A novel classification method for balance differences in elite versus expert athletes based on composite multiscale complexity index and ranking forests.
Balance is crucial for various athletic tasks, and accurately assessing balance ability among elite athletes using simple and accessible measurement methods is a significant challenge in sports science. A common approach to balance assessment involves recording center of pressure (CoP) displacements...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315454 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1825206782314676224 |
|---|---|
| author | Yuqi Cheng Dawei Wu Ying Wu Youcai Guo Xinze Cui Pengquan Zhang Jie Gao Yanming Fu Xin Wang |
| author_facet | Yuqi Cheng Dawei Wu Ying Wu Youcai Guo Xinze Cui Pengquan Zhang Jie Gao Yanming Fu Xin Wang |
| author_sort | Yuqi Cheng |
| collection | DOAJ |
| description | Balance is crucial for various athletic tasks, and accurately assessing balance ability among elite athletes using simple and accessible measurement methods is a significant challenge in sports science. A common approach to balance assessment involves recording center of pressure (CoP) displacements using force platforms, with various indicators proposed to distinguish subtle balance differences. However, these indicators have not reached a consensus, and it remains unclear whether these analyses alone can fully explain the complex interactions of postural control. In this study, we investigated four parameters related to balance control-anterior-posterior (AP) displacement, medial-lateral (ML) displacement, length, and tilt angle-in 13 elite athletes and 12 freestyle skiing aerial expert athletes. Data were recorded during 30-second trials on both soft and hard support surfaces, with eyes open and closed. We calculated the CMCI and used four machine learning algorithms-Logistic Regression, Support Vector Machine(SVM), Naive Bayes, and Ranking Forest-to combine these features and assess each participant's balance ability. A classic train-test split method was applied, and the performance of different classifiers was evaluated using Receiver Operating Characteristic(ROC) analysis. The ROC results showed that traditional time-domain features were insufficient for accurately distinguishing athletes' balance abilities, whereas CMCI performed the best overall. Among all classifiers, the combination of CMCI and Ranking Forest yielded the best performance, with a sensitivity of 0.95 and specificity of 0.35. This nonlinear, multidimensional approach appears to be highly suitable for assessing the complexity of postural control. |
| format | Article |
| id | doaj-art-47a36eb94c5140469be90275109d2b94 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-47a36eb94c5140469be90275109d2b942025-02-07T05:30:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031545410.1371/journal.pone.0315454A novel classification method for balance differences in elite versus expert athletes based on composite multiscale complexity index and ranking forests.Yuqi ChengDawei WuYing WuYoucai GuoXinze CuiPengquan ZhangJie GaoYanming FuXin WangBalance is crucial for various athletic tasks, and accurately assessing balance ability among elite athletes using simple and accessible measurement methods is a significant challenge in sports science. A common approach to balance assessment involves recording center of pressure (CoP) displacements using force platforms, with various indicators proposed to distinguish subtle balance differences. However, these indicators have not reached a consensus, and it remains unclear whether these analyses alone can fully explain the complex interactions of postural control. In this study, we investigated four parameters related to balance control-anterior-posterior (AP) displacement, medial-lateral (ML) displacement, length, and tilt angle-in 13 elite athletes and 12 freestyle skiing aerial expert athletes. Data were recorded during 30-second trials on both soft and hard support surfaces, with eyes open and closed. We calculated the CMCI and used four machine learning algorithms-Logistic Regression, Support Vector Machine(SVM), Naive Bayes, and Ranking Forest-to combine these features and assess each participant's balance ability. A classic train-test split method was applied, and the performance of different classifiers was evaluated using Receiver Operating Characteristic(ROC) analysis. The ROC results showed that traditional time-domain features were insufficient for accurately distinguishing athletes' balance abilities, whereas CMCI performed the best overall. Among all classifiers, the combination of CMCI and Ranking Forest yielded the best performance, with a sensitivity of 0.95 and specificity of 0.35. This nonlinear, multidimensional approach appears to be highly suitable for assessing the complexity of postural control.https://doi.org/10.1371/journal.pone.0315454 |
| spellingShingle | Yuqi Cheng Dawei Wu Ying Wu Youcai Guo Xinze Cui Pengquan Zhang Jie Gao Yanming Fu Xin Wang A novel classification method for balance differences in elite versus expert athletes based on composite multiscale complexity index and ranking forests. PLoS ONE |
| title | A novel classification method for balance differences in elite versus expert athletes based on composite multiscale complexity index and ranking forests. |
| title_full | A novel classification method for balance differences in elite versus expert athletes based on composite multiscale complexity index and ranking forests. |
| title_fullStr | A novel classification method for balance differences in elite versus expert athletes based on composite multiscale complexity index and ranking forests. |
| title_full_unstemmed | A novel classification method for balance differences in elite versus expert athletes based on composite multiscale complexity index and ranking forests. |
| title_short | A novel classification method for balance differences in elite versus expert athletes based on composite multiscale complexity index and ranking forests. |
| title_sort | novel classification method for balance differences in elite versus expert athletes based on composite multiscale complexity index and ranking forests |
| url | https://doi.org/10.1371/journal.pone.0315454 |
| work_keys_str_mv | AT yuqicheng anovelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT daweiwu anovelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT yingwu anovelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT youcaiguo anovelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT xinzecui anovelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT pengquanzhang anovelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT jiegao anovelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT yanmingfu anovelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT xinwang anovelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT yuqicheng novelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT daweiwu novelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT yingwu novelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT youcaiguo novelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT xinzecui novelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT pengquanzhang novelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT jiegao novelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT yanmingfu novelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests AT xinwang novelclassificationmethodforbalancedifferencesineliteversusexpertathletesbasedoncompositemultiscalecomplexityindexandrankingforests |