A Model for Lumbar EMG Signal Recognition Based on Stacking Integration Learning

Human waist action recognition based on surface electromyography (sEMG) signals is a complex pattern recognition problem, the difficulty of which lies in the coupling of EMG signals between different actions, and a single model cannot effectively utilize the feature information to achieve high accuracy of action recognition. To this end, this article proposes a stacking integration-based EMG signal model for the waist, which introduces the stacking integration framework into EMG signal recognition for the first time, using ${K}$ -nearest neighbor (KNN), random forest (RF)-master, XGboost, and LightGBM algorithms as base learners and decision tree (DT) model as a meta-learner to construct the integrated model. The complex feature information contained in the EMG signal is correlated with different waist movements to construct feature datasets with high correlation, and the training set is divided by using the fivefold cross-validation method to reduce the overfitting problem during the repeated training of the integrated model. Using the integrated model proposed in this article, the generalization ability of the model can be effectively improved and the recognition accuracy of EMG signals can be improved. According to experiments, the model has an average accuracy of 94.98% for the recognition of six waist movements in normal person, and the accuracy of recognition of the six movements in patients with lumbar strain was 91.5% on average, which is about 6% better than a single model, and the recognition speed can be within 150 ms, meeting the requirements of real-time recognition.