值得庆幸的是，作者感谢荷兰国家研究机构（NWO）提供的资金来源。此外，作者还感谢荷兰皇家足球协会（KNVB）通过提供研究设施来促进研究方案。最后，作者们要感谢蒂伊斯·威格斯对研究计划的贡献。

本节中描述的所有方法均已获得格罗宁根大学人类运动科学中心伦理委员会的批准（注册号:201800904）。
1. 现场测试和惯性测量单元准备
<ol><li>设置两个圆锥体彼此至少 1 米，以确定现场测试的开始。 注:圆锥体之间的 1 米距离使受试者能够轻松通过现场测试的起点。此距离可根据测试领导者的偏好进行调整。</li>
	<li>通过从测试起点滚动测量胶带，直到 30 米的线性距离，确定现场测试的终点。</li>
	<li>设置两个相互距离至少 1 米的圆锥体，以确定现场测试的终点。</li>
	<li>准备 IMUs 以正确连接到受试者的身体。 注:查看 IMU 尺寸和重量特征的 材料表 。<ol>
<li>将拉伸胶带切成 5 块，尺寸为 10 厘米 x 10 厘米。</li>
		<li>将双面胶带（例如托皮胶带）切成 5 块，相当于使用的 IMUs 的大小。</li>
		<li>将一块双面胶带连接到每个 IMU。</li>
		<li>标记每个 IMU，以便在数据分析期间单独识别它。</li>
	</ol>
</li>
</ol>2. 主题准备
<ol><li>获取有关受试者的性别、年龄、体重和身高的信息。要求受试者填写一份关于他们在团队运动中背景的调查问卷。获得符合包容标准的主体的书面知情同意。 注:问题的例子:（i）你踢足球多少年了？（二） 你在哪个级别踢足球？（三） 在过去的6个月里，你每周有多少小时的足球训练？（iv）你的比赛位置是什么？（v） 在过去 6 个月中，您是否经历过任何疼痛或下肢肌肉骨骼损伤？</li>
	<li>确定主体是否符合包含标准。 注:包括受试者在执行协议前的6个月内没有在下肢经历任何肌肉骨骼损伤或疼痛:科目应具有1年以上的业余组队运动经验。</li>
	<li>要求主题换上运动服（例如足球衫、足球短裤和足球鞋）。 注:因为传感器将被放置在头骨上，足球袜是不需要的。</li>
	<li>准备连接到主体主体的IMUs。<ol>
<li>对齐彼此旁边的所有5个IMUs。</li>
		<li>通过按下传感器顶部的按钮同时激活所有五个 IMUs。当绿灯闪烁时，传感器被激活。 注:从此时起，每个 IMU 以 500 Hz 的速度对数据进行采样，数据存储在 SD 卡内部。数据必须在测试完成后上传到笔记本电脑或计算机。</li>
		<li>通过同时在坚硬的表面上（例如在桌子上）敲击所有 IMUs 来确保生成机械峰值。 注:同步 IMU 信号需要机械峰值。IMU 信号的同步在数据处理过程中执行（第 5 节）。当使用市售传感器时，无需此部分。在这种情况下，使用相应的软件同步传感器。</li>
	</ol>
</li>
	<li>将IMUs连接到受试者的身体（图1）。<ol>
<li>在以下解剖位置剃掉受试者的身体毛发:在后部上部的 iliac 脊椎、右侧和左侧头骨的异常骨质部分以及左右大腿的侧面部分（即腹膜异常）之间的囊中。 注:传感器应放置的解剖位置可以通过触觉确定。</li>
		<li>在步骤 2.5.1 中描述的解剖位置喷洒胶粘剂喷雾。等待 5+10 s 以确保粘合剂喷雾干燥。 注意:将喷雾剂从皮肤上保持至少 10 厘米（4 英寸），然后用扫地运动喷洒所需的区域。</li>
		<li>从 IMUs 中去除双面胶带的保护层。</li>
		<li>将 IMU 放置在描述的解剖位置。用 IMU 的相应标签写下解剖位置（例如，右柄:IMU 1）。</li>
		<li>将拉伸胶带连接到每个 IMU 的顶部，以确保传感器被附加固定在皮肤上。</li>
	</ol>
</li>
</ol>3. IMU 传感器校准
<ol><li>指示受试者静止不动，双脚臀部宽度分开，双手侧身。将此位置保持至少 5 s。</li>
	<li>指示受试者在下垂平面中将左臀部和膝盖弯曲至 90° 角，然后将臀部扩展到第 3.1 步中描述的中性位置。 注:有关运动变量的定义，请参阅图 2。</li>
	<li>右臀部和膝盖重复步骤3.2。</li>
	<li>指示参与者将行李箱向地面弯曲，并返回到中立位置。</li>
	<li>等待至少5s的周期。</li>
	<li>重复步骤 3.1=3.5 一次。</li>
</ol>4. 执行 30 米线性冲刺测试
<ol><li>指示主题进行热身（例如，足球特定的国际足联11+热身计划20）。</li>
	<li>通知受试者有关测试协议的信息。<ol>
<li>明确指出，在测试期间受伤的风险并不高于正常训练期间，受试者可以随时中止测试，而无需给出任何理由。</li>
		<li>指示受试者站在正确的起跑位置，他们的首选脚站在起跑线上，肩膀站在场上的起跑线后面。</li>
		<li>指示主题，测试领导将从 3 倒数到 0，然后大喊"开始"。指示当"开始"被调用时，测试开始。</li>
		<li>通知受试者尽可能快地冲刺，直到达到30米终点。在达到30米终点后，主体必须尽快减速到静止状态。</li>
