Lateral walking gait phase recognition for hip exoskeleton by denoising autoencoder-LSTM

Lateral resistance walk is an effective way to strengthen the abductor muscles of the hip. Accurate lateral walking gait recognition is the prerequisite for exoskeletons to be applied to lateral walking exercises. This paper proposes a denoising autoencoder-LSTM (DAE-LSTM) algorithm for lateral walking gait recognition. Nine sets of IMU data at three speeds and three strides of ten subjects were collected. Four lateral walking gait phases of narrow double support (NDS), guided foot swing (GFS), wide double support (WDS) and following leg swing (FLS) were recognized. The recognition performance of random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), neural networks (NN) and DAE-LSTM were compared. The average cross-subject recognition accuracy of DAE-LSTM was 90.2 %, which was higher than the other four models and previous work. For each frame of IMU data, the average recognition time of DAE-LSTM is 0.383 ms, which is 5.32 ms higher than the previous work. When the signal-to-noise ratio (SNR) is greater than 100:1, the accuracy of the DAE-LSTM model is higher than 90.0 %, and the accuracy of the other four models were less than 85 %. The results show that the proposed algorithm can achieve the requirements of recognition accuracy, model recognition time and model robustness for application in exoskeleton.