Mental States Recognition Using Direct and Indirect Multi-Class Support Vector Machines

Hendel, Mounia; Hendel, Fatiha

doi:10.1109/ssd.2018.8570468

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…The sample data distribution of the simulation of mental state recognition signals of undergraduates in a medical college is shown in Table 1. Additionally, three methods are selected for mental state recognition comparison on the same test platform, which are M-SVM2 (26), PoPP (27), and MD-IMA (28), and three benchmarks can also be used for the simulation of emotion recognition.…”

Section: Experiments and Results Analysis Setupmentioning

confidence: 99%

“…In (25), a driving fatigue detection method based on partially oriented coherent graph CNN is proposed. In (26), a quadratic loss multi-class support vector machine (M-SVM2) was proposed, which considered all categories at the same time and classified five mental tasks by EEG signals. In (27), a post processing procedure (PoPP) was formulated to overcome the problem of static classification.…”

Section: Related Studymentioning

confidence: 99%

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AI-Based Publicity Strategies for Medical Colleges: A Case Study of Healthcare Analysis

Wang

2022

Front. Public Health

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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.

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Section: Experiments and Results Analysis Setupmentioning

confidence: 99%