		<li>允许被试者提问。如有必要，允许受试者执行练习运行，以便将主题与协议熟悉。</li>
	</ol>
</li>
	<li>询问主题说明是否清楚。</li>
	<li>确保主题处于正确的起始位置。</li>
	<li>从"3"倒计时到"0"，并大喊"开始"以开始测试。在给出开始符号时启动定时器。</li>
	<li>鼓励主题以达到最大性能。</li>
	<li>当参与者达到静止状态时，停止超时器。</li>
	<li>重复步骤 4.4=4.6，直到执行了三个冲刺。 注意:让参与者在试验之间休息至少2分钟。包括数据分析的最快冲刺。</li>
	<li>指示受试者进行冷却。</li>
	<li>通过从受试者的身体中取出拉伸胶带和双面胶带，将 IMUs 从主体中分离。</li>
</ol>5. 数据处理
<ol><li>使用 USB 电缆将 IMU 与计算机连接起来。将原始 IMU 数据导出到计算机上的特定文件夹。</li>
	<li>打开 MATLAB（版本 r2018b）。导入原始 IMU 数据文件（即加速度计、陀螺仪和磁力计数据）。</li>
	<li>同步原始 IMU 数据文件。<ol>
<li>导入一个传感器的加速数据文件（例如骨盆传感器）。</li>
		<li>通过区分 X、Y 和 Z 加速度信号来计算挺举。总结X，Y和Z抽搐，以获得总挺举。</li>
		<li>通过在数据文件中查找总抽搐已达到最大值的索引值来获取机械峰值。索引值是测量的开始。</li>
		<li>在传感器的指数值之前删除加速数据、磁力计数据和陀螺仪数据的所有数据点。</li>
		<li>对于相应传感器的每个原始数据文件，重复步骤 5.3.1=5.3.3。</li>
		<li>通过获取每个数据文件的样本数据点数，确定哪个传感器包含的数据点数量最少。</li>
		<li>剪切与在最短时间内注册信号的传感器大小相等的所有其他数据文件。</li>
	</ol></li>
	<li>使用第二阶低通巴特沃斯滤镜过滤陀螺仪数据，截止频率为 12 Hz。 注:在以前的试点实验中，根据可视数据检查选择过滤器和特定的截止频率。</li>
	<li>通过使用马奇威克滤镜35计算传感器的方向四重奏，获得与全球地球框架的传感器方向。 注:马奇威克等人对全球地球框架传感器方向的计算进行了广泛的描述。</li>
	<li>将传感器坐标帧对齐到车身段。<ol>
<li>在校准过程中，当主体静止不动时，选择数据文件的索引数（步骤 3.1）。 注:假设传感器的纵向轴与重力向量相似。</li>
		<li>使用步骤 5.6.1 的索引数来计算静态校准期间每个传感器相对于全球参考帧的平均方向。然后旋转每个传感器的传感器框架，以便在静态校准期间与全球参考帧对齐。</li>
		<li>执行左腿校准运动时，选择数据文件的索引数（步骤 3.2）。</li>
		<li>旋转左腿传感器的方向，使校准运动仅是前轴的旋转。</li>
		<li>重复步骤 5.6.3 和 5.6.4 用于右腿和躯干的校准运动。</li>
	</ol></li>
	<li>通过在每个关节的近部段的坐标框架中表达断肢体段的方向来获得关节方向。</li>
	<li>通过将获得的关节方向分解为"XZY"欧拉角度来获得关节角度。 注:如何将获得的关节方向分解为"XZY"欧拉角度，在Diebel36的作品中进行了描述。</li>
	<li>获得表达其相应近部段坐标框架中每个断面段的陀螺仪信号的关节角速，减去近部段的角速。</li>
	<li>使用步测算法识别线性冲刺过程中的每一步。<ol>
<li>在 MATLAB 中导入过滤陀螺仪数据。</li>
		<li>使用峰值检测功能识别陀螺仪信号中的峰值。 注:峰值高度设置为 286.5 °s-1， 最小峰值距离设置为 100 个样本（=0.2 s）。</li>
	</ol></li>
	<li>对于每一步，计算臀部角度、膝盖角度、臀部角速和膝盖角速的最大值。</li>
	<li>对于每一步，计算臀部角度、膝盖角度、臀部角速和膝盖角速的最低值。</li>
	<li>对于每一步，通过从最大臀部角度减去最小臀部角度来计算臀部运动范围。</li>
	<li>对于每一步，通过从最大膝盖角度减去最小膝盖角度来计算膝盖的运动范围。</li>
	<li>将已处理的数据保存到计算机上的特定文件夹，以便将其用于进一步分析。</li>
</ol>6. 数据分析
<ol><li>在 MATLAB 中导入已处理的 IMU 数据。</li>
	<li>根据步数检测算法识别的步骤，将冲刺分为加速、最高速度和减速阶段。 注:本文中的冲刺阶段是任意选择的。加速阶段定义为步骤 3 到 837，而减速阶段定义为冲刺的最后八个步骤。最高速度数据源自这些阶段之间执行的步骤。</li>
	<li>选择角度速度数据进行数据分析。</li>
	<li>计算 30 米线性冲刺测试每个阶段所有步骤运动变量的平均值和标准偏差。</li>
	<li>重复步骤 6.3 和 6.4 的角度数据。</li>
</ol>

