ENHANCED LOCOMOTION MODE RECOGNITION FOR LOWER LIMB EXOSKELETONS USING EMD DATA AUGMENTATION AND BiTCN-CNN

Human locomotion mode recognition plays a crucial role in the effective operation of lower limb exoskeletons. This paper proposes a method that integrates Empirical Mode Decomposition (EMD) with data enhancement and bidirectional temporal convolutional networks and convolutional neural networks (BiTCN-CNN), addressing the challenges of low recognition accuracy and overfitting in small-sample training. The CatBoost algorithm selects key features from inertial measurement unit (IMU) data to perform locomotion mode recognition. EMD decomposes the signal into intrin-sic mode functions (IMFs) and progressively extracts the residuals, thereby enhancing the angular data while increasing the diversity and representativeness of the dataset. Subsequently, BiTCN-CNN models are employed to recognize various movement patterns. Motion data from 12 healthy subjects were collected, enhanced using EMD, and subsequently input into a BiTCN-CNN model for training and testing. The performance was compared with traditional recognition algorithms, such as Convolutional Neural Networks (CNNs), Temporal Convolutional Networks (TCN), and Long Short-Term Memory (LSTM) networks. The experimental results demonstrated that the method, combining EMD data augmentation and BiTCN-CNN, improved recognition accuracy under small sample conditions. Notably, for the five stable locomotion modes (level ground walking (LW), stair ascent (SA), stair descent (SD), ramp ascending (RA), and ramp descending (RD)), the recognition accuracy reached 99.27%. Furthermore, the model also demonstrated promising performance in eight pattern transition tasks (including LW-SA, LW-SD, LW-RA, LW-RD, SA-LW, SD-LW, RA-LW, RD-LW), achieving an accuracy of 97.27%.