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In lower-limb rehabilitation equipment, the prediction of the knee joint moment using surface electromyography signals is an important method of motion intention recognition. To improve the viability of control by human–computer interactions and to reduce the complexity of the knee joint moment prediction model, this paper presents a prediction model for knee joint moment based on artificial neural networks, in which the knee joint angle, the knee joint angular velocity, and a pair of surface electromyography signals from the antagonistic and agonistic muscles of the knee joint are selected as inputs. Two public databases that include the walking data of hemiplegic patients and healthy people are used to test the effect of muscle pair selection on knee joint moment prediction under non-isometric contraction. The dependence of the model on speed and the individual is also tested. The correlation coefficient and the mean absolute error are used as performance indicators. The results demonstrate that the proposed model can predict the knee joint moment well. Across the difference of speeds and subjects, the choice of muscle pair has no significant effect on the prediction of the knee joint moment. Compared with previous research, the proposed model simplifies the measurement parameters and the signal processing process, reducing the number of sensors used in practical applications, which increases the safety and the fluency of the lower-limb movement.

Prediction of Knee Joint Moment by Surface Electromyography of the Antagonistic and Agonistic Muscle Pairs