作者没有什么可透露的。

目前监测运动员在团队运动中的方法没有记录下肢运动学，这可能是识别受伤危险因素的有用措施。在冲刺过程中分析下肢运动学的黄金标准是光电子测量系统29、30、31、32。虽然光电子测量系统是金本位制，但由于测量面积有限，这些系统缺乏生态有效性。本文中提出的传感器设置克服了当前测量系统的局限性，而且相对便宜。通过传感器设置测量，在现场注册下肢运动学的可能性可能会改善运动员的监测实践。
先前的研究，检查冲刺运动学29，31，37，38，39报告臀部角度从210°延伸到90°弯曲。此外，这些研究还报告了膝盖角度从160°最小的弯曲和40°的最大弯曲。本研究中观察到的值在先前报告的范围之内。一项研究38报告臀部角速范围从 -590 °s-s -1到 700 °s-1和膝盖角速度范围从 -1，000 °s-s -1到 1，100 °s-1.虽然在这项研究中观察到的值较高，但随着时间的推移，它们也呈现出类似的趋势。该方法已得到验证，可用于运动员在现场监测40。
目前的研究有一些需要解决的局限性。首先，除了已使用的IMUs的特性外，用户必须知道，来自IMUS的信号受到几个错误源的影响，这些误差源限制了41个应用程序的可能范围。首先，骨骼周围软组织的振荡（即软组织人工制品42）会影响运动学的注册。因此，必须根据协议中描述的步骤，将IMUs仔细地附着在主体的身上。虽然采取了必要的步骤，但应当指出，目前的研究没有包括防止传感器错误移动的额外弹性表带。这可能改善结果，并可能被视为这项研究的局限性。其次，来自其他设备（主要是建筑物内部）的铁磁干扰改变了 IMU 磁力计测量磁场矢量的大小或方向，从而导致估计方向43的错误。因此，应尽可能避免铁凝干扰的来源。此外，必须指出，传感器设置不适用于滑动滑轮，因为传感器会因接触地面而脱离皮肤。因此，应指示参与者不要在小侧游戏期间执行滑动滑动。解决这个问题的一个可能的解决方案可能是将传感器设置集成到智能服装 （即智能传感器紧身衣）中。
传感器设置获得运动变量可用于分段模型，以监控现场的运动员。先前的研究发现，在足球比赛模拟44后，每半场的臀部弯曲和膝盖伸展角度（即理论腿筋长度）减少最大。在同一研究中，在每半结束期间观察到柄角速度的增加。较低的腿筋长度加上刀柄速度的加快可能表明疲劳后腿筋过度拉伤的风险增加。使用惯性测量单元 （IMU） 驱动的细分模型，可以在字段设置中检测到冲刺运动学中的这种变化。除了关节运动学的变化，对身体整体起作用的力量也可以估计。地面反应力 （GRF） 描述了整个肌肉骨骼系统经历的生物力学负荷，并且可以使用牛顿的第二运动定律（即 F = m = a） 进行估计。目前研究在运行中使用GRF估计优化冲刺性能45，46或评估潜在的伤害风险47，48，49，50。这些研究表明，装载率、垂直冲击力峰值和水平断裂力与肌肉骨骼过度使用损伤有关。虽然在高度动态的团队运动特定动作51、52中准确估计 GRF 是一项挑战，但在现场测量期间监控这些变量的可能性可能会提供新的信息来优化性能或防止受伤。
