Sensory Interference Recognition Based on NRBO-CNN-SE-LSTM

Abstract

Human walking balance relies on proprioception, vision, and the vestibular sense. Dysfunction in any of these systems can lead to unstable gait. Accurately identifying sensory disturbances is key to understanding the causes of sensory dysfunction. Most existing studies focus on disturbances in a single sensory system and lack methods to identify multiple disturbance types. This paper proposes a recognition method based on Newton–Raphson–based optimizer (NRBO)-convolutional neural network (CNN)-squeeze-and-excitation (SE)-long short-term memory (LSTM). The original feature set is filtered through multiple rounds of hybrid filtering-wrapping feature selection algorithm (HLOA-HFWA) to obtain a feature subset. The union of these features forms an integrated feature (IF) set. This set is used as input to a CNN-SE-LSTM model designed for multiclassification. The Newton–Raphson method (NRBO) is then used to optimize key hyperparameters, including learning rate (LR), LSTM hidden node number, and L2 regularization (L2R) coefficient. This improves the network’s feature extraction and pattern recognition capabilities. Experiments on public IF sets and gait-IF sets show that NRBO-CNN-SE-LSTM outperforms other models in both sensory disturbance recognition and overall performance.