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摘要: 狭义的运动生物力学特指人体运动中的生物力学, 主要解决竞技体育领域中如何提高运动成绩和减少运动损伤的问题. 随着相关学科的融合和发展, 当前运动生物力学的研究已扩展到与人类运动相关的生物学、医学、力学等学科领域. 近年来, 智能测试、大数据分析、人工智能等技术快速发展, 对运动生物力学实验、仿真方法产生了重要的影响, 在不断拓展和深化着该学科的研究内容和方向的同时, 也对运动生物力学发展提出了新的挑战. 本文综述了近年来运动生物力学领域的研究现状, 并指出了相关研究方向的关键问题及发展趋势: 在理论建模和模拟仿真计算方面, 肌肉本构理论及肌肉力计算准确性是重点和难点; 实验测试的新技术在竞技体育运动项目中的应用研究中扮演重要角色, 其中基于深度学习的人体关键点检测算法在解决竞技体育的非接触测量方面有突破性进展; 对于骨、韧带、软骨、肌肉等组织的宏观损伤机制认识不断清晰, 但对于其早期损伤预测以及跨尺度损伤发生机制的研究仍有待深入; 智能可穿戴装备、人工智能等新技术开始应用于运动生物力学研究及实践, 成为目前运动生物力学领域最具活力的研究方向之一. 本文的综述表明当前运动生物力学研究越来越向智能化、个体化、定量化发展, 并正在与相关学科不断交叉融合, 持续推进着体育、健康、医疗等领域的科技创新发展.Abstract: The early concept of sports biomechanics refers to biomechanics in sports, which mainly solves the problems of performance enhancement and injury prevention. With the integrated development of related disciplines, the research of sports biomechanics has been extended to the fields of biology, medicine, mechanics, et al. The advance of intelligent testing, big data and artificial intelligence technologies greatly promotes the experimental measurement technologies and simulation methods of sports biomechanics. The renewal of technology and method has gradually expanded and deepened the research content and direction of this discipline and also offered new challenges. This review summarizes the recent advances and points out the development trends and key issues in sports biomechanics. The muscle constitutive theory and calculation of muscle force are the key points in the theoretical modeling and simulation calculation of sports biomechanics. The new testing methods play important role in the competitive sports. Among them the human key point detection algorithm based on deep learning has made a breakthrough in solving the non-contact measurement of competitive sports. The macroscopic damage mechanism of bone, ligament, cartilage, muscle and other tissues has been clarified, but the prediction of their early damage and the mechanism of cross-scale damage still need to be further studied. Intelligent wearable equipment, artificial intelligence and other new technologies have been applied to the research of sports biomechanics and become the hot topics. This review shows that the current research of sports biomechanics is developing towards intelligence, individualization and quantification, integrating with the related disciplines, and continuously promoting the development of scientific and technological innovation in sports, health, medical and other fields.
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图 1 典型的膝关节双刚体模型(Richard et al. 2016). (a) 膝关节模型的坐标系统Qi: 小腿 (i = 2) , 大腿 (i = 3) 以及膝关节, (b) 从上到下表示四种不同的膝关节模型: 无关节模型 (N) 、球形模型 (S) 、并联机构 (P) 和刚度矩阵 (M)
图 2 基于人体运动分析的多体动力学模型. (a) 平面多刚体力学建模(King et al. 2019), (b) Anybody全人体肌骨模型 (AnyBody Technology Inc., Denmark), (c) OpenSim全人体模型(Raabe & Chaudhari 2016)
图 3 基于Hill-Zajac假设的肌肉收缩力模型. (a) Hill-Zajac模型的基本假设及力学模型的抽象过程(Zajac 1989), (b) 肌腱力随肌腱长度变化关系的实验(Magnusson et al. 2001, Maganaris & Paul 2002)和模型结果(Blankevoort et al. 1991), (c) 肌肉力与肌纤维长度关系的实验(Gollapud & Lin 2009, Winters et al. 2011)和模型结果(Arnold et al. 2010), (d) 肌肉力与肌纤维收缩速度关系的实验(Joyce et al. 1969, MASHIMA 1984)和模型结果(Blankevoort et al. 1991)
图 4 肌骨动力学模拟在实践中的应用. (a) 不同步态的关节受力分析(Lerner et al. 2015), 局部放大图显示膝关节模型结构, 右侧逻辑图显示模型结构间位置和运动关系, (b) 人工关节评估和优化(Chen et al. 2014), (c) 人机耦合外骨骼助力装备的设计, 从左至右分别为外骨骼装置照片、人-机耦合骨骼肌肉动力学分析和实验照片(Gordon et al. 2018), (d) 专项运动技术动作优化和损伤分析(Trasolini et al. 2022)
图 5 动力学参数测量设备. (a) 足底压力鞋垫在大跳台训练中的应用, (b) 三维测力台在下肢评估中的应用, (c) 分布式足底压力测试系统 (Materialise Inc., Belgium), (d) 等速肌力测试系统
图 6 冬季项目训练智能管理系统(霍波等2022a). 结合生理学、运动学、动力学等参数的检测实现对人体心肺系统、骨骼肌肉系统建模分析, 实现多维度、精细化、科学化的运动训练管理
图 7 短跑的起跑过程中关键动作和对应时间(Bezodis et al. 2019b).
图 8 膝关节在体建模和有限元分析流程(Jogi et al. 2021). (a) 人体膝关节核磁共振成像图 (单层), (b) 对核磁图像进行不同组织的分割并用Mimics创建三维几何体, (c) ANSYS对三维模型划分网格, (d) 针对不同软组织刚度取值, 有限元模型计算得到每位受试者的胫骨关节变形结果
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