本文介绍的结果仅限于在线性冲刺期间监测下肢运动学，重点是腿筋拉伤机制。然而，应该指出的是，臀部和腹股沟受伤也经常发生在团队运动14，17，53，54，55。这些伤害可能是由踢和改变方向的重复参与造成的。因此，未来的研究不仅应限制他们在与腿筋拉伤机制的关系中冲刺的重点，而且应侧重于扩大有关改变方向任务的知识56和踢57，58，59与臀部和腹股沟受伤的关系。
总之，此传感器设置可以集成到智能服装中。智能服装可以在团队运动特定任务期间在运动场注册下肢运动学，从而改善未来对运动员的监测。这可以帮助日常运动环境中的专业人士评估他们的训练计划并优化他们，以降低受伤风险。

团队运动（如足球和曲棍球）的特点是交替的短暂爆炸性动作，如高强度跑步或短跑，较长的步行或慢跑等要求较低的活动时间较长，如步行或慢跑1，2，3，4，5，6。在过去的几十年里，游戏的身体需求随着高速和短跑、更快的球速和更多的传球7、8的距离而演变。
运动员不断刻苦训练，以保持和提高身体承受比赛身体要求的能力。正确应用训练刺激与足够的恢复相结合，诱导反应，导致人体适应，提高体能和性能9。相反，训练刺激和恢复之间的不平衡可能导致长期疲劳和不良的训练反应（不适应），这增加了职业和业余团队运动运动员10，11，12，13受伤的风险。
伴随高训练和比赛刺激的主要风险之一是肌肉拉伤。肌肉拉伤占团队运动中所有时间损失伤害的三分之一以上，导致超过四分之一的伤病缺席，其中腿筋是最常见的涉及14，15，16，17。此外，每年遭受腿筋拉伤的运动员人数每年增加18、19人，尽管已经引进了多个项目来预防腿筋拉伤12、13、20、21。因此，从运动22和财务23的角度来看，这具有负面影响，因此，对个别运动员的充分监测对于优化训练计划、尽量减少受伤风险和优化成绩至关重要。
目前运动员在团队运动中的监测实践主要基于由本地或全球定位系统24、25测量的位置数据。这些系统以 GPS 为基础的指标（如覆盖的距离、平均运行速度或基于加速度的指标，如 PlayerLoad26 、27、28）来监控活动。这些措施的缺点是，它们不包括下肢运动学。光电测量系统作为黄金标准，在直线冲刺29、30、31、32期间对下肢进行运动分析。这些系统的缺点是缺乏生态有效性，因为其测量区域有限，需要专家来操作该系统和耗时的数据分析。因此，这种方法不适合日常运动练习。
传感器技术的飞速发展可以克服目前监测运动员的方法的局限性。惯性测量单元 （IMU） 最近的可靠性、小型化和数据存储可能性使传感器技术能够在现场应用。IMUs包含一个加速度计、陀螺仪和磁力计，分别在33、34个正交轴中测量加速度、角度速度和磁场。通过将传感器安全地固定在车身段、传感器融合算法和生物力学模型中，可以估计联合运动学33。联合运动学的注册，结合不同身体部位加速的信息，可以改善运动员对团队运动的监测。
通过将 IMU 传感器设置与标准化现场测试耦合在一起，可以说明在野外线性冲刺期间，下肢运动学是如何注册的，这可能是识别伤害风险因素的有用措施。传感器设置可以为当前监控措施提供额外信息，专业人员可以使用这些信息来优化培训计划，以提高性能并最大限度地降低伤害风险。因此，本文的主要目的是演示一个惯性传感器设置，用于估算田径运动中团队运动运动员的臀部和膝盖关节运动学。

