Sensor-based technologies for motion analysis in sports injuries: a scoping review.

IF 2.1 3区 医学 Q1 REHABILITATION BMC Sports Science Medicine and Rehabilitation Pub Date : 2025-01-30 DOI:10.1186/s13102-025-01063-z
Afrooz Arzehgar, Seyedeh Nahid Seyedhasani, Fatemeh Baharvand Ahmadi, Fatemeh Bagheri Baravati, Alireza Sadeghi Hesar, Amir Reza Kachooei, Shokoufeh Aalaei
{"title":"Sensor-based technologies for motion analysis in sports injuries: a scoping review.","authors":"Afrooz Arzehgar, Seyedeh Nahid Seyedhasani, Fatemeh Baharvand Ahmadi, Fatemeh Bagheri Baravati, Alireza Sadeghi Hesar, Amir Reza Kachooei, Shokoufeh Aalaei","doi":"10.1186/s13102-025-01063-z","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Insightful motion analysis provides valuable information for athlete health, a crucial aspect of sports medicine. This systematic review presents an analytical overview of the use of various sensors in motion analysis for sports injury assessment.</p><p><strong>Methods: </strong>A comprehensive search of PubMed/MEDLINE, Scopus, and Web of Science was conducted in February 2024 using search terms related to \"sport\", \"athlete\", \"sensor-based technology\", \"motion analysis\", and \"injury.\" Studies were included based on PCC (Participants, Concept, Context) criteria. Key data, including sensor types, motion data processing methods, injury and sport types, and application areas, were extracted and analyzed.</p><p><strong>Results: </strong>Forty-two studies met the inclusion criteria. Inertial measurement unit (IMU) sensors were the most commonly used for motion data collection. Sensor fusion techniques have gained traction, particularly for rehabilitation assessment. Knee injuries and joint sprains were the most frequently studied injuries, with statistical methods being the predominant approach to data analysis.</p><p><strong>Conclusions: </strong>This review comprehensively explains sensor-based techniques in sports injury motion analysis. Significant research gaps, including the integration of advanced processing techniques, real-world applicability, and the inclusion of underrepresented domains such as adaptive sports, highlight opportunities for innovation. Bridging these gaps can drive the development of more effective, accessible, and personalized solutions in sports health.</p>","PeriodicalId":48585,"journal":{"name":"BMC Sports Science Medicine and Rehabilitation","volume":"17 1","pages":"15"},"PeriodicalIF":2.1000,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11780775/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Sports Science Medicine and Rehabilitation","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13102-025-01063-z","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"REHABILITATION","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Insightful motion analysis provides valuable information for athlete health, a crucial aspect of sports medicine. This systematic review presents an analytical overview of the use of various sensors in motion analysis for sports injury assessment.

Methods: A comprehensive search of PubMed/MEDLINE, Scopus, and Web of Science was conducted in February 2024 using search terms related to "sport", "athlete", "sensor-based technology", "motion analysis", and "injury." Studies were included based on PCC (Participants, Concept, Context) criteria. Key data, including sensor types, motion data processing methods, injury and sport types, and application areas, were extracted and analyzed.

Results: Forty-two studies met the inclusion criteria. Inertial measurement unit (IMU) sensors were the most commonly used for motion data collection. Sensor fusion techniques have gained traction, particularly for rehabilitation assessment. Knee injuries and joint sprains were the most frequently studied injuries, with statistical methods being the predominant approach to data analysis.

Conclusions: This review comprehensively explains sensor-based techniques in sports injury motion analysis. Significant research gaps, including the integration of advanced processing techniques, real-world applicability, and the inclusion of underrepresented domains such as adaptive sports, highlight opportunities for innovation. Bridging these gaps can drive the development of more effective, accessible, and personalized solutions in sports health.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
BMC Sports Science Medicine and Rehabilitation
BMC Sports Science Medicine and Rehabilitation Medicine-Orthopedics and Sports Medicine
CiteScore
3.00
自引率
5.30%
发文量
196
审稿时长
26 weeks
期刊介绍: BMC Sports Science, Medicine and Rehabilitation is an open access, peer reviewed journal that considers articles on all aspects of sports medicine and the exercise sciences, including rehabilitation, traumatology, cardiology, physiology, and nutrition.
期刊最新文献
Postural stability measures as diagnostic tools for chronic ankle instability: a comprehensive assessment. Sensor-based technologies for motion analysis in sports injuries: a scoping review. Reproducibility of peak force for isometric and isokinetic multi-joint leg extension exercise. Health-related quality of life associated with fatigue, physical activity and activity pacing in adults with chronic conditions. A meta-analysis of the effects of plyometric training on muscle strength and power in martial arts athletes.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1