人工智能技术在运动损伤预防中的应用

张博轩, 周珂, 陶宽

运动科学与健康研究 ›› 2025, Vol. 1 ›› Issue (2) : 85-93.

PDF(1858 KB)
PDF(1858 KB)
运动科学与健康研究 ›› 2025, Vol. 1 ›› Issue (2) : 85-93. DOI: 10.3969/j.issn.2097-5457.2025.01.006
讲座

人工智能技术在运动损伤预防中的应用

作者信息 +

Application of artificial intelligence technology in the prevention of sports injury

Author information +
文章历史 +

摘要

人工智能技术在运动损伤预防中的应用日益广泛。通过人工智能的支持,人体运动数据的采集、复杂运动数据的分析处理以及智能化运动损伤预防模型的构建变得更加便捷,帮助研究人员更加精确地预测运动损伤。本文梳理了基于人工智能技术预防运动损伤的相关研究,系统分析了构建人工智能模型的一般流程,概括了运动损伤预防模型构建中各关键技术环节,评估了现有方法的优势和不足,并探讨了未来的研究方向。未来研究应进一步加强数据安全技术的发展,构建数据共享平台,探索多模态方法的应用。

Abstract

The application of artificial intelligence (AI) technology in sports injury prevention is becoming increasingly widespread. With the support of AI, the collection of human movement data, the analysis and processing of complex movement data, and the construction of intelligent sports injury prevention models have become more convenient, helping researchers to predict sports injuries with greater accuracy. This paper integrates research on sports injury prevention based on AI technology, systematically outlines and analyzes the general building process of AI models, summarizes key technical aspects in constructing sports injury prevention models, evaluates the strengths and limitations of current methods, and explores future research directions. Although existing studies have achieved some progress, several challenges remain. Future research should focus on enhancing data security technology, building data-sharing platforms, and exploring the application of multimodal approaches.

关键词

运动损伤 / 损伤预防 / 人工智能 / 机器学习 / 深度学习

Key words

Sports injury / Injury prevention / Artificial intelligence / Machine learning / Deep learning

引用本文

导出引用
张博轩, 周珂, 陶宽. 人工智能技术在运动损伤预防中的应用[J]. 运动科学与健康研究, 2025, 1(2): 85-93 https://doi.org/10.3969/j.issn.2097-5457.2025.01.006
Zhang Boxuan, Zhou Ke, Tao Kuan. Application of artificial intelligence technology in the prevention of sports injury[J]. Research on Sports Science and Health, 2025, 1(2): 85-93 https://doi.org/10.3969/j.issn.2097-5457.2025.01.006
中图分类号: R873.1   

