| Id | 2582 | |
| Author | Lu D. | |
| Title | Evaluation Model of Music Therapy's Auxiliary Effect on Mental Health Based on Artificial Intelligence Technology | |
| Reference | Lu D. Evaluation Model of Music Therapy's Auxiliary Effect on Mental Health Based on Artificial Intelligence Technology,Journal of Environmental and Public Health 2022 |
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| Keywords | Artificial Intelligence; Humans; Mental Health; Music; Music Therapy; Reproducibility of Results; Technology; article; artificial intelligence; controlled study; extraction; hearing; human; human experiment; independent variable; learning; leisure; mental health; music therapy; physiological stress; quantitative analysis; questionnaire; reliability; social problem; teacher; thalamus; artificial intelligence; mental health; music; procedures; psychology; reproducibility; technology |
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| Link to article | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139099291&doi=10.1155%2f2022%2f9960589&partnerID=40&md5=fc8fa9bd5d980429047f0a5179565eaf |
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| Abstract | The reason why music can affect people's emotional experience is that the stimulation can be transmitted to the brain through hearing, such as the thalamus and lenticular nucleus. Music therapy has a positive auxiliary treatment effect on mental health. Therefore, an evaluation model of the auxiliary effect of music therapy on mental health based on artificial intelligence technology is proposed. We construct the constraint index parameters for the evaluation of music therapy's auxiliary effect on mental health, take parent pressure, self-pressure, teacher pressure, and social pressure as the questionnaire object parameters, take class type as the independent variable, carry out an independent sample t-test, and construct an adaptive information extraction model for the evaluation of music therapy's auxiliary effect on mental health. Paired sample t-test is used to analyze whether there is a difference between the experimental group and the control group on the learning stress scale. According to the analysis of the difference between the experimental group and the control group, combined with the difference test analysis of the data of the stress release of music therapy on mental health, the quantitative evaluation of the auxiliary effect of music therapy on mental health is realized through artificial intelligence optimization control. The experimental results show that the accuracy and reliability of this method to analyze the auxiliary effect of music therapy on mental health are high. There are obvious changes in the data of students' self-pressure, and the difference between the average value and standard deviation of the data before and after the course is obvious. From the perspective of the effectiveness of the course, the students in the class who implement the four relaxation experience courses in the course are under the pressure of parents, self-pressure, teacher pressure, and social pressure. There are obvious changes in the five aspects of learning pressure compared with that before the implementation of the course. After the course experience, the pressure value of most students decreases, and the course intervention effect is obvious. © 2022 Dan Lu. |
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| Metodology | Technique |
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