{"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
摘要
背景:具有洞察力的运动分析为运动员的健康提供了有价值的信息,这是运动医学的一个重要方面。本系统综述对运动分析中使用各种传感器进行运动损伤评估的情况进行了分析概述:方法:2024 年 2 月,使用与 "运动"、"运动员"、"基于传感器的技术"、"运动分析 "和 "损伤 "相关的检索词对 PubMed/MEDLINE、Scopus 和 Web of Science 进行了全面检索。研究根据 PCC(参与者、概念、背景)标准进行收录。提取并分析了关键数据,包括传感器类型、运动数据处理方法、损伤和运动类型以及应用领域:结果:42 项研究符合纳入标准。惯性测量单元(IMU)传感器是最常用的运动数据采集设备。传感器融合技术已得到广泛应用,尤其是在康复评估方面。膝关节损伤和关节扭伤是最常研究的损伤,统计方法是数据分析的主要方法:本综述全面阐述了运动损伤运动分析中基于传感器的技术。研究中存在的重大差距,包括先进处理技术的整合、现实世界的适用性以及适应性运动等代表性不足的领域,都凸显了创新的机遇。缩小这些差距可以推动开发更有效、更方便、更个性化的运动健康解决方案。
Sensor-based technologies for motion analysis in sports injuries: a scoping review.
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.
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.