<table><tbody><tr><td>Computer software</td><td>The MathWorks, Inc., Natick, MA, USA</td><td></td><td>Matlab Version 2018b</td></tr><tr><td>Cones</td><td>Nike</td><td></td><td>n = 4</td></tr><tr><td>Double-sided adhesive tape</td><td></td><td></td><td>For attaching IMUs on the skin</td></tr><tr><td>Inertial Measurement Units</td><td>MPU-9150, Invensense, San Jose, California, United States</td><td></td><td>n = 5; Dimensions: 3.5 x 2.5 x 1.0 cm; Weight: 0,011 kg; Sample frequency: 500Hz; Accelerometer: &amp;plusmn; 16 G, Gyroscope: &amp;plusmn; 2000 &amp;deg;/s</td></tr><tr><td>Measuring tape</td><td></td><td></td><td>Minimal length: 30 meters</td></tr><tr><td>Pre-tape spray</td><td>Mueller Tuffner, Mueller Sports Medicine, Inc., Wisconsin, United States</td><td></td><td>Contents: 283 g</td></tr><tr><td>Stretch Tape</td><td>Fixomull, BSN Medical, Almere, The Netherlands</td><td></td><td></td></tr></tbody></table>

an inertial measurement unit based method to estimate hip and knee joint kinematics in team sport athletes on the field

目前团队运动中的运动员监测实践主要基于全球定位或本地定位系统测量的位置数据。这些测量系统的缺点是，它们没有记录下肢运动学，这可能是识别伤害危险因素的有用措施。传感器技术的快速发展可以克服当前测量系统的局限性。通过惯性测量单元（IMUs）安全地固定在车身段、传感器融合算法和生物力学模型中，可以估计联合运动学。本文的主要目的是演示一个传感器设置，用于估计田径场上团队运动运动员的臀部和膝盖关节运动学。5个男性科目（年龄22.5±2.1岁:体重77.0±3.8公斤:身高184.3±5.2厘米:训练经验15.3±4.8岁）进行了30米线性冲刺。臀部和膝盖关节角度和角度速度是由放在骨盆上的五个IMUs获得的，大腿和双柄。臀部角度范围从 195° （± 8°） 扩展到 100.5° （± 8°） 弯曲和膝盖角度范围从 168.6° （± 12°） 最小的弯曲和 62.8° （± 12°） 最大弯曲。此外，臀部角速范围介于 802.6 °s-1 （± 192 °s-1）和 -674.9 °s-1 （± 130 °s-1）之间。膝盖角度速度介于 1155.9 °s-s -1 （± 200 °s-1）和 -1208.2 °s-1 （± 264 °s-1）之间。传感器设置已得到验证，可以提供有关运动员在现场监测的其他信息。这可以帮助日常运动环境中的专业人士评估他们的训练计划，旨在减少伤害和优化性能。

