Abstract
The health status and cognition of undergraduates, especially the scientific concept of healthcare, are particularly important for the overall development of society and themselves. The survey shows that there is a significant lack of knowledge about healthcare among undergraduates in medical college, even among medical undergraduates, not to mention non-medical undergraduates. Therefore, it is a good way to publicize healthcare lectures or electives for undergraduates in medical college, which can strengthen undergraduates' cognition of healthcare and strengthen the concept of healthcare. In addition, undergraduates' emotional and mental state in healthcare lectures or electives can be analyzed to determine whether undergraduates have hidden illnesses and how well they understand the healthcare content. In this study, at first, a mental state recognition method of undergraduates in medical college based on data mining technology is proposed. Then, the vision-based expression and posture are used for expanding the channels of emotion recognition, and a dual-channel emotion recognition model based on artificial intelligence (AI) during healthcare lectures or electives in a medical college is proposed. Finally, the simulation is driven by TensorFlow with respect to mental state recognition of undergraduates in medical college and emotion recognition. The simulation results show that the recognition accuracy of mental state recognition of undergraduates in a medical college is more than 92%, and the rejection rate and misrecognition rate are very low, and false match rate and false non-match rate of mental state recognition is significantly better than the other three benchmarks. The emotion recognition of the dual-channel emotion recognition method is over 96%, which effectively integrates the emotional information expressed by facial expressions and postures.
Highlights
At present, the healthcare system of undergraduates in China is not well-established, especially in medical colleges where the healthcare system should be perfect [1]
To solve the problems in the process of mental state recognition of undergraduates and improve the accuracy of mental state recognition of undergraduates in medical college, this study proposes a mental state recognition method of undergraduates in medical college based on data mining technology
Three methods are selected for mental state recognition comparison on the same test platform, which are M-SVM2 [26], post processing procedure (PoPP) [27], and monitoring agent based on multimodal data (MD-IMA) [28], and three benchmarks can be used for the simulation of emotion recognition
Summary
The healthcare system of undergraduates in China is not well-established, especially in medical colleges where the healthcare system should be perfect [1]. Undergraduates are at the junction of school and society, and in the transition period from minors to adults. In this special period, if they do not have a good attitude and sufficient knowledge reserve, they are likely to make wrong decisions and suffer all kinds of harm. The study of healthcare knowledge for undergraduates is significant for themselves and the whole society. It can maintain their own health, avoid various diseases and accidents, and reduce social losses [3,4,5]
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