sensors was proposed. Through experimental tests on 10 healthy subjects (5 males and 5 females, age 23±2 years), sEMG signals and human-machine interaction force (HMIF) signals were collected during elbow flexion, extension, and shoulder internal and external rotation. The AFTFP signals based on dynamic calibration compensation and the sEMG signals were processed for feature extraction and fusion, and the recognition performance of single signals and fused signals was compared using a support vector machine (SVM). The experimental results showed that the sEMG signals consistently appeared 175±25 ms earlier than the HMIF signals (<italic>p</italic>&lt;0.01, paired <italic>t</italic>-test). In offline conditions, the recognition accuracy of the fused signals exceeded 99.77% across different time windows. Under a 0.1 s time window, the real-time recognition accuracy of the fused signals was 14.1% higher than that of the single sEMG signal, and the system’s end-to-end delay was reduced to less than 100 ms. The AFTFP sensor is applied to motion intention recognition for the first time. And its low-cost, high-density array design provided an innovative solution for rehabilitation robots. The findings demonstrate that the AFTFP sensor adopted in this study effectively enhances intention recognition performance. The fusion of its output HMIF signals with sEMG signals combines the advantages of both modalities, enabling real-time and accurate motion intention recognition. This provides efficient command output for human-machine interaction in scenarios such as stroke rehabilitation.