Multi-Sensor and Deep Learning based Ankle Joint Motion Intention Prediction under Diverse Gait Speed Conditions

Motion intention prediction is an essential challenge in improving the efficacy of exoskeleton human-machine interactions. Traditional gait phase detection and motion pattern recognition methods can only identify the current gait state, lacking the ability to adjust motion parameters based on step speed dynamically, and real-time performance is difficult to guarantee. In this paper, we proposed a deep learning model TCN-LSTM, which integrated Time Convolutional Network (TCN) and Long Short-Term Memory Network (LSTM), to predict human ankle joint angles under different walking speeds based on data from Inertial Measurement Unit (IMU) and Goniometer (GON). We selected 5 subjects in a public dataset for experimental validation. Experimental results demonstrated that the proposed model could accurately and rapidly predict ankle joint angles. The MAE of the individual-specific model was 1.191°, outperforming the 3 baseline models: LSTM (1.750°), Gated Recurrent Unit (GRU) (1.653°), and TCN (1.340°). The Mean Absolute Error (MAE) of the general model is reduced by 14.3% compared to the 3 baseline models. The prediction error of TCN-LSTM with GON is reduced by 13.69%. Furthermore, the average prediction time of our model is only 3.7ms. Therefore, the accuracy and real-time performance of the model can meet the continuous prediction of human ankle joint angle.