Sports training injury risk assessment combined with dynamic analysis algorithm

To explore the application of dynamic analysis algorithm in sports training injury risk assessment, this paper takes the Spatio-Temporal Graph Convolutional Network (ST-GCN) as the main algorithm, and introduces the Adaptive Graph Convolution Module (AGCM) and Residual Channel Attention Module (RCAM...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Molecular & cellular biomechanics Jg. 21; H. 3; S. 484
Hauptverfasser: Hou, Zhihong, Xue, Yuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: 18.11.2024
ISSN:1556-5297, 1556-5300
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Abstract To explore the application of dynamic analysis algorithm in sports training injury risk assessment, this paper takes the Spatio-Temporal Graph Convolutional Network (ST-GCN) as the main algorithm, and introduces the Adaptive Graph Convolution Module (AGCM) and Residual Channel Attention Module (RCAM). ST-GCN is improved to form AGCM + RCAM-ST-GCN (ARST-GCN) motion posture recognition algorithm. Meanwhile, combined with the extreme gradient boosting (XG Boost), the final physical training injury risk assessment model is formed. The performance of the improved ARST-GCN and the proposed damage risk assessment model is verified by experiments. The results show that ARST-GCN, which combines AGCM and RCAM modules, performs best in all indicators. Compared with ST-GCN, the accuracy rate is increased by 1.94% and the F1 value is increased by 4.3%. In addition, in the performance comparison of different sports injury risk models, the recall rate and F2 value of XGBoost are 0.937 and 0.893, respectively, and the overall performance is the best, indicating that XGBoost has significant advantages in dealing with sports injury risk assessment (SIRA) tasks. The research results provide theoretical basis and practical reference for injury prevention in sports training, and help to improve the accuracy and reliability of SIRA.
AbstractList To explore the application of dynamic analysis algorithm in sports training injury risk assessment, this paper takes the Spatio-Temporal Graph Convolutional Network (ST-GCN) as the main algorithm, and introduces the Adaptive Graph Convolution Module (AGCM) and Residual Channel Attention Module (RCAM). ST-GCN is improved to form AGCM + RCAM-ST-GCN (ARST-GCN) motion posture recognition algorithm. Meanwhile, combined with the extreme gradient boosting (XG Boost), the final physical training injury risk assessment model is formed. The performance of the improved ARST-GCN and the proposed damage risk assessment model is verified by experiments. The results show that ARST-GCN, which combines AGCM and RCAM modules, performs best in all indicators. Compared with ST-GCN, the accuracy rate is increased by 1.94% and the F1 value is increased by 4.3%. In addition, in the performance comparison of different sports injury risk models, the recall rate and F2 value of XGBoost are 0.937 and 0.893, respectively, and the overall performance is the best, indicating that XGBoost has significant advantages in dealing with sports injury risk assessment (SIRA) tasks. The research results provide theoretical basis and practical reference for injury prevention in sports training, and help to improve the accuracy and reliability of SIRA.
Author Xue, Yuan
Hou, Zhihong
Author_xml – sequence: 1
  givenname: Zhihong
  surname: Hou
  fullname: Hou, Zhihong
– sequence: 2
  givenname: Yuan
  surname: Xue
  fullname: Xue, Yuan
BookMark eNplkE1Lw0AQhhepYKviX9ibp-h-TLrJUYpfUOhBPYfJ7qZuTTZlJyL590arFz3Ny7wPA_Ms2Cz20TN2IcXVUi2lue5sDQUcsbnM82WWayFmv1mV5oQtiHZCgCyVmbPN075PA_EhYYghbnmIu_c08hTojSORJ-p8HLjtuzpE7_hHGF65GyN2wXKM2I4UiGO77dPUdGfsuMGW_PnPPGUvd7fPq4dsvbl_XN2sMyuNhsxpMKUXoMEjIEDhpr0RjVJ5UXrpSqxBF05JNQEF1LXSjcmVLTU6KKzUp-zycNemnij5ptqn0GEaKymqbw_VwcNEZn9IGwYcQh-_fm7_8Z-tMmKo
