To achieve an objective assessment of driving comfort, this study developed and systematically compared multiple machine learning models based on multimodal physiological signals. 64 individuals had their electrocardiograms (ECGs), surface electromyograms (sEMGs), muscle oxygen saturation (SmO₂), and arterial blood flow signals recorded while they drove. Using comfort scores as labels, six types of models, including support vector machine (SVM), random forest (RF), and adaptive Boosting (AdaBoost), were constructed and optimized with principal component analysis, synthetic minority oversampling technique (SMOTE), and grid search. Results showed that multimodal signal fusion significantly improved classification performance. The SVM model achieved optimal performance with the feature set containing arterial blood flow parameters, reaching an average area under the curve (AUC) of 0.97 and an accuracy of 0.90. RF models consistently exceeded 0.95 AUC with fused signals. Practical signal combinations for portable monitoring were identified: "sEMG + ECG" dual-signal combination (average AUC is 0.90) and "SmO₂ + ECG + sEMG" triple-signal combination (average AUC is 0.92). This study offers empirical support for model selection and signal setup in objective driving comfort evaluation, providing insightful information for the development of in-car health monitoring systems.

In space rescue missions, the rescued personnel may experience food shortages. In order to determine rescue missions, it is essential to examine the behavioral performance and electroencephalogram (EEG) signal changes of healthy adults completing short-term memory and concentration activities while fasting for 21 days. This paper recruited 13 volunteers and had them participate in a 21-day complete fasting experiment divided into 4 experimental stages in an experimental cabin. During this time, volunteers who completed the Two-back paradigm short-term memory task had their performance and resting state EEG data collected. Power spectrum analysis was used to determine the characteristics of the different rhythmic components in the EEG signals in order to assess the volunteers’ memory and attention changes. The results showed that the task response time was significantly prolonged during prolonged fasting (P<0.05), and the change in accuracy was not significant. The reaction speed and concentration level of volunteers gradually weaken, and the impact is most significant around the 16th day; During fasting, δ activity increased, α activity decreased, (δ+θ)/(α+β), θ/α ratio increased, and changes occur during the 10th to 16th day of fasting, and return to normal during the recovery period. Based on EEG and performance data, this paper believes that after 21 days of fasting δ, α, (δ+θ)/(α+β), θ/α changes significantly in the long period of fasting. Based on the features of each rhythmic element, these markers show how volunteers’ basic cognitive function will be impacted by attentional distraction and working memory loss during fasting. The main changes occur from the 10th to the 16th day of fasting. The research results of the paper can be used as supporting data for the assessment of cognitive ability changes during long fasting, and it also provides a reference for further exploring the mechanism of fasting in future aerospace and other fields.