Jinzhou Medical University, Jinzhou, China.
Front Public Health. 2022 Feb 7;9:832568. doi: 10.3389/fpubh.2021.832568. eCollection 2021.
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.
医学生本科生的健康状况和认知水平，尤其是医疗保健科学观念，对社会和自身的全面发展尤为重要。调查显示，医学生本科生对医疗保健知识的了解明显不足，即使是医学本科生，更不用说非医学本科生了。因此，为医学生本科生开设医疗保健讲座或选修课是一种很好的方式，可以加强本科生对医疗保健的认知，强化医疗保健观念。此外，可以通过分析本科生在医疗保健讲座或选修课中的情绪和精神状态，确定本科生是否有隐藏的疾病，以及他们对医疗保健内容的理解程度。在本研究中，首先提出了一种基于数据挖掘技术的医学生本科生精神状态识别方法。然后，利用基于视觉的表情和姿势来扩展情感识别的渠道，并提出了一种基于人工智能（AI）的医学生医疗保健讲座或选修课中的双通道情感识别模型。最后，利用 TensorFlow 对医学生本科生的精神状态识别和情感识别进行模拟。模拟结果表明，医学生本科生精神状态识别的识别准确率超过 92%，拒绝率和误识别率非常低，精神状态识别的假匹配率和假不匹配率明显优于其他三个基准。双通道情感识别方法的情感识别率超过 96%，有效地整合了面部表情和姿势所表达的情感信息。