参考文献

[1] 郝卫亚. 运动损伤生物力学研究[J]. 医用生物力学, 2017, 32(4): 299-306.
[2] 任玉衡, 田得祥. 中国优秀运动员运动创伤流行病学研究[M]. 北京: 国家体育总局科教司, 1999: 1-57.
[3] MCBAIN K, SHRIER I, SHULTZ R, et al.Prevention of sports injury I: a systematic review of applied biomechanics and physiology outcomes research[J]. British Journal of Sports Medicine, 2012, 46(3): 169-173.
[4] 张斯佳. 体育大数据分析的价值与应用探索[C]//四川省体育科学学会, 四川省学生体育艺术协会. 2024第二届四川省体育科学大会论文报告会论文集(1). 出版者不详, 2024: 4.
[5] 黄谦, 石勇. 数据挖掘在体育训练指导中的应用研究[J]. 广州体育学院学报, 2009, 29(6): 106-110.
[6] 张朕, 夏锐, 张会杰. BP神经网络预测模型对2013~2023年浙江省青少年游泳比赛成绩预测的实证分析[C]//中国体育科学学会. 第十三届全国体育科学大会论文摘要集——#墙报交流(运动训练学分会)(一). 出版者不详, 2023: 3.
[7] 高洪歌. 数据挖掘技术在乒乓球比赛技战术分析中的应用研究[D]. 北京: 北方工业大学, 2006.
[8] SHERRY D, PAUL H.Video processor systems for ball tracking in ball games[P]. International Patent, Publishing Number WO, 2001, 1(41884): A1.
[9] 季欣, 杨子喆, 马勇, 等. 人工智能技术在健康促进、运动能力提升和损伤预测等方面的研究进展[J]. 体育科技文献通报, 2023, 31(5): 235-238.
[10] 魏梦力, 钟亚平, 桂辉贤, 等. 基于机器学习的运动损伤预警模型[J]. 中国组织工程研究, 2025, 29(2): 409-418.
[11] HULIN B T, GABBETT T J, BLANCH P, et al.Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers[J]. British Journal of Sports Medicine, 2014, 48(8): 708-712.
[12] OPAR D A, WILLIAMS M D, TIMMINS R G, et al.Eccentric hamstring strength and hamstring injury risk in Australian footballers[J]. Medicine & Science in Sports & Exercise, 2015, 47(4): 857-865.
[13] PEXA B S, JOHNSTON C J, TAYLOR J B, et al.Training load and current soreness predict future delayed onset muscle soreness in collegiate female soccer athletes[J]. International Journal of Sports Physical Therapy, 2023, 18(6): 1271.
[14] TSILIMIGKRAS T, KAKKOS I, MATSOPOULOS G K, et al.Enhancing sports injury risk assessment in soccer through machine learning and training load analysis[J]. Journal of Sports Science & Medicine, 2024, 23(1): 537.
[15] 赵炎涛, 陈丽. 基于支持向量机的运动训练负荷水平预测[C]//中国体育科学学会. 第十三届全国体育科学大会论文摘要集——墙报交流(体育统计分会). 出版者不详, 2023: 3.
[16] SUN Z, KE Q, RAHMANI H, et al.Human action recognition from various data modalities: a review[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(3): 3200-3225.
[17] 赵炎涛, 陈丽. 人工智能在竞技体育中的研究现状及展望[J]. 当代体育科技, 2024, 14(25): 175-178.
[18] 马冉, 刘宏超. 国际竞技训练领域内可穿戴设备的应用及未来发展趋势[J]. 湖北体育科技, 2020, 39(12): 1113-1117.
[19] KIERNAN D, HAWKINS D A, MANOUKIAN M A C, et al. Accelerometer based prediction of running injury in national collegiate athletic association track athletes[J]. Biomech, 2018(73): 201-209.
[20] 王佳鑫. 可穿戴式游泳姿态捕捉与相位分割算法研究[D]. 大连: 大连理工大学, 2021.
[21] 程晓雯, 杨勇. 柔性可穿戴传感器用于篮球技术动作监测及伤病预防的研究[J]. 文体用品与科技, 2023, (1): 148-150.
[22] ZHOU Y, ZIA U R REHMAN R, HANSEN C, et al. Classification of neurological patients to identify fallers based on spatial-temporal gait characteristics measured by a wearable device[J]. Sensors, 2020, 20(15): 4098.
[23] ZHENG Y, POON C C, YAN B P, et al.Pulse arrival time based cuff-less and 24-h wearable blood pressure monitoring and its diagnostic value in hypertension[J]. J Med Syst, 2016, 40(9): 195-195.
[24] AGUILAR-ORTEGA R, BERRAL-SOLER R, JIMÉNEZ-VELASCO I, et al. Uco physical rehabilitation: new dataset and study of human pose estimation methods on physical rehabilitation exercises[J]. Sensors, 2023, 23(21): 8862.
[25] KESKIN C, KIRAÇ F, KARA Y E, et al.Real time hand pose estimation using depth sensors[J]. Consumer Depth Cameras for Computer Vision: Research Topics and Applications, 2013: 119-137.
[26] MANSOOR M, AMIN R, MUSTAFA Z, et al.A machine learning approach for non-invasive fall detection using Kinect[J]. Multimedia Tools and Applications, 2022, 81(11): 15491-15519.
[27] 刘明吉, 王秀峰, 黄亚楼. 数据挖掘中的数据预处理[J]. 计算机科学, 2000, (4): 54-57.
[28] ZHANG K, ZUO W, CHEN Y, et al.Beyond a gaussian denoiser: residual learning of deep cnn for image denoising[J]. IEEE Transactions on Image Processing, 2017, 26(7): 3142-3155.
[29] ZHANG K, ZUO W, ZHANG L.FFDNet: toward a fast and flexible solution for CNN-based image denoising[J]. IEEE Transactions on Image Processing, 2018, 27(9): 4608-4622.
[30] GUO S, YAN Z, ZHANG K, et al.Toward convo-lutional blind denoising of real photographs[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 1712-1722.
[31] ANWAR S, BARNES N.Real image denoising with feature attention[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 3155-3164.
[32] RAHLF A L, HOENIG T, STÜRZNICKEL J, et al. A machine learning approach to identify risk factors for running-related injuries: study protocol for a prospective longitudinal cohort trial[J]. BMC Sports Science, Medicine and Rehabilitation, 2022, 14(1): 1-11.
[33] JAUHIAINEN S, KAUPPI J P, KROSSHAUG T, et al.Predicting ACL injury using machine learning on data from an extensive screening test battery of 880 female elite athletes[J]. American Journal of Sports Medicine, 2022, 50(11): 2917-2924.
[34] HU W J.The application of artificial intelligence and big data technology in basketball sports training[J]. EAI Endorsed Transactions on Scalable Information Systems, 2023, 10(4).
[35] 奚耀昌. 基于深度学习的人体摔倒检测方法研究[D]. 临沂: 临沂大学, 2024.
[36] YUAN C, YANG Y, LIU Y.Sports decision-making model based on data mining and neural network[J]. Neural Computing and Applications, 2021, 33: 3911-3924.
[37] PRINCE S J D. Computer vision: models, learning, and inference[M]. Cambridge University Press, 2012.
[38] TEIXEIRA J E, ENCARNAÇÃO S, BRANQUINHO L, et al. Data mining paths for standard weekly training load in sub-elite young football players: a machine learning approach[J]. Journal of Functional Morphology and Kinesiology, 2024, 9(3): 114.
[39] 陈文继. 基于Radon变换的健美操跳跃动作轨迹实时提取方法[J]. 自动化技术与应用, 2023, 42(1): 60-63, 81.
[40] DONGDONG Z, HONGLEI Z, YULONG S, et al.Injury risk prediction of aerobics athletes based on big data and computer vision[J]. Scientific Programming, 2021.
[41] HSU W W, GUO J M, CHEN C Y, et al.Fall detection with the spatial-temporal correlation encoded by a sequence-to-sequence denoised GAN[J]. Sensors, 2022, 22(11): 4194.
[42] 兰帅辉, 张麟. 智能技术赋能体育传媒产业研究[J]. 体育文化导刊, 2023, (12): 96-103.

基金

中央高校基本科研业务费项目专项资助“基于可穿戴设备的青少年体育活动信息有效性探究”(项目编号:2024TZJK005)

PDF(1858 KB)

85

Accesses

0

Citation

Detail

段落导航
相关文章

/