Evaluation of the effects of the body on athletes’ emotions and motivational behaviors from the perspective of big data public health
ObjectiveAn analysis was conducted on the impact of the body on athletes’ emotions and motivation from the perspective of Public Health (PH).MethodsPSO-KNN (Particle Swarm Optimization-K-Nearest Neighbor) algorithm and PSO-SVM algorithm (Particle Swarm Optimization-Support Vector Machine) were obtai...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
|
| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1640081/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849770803706462208 |
|---|---|
| author | Qiang Zhang Diandong Lian Yiqiao Zhang |
| author_facet | Qiang Zhang Diandong Lian Yiqiao Zhang |
| author_sort | Qiang Zhang |
| collection | DOAJ |
| description | ObjectiveAn analysis was conducted on the impact of the body on athletes’ emotions and motivation from the perspective of Public Health (PH).MethodsPSO-KNN (Particle Swarm Optimization-K-Nearest Neighbor) algorithm and PSO-SVM algorithm (Particle Swarm Optimization-Support Vector Machine) were obtained by combining Particle Swarm Optimization (PSO), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM), and then the recognition rates of the two algorithms were compared.ResultsWhen comparing the PSO-KNN algorithm and PSO-SVM algorithm on baseline removed and baseline not removed, the average recognition rates of PSO-KNN algorithm and PSO-SVM algorithm under emotional state were 56.66 and 54.75%, respectively. The average recognition rates of PSO-KNN algorithm and PSO-SVM algorithm with baseline removal under tension were 53.16 and 50.58%, respectively.ConclusionThe algorithm that removes the baseline is better than the algorithm that does not remove the baseline, and the PSO-KNN algorithm is better than the PSO-SVM algorithm. |
| format | Article |
| id | doaj-art-0ff4160ec350455389e23ffc2bb26d6e |
| institution | DOAJ |
| issn | 1664-1078 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj-art-0ff4160ec350455389e23ffc2bb26d6e2025-08-20T03:02:52ZengFrontiers Media S.A.Frontiers in Psychology1664-10782025-08-011610.3389/fpsyg.2025.16400811640081Evaluation of the effects of the body on athletes’ emotions and motivational behaviors from the perspective of big data public healthQiang Zhang0Diandong Lian1Yiqiao Zhang2College of Physical Education, Suzhou University, Suzhou, Anhui, ChinaDepartment of Physical Education, Tarim University, Alar, Xinjiang, ChinaCollege of Physical Education, Hubei University of Arts and Sciences, Xiangyang, Hubei, ChinaObjectiveAn analysis was conducted on the impact of the body on athletes’ emotions and motivation from the perspective of Public Health (PH).MethodsPSO-KNN (Particle Swarm Optimization-K-Nearest Neighbor) algorithm and PSO-SVM algorithm (Particle Swarm Optimization-Support Vector Machine) were obtained by combining Particle Swarm Optimization (PSO), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM), and then the recognition rates of the two algorithms were compared.ResultsWhen comparing the PSO-KNN algorithm and PSO-SVM algorithm on baseline removed and baseline not removed, the average recognition rates of PSO-KNN algorithm and PSO-SVM algorithm under emotional state were 56.66 and 54.75%, respectively. The average recognition rates of PSO-KNN algorithm and PSO-SVM algorithm with baseline removal under tension were 53.16 and 50.58%, respectively.ConclusionThe algorithm that removes the baseline is better than the algorithm that does not remove the baseline, and the PSO-KNN algorithm is better than the PSO-SVM algorithm.https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1640081/fullpublic health perspectiveathlete emotionparticle swarm optimizationK-nearest neighborsupport vector machine |
| spellingShingle | Qiang Zhang Diandong Lian Yiqiao Zhang Evaluation of the effects of the body on athletes’ emotions and motivational behaviors from the perspective of big data public health Frontiers in Psychology public health perspective athlete emotion particle swarm optimization K-nearest neighbor support vector machine |
| title | Evaluation of the effects of the body on athletes’ emotions and motivational behaviors from the perspective of big data public health |
| title_full | Evaluation of the effects of the body on athletes’ emotions and motivational behaviors from the perspective of big data public health |
| title_fullStr | Evaluation of the effects of the body on athletes’ emotions and motivational behaviors from the perspective of big data public health |
| title_full_unstemmed | Evaluation of the effects of the body on athletes’ emotions and motivational behaviors from the perspective of big data public health |
| title_short | Evaluation of the effects of the body on athletes’ emotions and motivational behaviors from the perspective of big data public health |
| title_sort | evaluation of the effects of the body on athletes emotions and motivational behaviors from the perspective of big data public health |
| topic | public health perspective athlete emotion particle swarm optimization K-nearest neighbor support vector machine |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2025.1640081/full |
| work_keys_str_mv | AT qiangzhang evaluationoftheeffectsofthebodyonathletesemotionsandmotivationalbehaviorsfromtheperspectiveofbigdatapublichealth AT diandonglian evaluationoftheeffectsofthebodyonathletesemotionsandmotivationalbehaviorsfromtheperspectiveofbigdatapublichealth AT yiqiaozhang evaluationoftheeffectsofthebodyonathletesemotionsandmotivationalbehaviorsfromtheperspectiveofbigdatapublichealth |