Train driver fatigue detection based on UMAP and speech multi-feature fusion

Using standardized verbal confirmations by train operators throughout transport operations is pivotal in ensuring railway transportation safety. The call-response speech of train operators can effectively evaluate their driving fatigue status and level of attentiveness. This leads to supervising train operators to uphold the quality of operational activities and control the risks associated with driving safety. In the process of fatigue detection for train drivers using speech technology, the challenges of insufficient fatigue feature extraction and the resulting diminished fatigue detection accuracy caused by the high dimensionality of extracted features were addressed. It proposed a train driver fatigue detection model based on UMAP for the fusion of multiple speech features. First, considering that train drivers entering a state of driving fatigue are primarily influenced by physiological and psychological factors, speech signals containing rich physiological and psychological information were selected as inputs for fatigue detection. Second, fatigue features were extracted from call-response speech signals, encompassing rhythmic, acoustic, spectrogram, and nonlinear dynamic characteristics and their statistical parameter families. The UMAP algorithm was applied to perform feature fusion and dimensionality reduction on the extracted feature matrix, effectively representing high-dimensional data in a lower-dimensional space. The process eliminated redundant information while retaining feature vectors sensitive to fatigue. Then the feature vectors were inputted into the Informer classifier to derive fatigue detection results. Experimental results show that the discriminative capability between awake and fatigued states is significantly improved by the fusion of key features preserved after dimensionality reduction using the UMAP algorithm. In comparison to linear dimensionality reduction algorithms like PCA, KPCA, and manifold dimensionality reduction algorithm T-SNE, the effect is significant. Finally, integrated with the Informer classifier, the model effectively detects the driver's fatigue state with an accuracy of 91.8%. In terms of accurately and robustly identifying train driver fatigue states to ensure operational safety, this model satisfies the practical requirements for train driver fatigue detection applications. © 2024, Central South University Press. All rights reserved.