Psychological Motivation of Athletes’ Physical Training Based on Deep Learning Model
In the field of sports training, two methods of physical and psychological monitoring are usually used to monitor the training process. Physiological index monitoring can objectively reflect the physical function of athletes, and there are many monitoring constraints. Psychological indicators can su...
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Wiley
2022-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2022/1962461 |
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| author | Yining Yang |
| author_facet | Yining Yang |
| author_sort | Yining Yang |
| collection | DOAJ |
| description | In the field of sports training, two methods of physical and psychological monitoring are usually used to monitor the training process. Physiological index monitoring can objectively reflect the physical function of athletes, and there are many monitoring constraints. Psychological indicators can subjectively reflect the athlete’s own state, and the monitoring is simple and easy. This paper mainly used the subjective perception of effort (RPE) and the profile of mood state (POMS) scales to track and monitor 20 nonprofessional athletes in a university track and field team. Based on the change of the same training volume, the change law and relationship between RPE and POMS, and the change law and relationship between RPE, POMS, heart rate, and blood pressure were analyzed. Finally, it was concluded that the athletes are feeling more and more about the amount of training, and the minimum value P<0.01 showed a very significant difference, reflecting that the increase of training volume has a significant impact on the t-test value. The training volume has an impact on both the positive and negative dimensions of POMS, but the negative dimension reflects the training volume more clearly. There was a linear relationship between RPE and POMS subscales. RPE was not significantly correlated with positive emotions but positively correlated with negative emotions and TMD. The change trend was the same and the RPE grade increases; the blood pressure and systolic blood pressure also increased accordingly, and vice versa. The POMS negative dimension and TMD were the same as changes in blood pressure increase or decrease, and TMD was not related to heart rate. Scientific training has a large impact on the training of nonprofessional athletes, and whether the training volume is reasonable or not directly affects the qualitative change of athletes’ physical functions. Therefore, it is particularly important to monitor the physiological and psychological indicators of nonprofessional athletes. The improvement of sports performance is the goal, and the improvement of physical function is the guarantee. |
| format | Article |
| id | doaj-art-ca9311fb52e84ed1b93c147599cf285b |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-ca9311fb52e84ed1b93c147599cf285b2025-02-03T01:24:29ZengWileyInternational Transactions on Electrical Energy Systems2050-70382022-01-01202210.1155/2022/1962461Psychological Motivation of Athletes’ Physical Training Based on Deep Learning ModelYining Yang0Fujian Polytechnic Normal UniversityIn the field of sports training, two methods of physical and psychological monitoring are usually used to monitor the training process. Physiological index monitoring can objectively reflect the physical function of athletes, and there are many monitoring constraints. Psychological indicators can subjectively reflect the athlete’s own state, and the monitoring is simple and easy. This paper mainly used the subjective perception of effort (RPE) and the profile of mood state (POMS) scales to track and monitor 20 nonprofessional athletes in a university track and field team. Based on the change of the same training volume, the change law and relationship between RPE and POMS, and the change law and relationship between RPE, POMS, heart rate, and blood pressure were analyzed. Finally, it was concluded that the athletes are feeling more and more about the amount of training, and the minimum value P<0.01 showed a very significant difference, reflecting that the increase of training volume has a significant impact on the t-test value. The training volume has an impact on both the positive and negative dimensions of POMS, but the negative dimension reflects the training volume more clearly. There was a linear relationship between RPE and POMS subscales. RPE was not significantly correlated with positive emotions but positively correlated with negative emotions and TMD. The change trend was the same and the RPE grade increases; the blood pressure and systolic blood pressure also increased accordingly, and vice versa. The POMS negative dimension and TMD were the same as changes in blood pressure increase or decrease, and TMD was not related to heart rate. Scientific training has a large impact on the training of nonprofessional athletes, and whether the training volume is reasonable or not directly affects the qualitative change of athletes’ physical functions. Therefore, it is particularly important to monitor the physiological and psychological indicators of nonprofessional athletes. The improvement of sports performance is the goal, and the improvement of physical function is the guarantee.http://dx.doi.org/10.1155/2022/1962461 |
| spellingShingle | Yining Yang Psychological Motivation of Athletes’ Physical Training Based on Deep Learning Model International Transactions on Electrical Energy Systems |
| title | Psychological Motivation of Athletes’ Physical Training Based on Deep Learning Model |
| title_full | Psychological Motivation of Athletes’ Physical Training Based on Deep Learning Model |
| title_fullStr | Psychological Motivation of Athletes’ Physical Training Based on Deep Learning Model |
| title_full_unstemmed | Psychological Motivation of Athletes’ Physical Training Based on Deep Learning Model |
| title_short | Psychological Motivation of Athletes’ Physical Training Based on Deep Learning Model |
| title_sort | psychological motivation of athletes physical training based on deep learning model |
| url | http://dx.doi.org/10.1155/2022/1962461 |
| work_keys_str_mv | AT yiningyang psychologicalmotivationofathletesphysicaltrainingbasedondeeplearningmodel |