CitedBy_id crossref_primary_10_1177_14727978251361419
ContentType Journal Article
DBID AAYXX
CITATION
DOI 10.62617/mcb484
DatabaseName CrossRef
DatabaseTitle CrossRef
DatabaseTitleList CrossRef
DeliveryMethod fulltext_linktorsrc
Discipline Biology
EISSN 1556-5300
ExternalDocumentID 10_62617_mcb484
GroupedDBID ---
123
AAFWJ
AAYXX
ADMLS
AENEX
AFFHD
AFKRA
ALMA_UNASSIGNED_HOLDINGS
BBNVY
BENPR
BHPHI
CCPQU
CITATION
EBS
EJD
F5P
HCIFZ
M7P
P2P
PHGZM
PHGZT
PIMPY
PQGLB
RTS
ID FETCH-LOGICAL-c1734-d3479e0434ea4a448dc1770f22589e1d9ab438d2124ea84bb23f752c93ad48c13
ISSN 1556-5297
IngestDate Sat Nov 29 02:47:36 EST 2025
Tue Nov 18 22:43:45 EST 2025
IsDoiOpenAccess false
IsOpenAccess true
IsPeerReviewed false
IsScholarly true
Issue 3
Language English
License https://creativecommons.org/licenses/by/4.0
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-c1734-d3479e0434ea4a448dc1770f22589e1d9ab438d2124ea84bb23f752c93ad48c13
OpenAccessLink https://ojs.sin-chn.com/index.php/mcb/article/download/484/280
ParticipantIDs crossref_primary_10_62617_mcb484
crossref_citationtrail_10_62617_mcb484
PublicationCentury 2000
PublicationDate 2024-11-18
PublicationDateYYYYMMDD 2024-11-18
PublicationDate_xml – month: 11
  year: 2024
  text: 2024-11-18
  day: 18
PublicationDecade 2020
PublicationTitle Molecular & cellular biomechanics
PublicationYear 2024
SSID ssj0041927
Score 2.345495
Snippet To explore the application of dynamic analysis algorithm in sports training injury risk assessment, this paper takes the Spatio-Temporal Graph Convolutional...
SourceID crossref
SourceType Enrichment Source
Index Database
StartPage 484
Title Sports training injury risk assessment combined with dynamic analysis algorithm
Volume 21
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVPQU
  databaseName: ProQuest Biological Science Database (NC LIVE)
  customDbUrl:
  eissn: 1556-5300
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0041927
  issn: 1556-5297
  databaseCode: M7P
  dateStart: 20040101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/biologicalscijournals
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central
  customDbUrl:
  eissn: 1556-5300
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0041927
  issn: 1556-5297
  databaseCode: BENPR
  dateStart: 20040101
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Publicly Available Content Database
  customDbUrl:
  eissn: 1556-5300
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0041927
  issn: 1556-5297
  databaseCode: PIMPY
  dateStart: 20040101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3NT8IwFG8QNfFi_Iz4lR6MFzPdaEe3ozEaPIgkYoIn0m2dYGAQBIL_va9fY-JBPXhZxuN1gf669v3a94HQWeq5RNA0dngaRQ4NIx_ugPOwNOJhQniQuFwVm2CNRtBuh81S6d7Gwsz6LMuC-Twc_SvUIAOwZejsH-DOHwoCuAfQ4Qqww_VXwD_pQwBb--Gil71Bv2kfcp7n4ZS-5ECKrfN5ogvTX3Cbo4T3X4dj-GZQtF4fbC1dNWDknr_6oEL4ZQRxwXO-Ppyqc49urzs0iyNI21O1f_oyNWPSbDdUqYy7-zpD-jVgr9qp9lIUZMR1i9OqDnw2w4cU5kidvPTb3F2TueGhcwdxZDS-ZsdeWrVyX0JgMappRzdcQatVBixJeXE27aIsz7pVnR3763X8tGp4pRsWDJOChdHaQpuGGuBrDek2KolsB63rYqEfu-hRA4stsFgDiyWweAEstsBiCSw2wGILLM6B3UPPd7etm7pjymE4sccIdRIZ9CtcSqjglAOtTkDOXHiz_CAUXhLyiJIgAVsEFAIaRVWSMr8ah4QnNIg9so_K2TATBwgDp_dcwVxe4wzeTwZWm_B56HMBLIYHpILObV90YpMrXv67fmeptysI54ojnR5lWeXwZ5UjtLEYaseoPBlPxQlai2eT3vv4VAH5CZ42XR8
linkProvider ProQuest
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Sports+training+injury+risk+assessment+combined+with+dynamic+analysis+algorithm&rft.jtitle=Molecular+%26+cellular+biomechanics&rft.au=Hou%2C+Zhihong&rft.au=Xue%2C+Yuan&rft.date=2024-11-18&rft.issn=1556-5297&rft.eissn=1556-5300&rft.volume=21&rft.issue=3&rft.spage=484&rft_id=info:doi/10.62617%2Fmcb484&rft.externalDBID=n%2Fa&rft.externalDocID=10_62617_mcb484
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1556-5297&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1556-5297&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1556-5297&client=summon