Lower limb motion intention recognition using multi-source able-bodied gait signals.

Objective. Accurate motion intention recognition is challenging due to the limited use of distributed muscle signals. In this study, we proposed a framework that integrates musculoskeletal modelling, advanced feature processing, and feature selection to enhance recognition performance.Approach. A muscle screening method was developed to identify the most informative muscles for joint motion representation. Nonlinear features were extracted from surface electromyography signals using variational mode decomposition and fused with linear features derived from joint angles and moments. To reduce feature redundancy, a Random Forest with Bagging (RF-Bagging) algorithm was applied for feature selection. Finally, a long short-term memory regression model was trained for continuous prediction of joint angles and moments.Main results. Experiments on 20 subjects showed that using optimally screened muscles significantly improved prediction accuracy. For knee joint predictions (angle/moment), we achieved MAE of 2.63 ± 0.68 deg/0.020 ± 0.006Nm kg-1, RMSE of 4.03 ± 1.13 deg/0.029 ± 0.010Nm kg-1,R2of 0.93 ± 0.05/0.95 ± 0.036, MSE of 17.49 ± 10.78 deg/0.0009 ± 0.0006Nm kg-1, and RPD of 1.011 ± 0.049/1.019 ± 0.034, while ankle joint predictions showed MAE of 1.32 ± 0.31 deg/0.035 ± 0.012Nm kg-1, RMSE of 1.72 ± 0.43 deg/0.050 ± 0.020Nm kg-1,R2of 0.93 ± 0.03/0.98 ± 0.021, MSE of 3.13 ± 1.47 deg/0.002 ± 0.002Nm kg-1, and RPD of 1.004 ± 0.051/1.020 ± 0.040.Significance. Our findings demonstrate that that integrating optimal muscle selection, nonlinear feature enhancement, and intelligent feature selection significantly improves motion intention recognition.