五个科目（全部为男性;所有足球运动员;年龄22.5±2.1岁;体重77.0±3.8公斤;身高184.3±5.2厘米:训练经验15.3±4.8岁）进行了30米线性冲刺。臀部角度范围在 100.5° （± 8°） 最大弯曲和 183.1° （± 8°） 最大扩展之间， 104.1° （± 8°） 最大弯曲和 195° （± 8°） 最大扩展在最高速度，128.4° （± 11°） 最大弯曲和 171.9° （± 23°） 减速期间最小弯曲。臀部角度速度在加速期间介于 744.9 °s-s -1 （± 154 °s-1）和 –578 °s-1 （± 99°s-1）之间， 802.6 °s-1 （± 192 °s-1）和 -674.9 °s-s -1 （± 130 °s-1） 在最高速度， 减速期间为 447.7 °s-1 （± 255 °s-1）和 -430.3 °s-1 （± 189 °s-1）。
此外，膝盖角度范围在 73.5° （± 12°） 最大弯曲和 162.6° （± 7°） 之间，在加速度期间最小弯曲， 62.8° （± 12°） 最大弯曲和 164.8° （± 6°） 在最高速度下的最小弯曲，以及 81.1° （± 16°） 最大弯曲和 168.6° （± 12°） 减速期间的最小弯曲。膝盖角速在加速期间在 935.8 °s-s -1 （± 186 °s-1）和 -1137.8 °s-1 （± 214 °s_s+1） 之间变化， 最高时速为 1155.9 °s-1 （± 200 °s-1）和 -1208.2 °s-1 （± 264 °s-1）之间， 和 1000.1 °s-1 （± 282 °s-1）和 -1004.3 °s-1 （± 324 °s-1）。图 3显示了线性 30 米冲刺测试的一个试验的连续运动数据，而图 4和图 5则说明了一个试验的加速、最高速度和减速期间单步周期的运动数据。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60857/60857fig1.jpg"> 图1:传感器放置的表示。 （A） 传感器放置在左右柄上。（B） 传感器放置在骨盆上，以及左右大腿上。 <a href="https://www.jove.com/files/ftp_upload/60857/60857fig1large.jpg" target="_blank">请点击这里查看此数字的较大版本。</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60857/60857fig2.jpg"> 图2:臀部和膝盖关节角度和角度速度的定义。 （A） 代表下垂平面中的中性位置。中性位置的关节角度为 180°。（B） 表示臀部关节（θ臀部）、膝盖关节（θ膝盖）和运动范围（ROM）。 <a href="https://www.jove.com/files/ftp_upload/60857/60857fig2large.jpg" target="_blank">请点击这里查看此数字的较大版本。</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60857/60857fig3.jpg"> 图3:在加速、最高速度和减速阶段，一个试验的冲刺运动学可视化。 星号表示已检测到步骤。（A） 左右臀部弯曲和延伸角度随着时间的推移。（B） 随着时间的推移，左右臀部角速度。（C） 随着时间的推移，左膝和右膝的角度。（D） 随着时间的推移，左膝和右膝角速度。 <a href="https://www.jove.com/files/ftp_upload/60857/60857fig3large.jpg" target="_blank">请点击这里查看此数字的较大版本。</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60857/60857fig4.jpg"> 图4:极地图，其中臀部关节角度（°）和一步角速度（弯曲/延伸）在加速、最高速度和减速过程中被说明。<a href="https://www.jove.com/files/ftp_upload/60857/60857fig4large.jpg" target="_blank">请点击这里查看此数字的较大版本。</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/60857/60857fig5.jpg"> 图 5:在加速、最高速度和减速过程中显示膝盖关节角 （°） 和一步角速度（弯曲/延伸）的极地图。<a href="https://www.jove.com/files/ftp_upload/60857/60857fig5large.jpg" target="_blank">请点击这里查看此数字的较大版本。</a>

Watch this Scientific Journal Video about An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field at JoVE.com

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

监测运动员对于提高团队运动成绩和降低受伤风险至关重要。目前监测运动员的方法不包括下肢。将多个惯性测量单元连接到下肢可以改善对运动员的监测。

一种基于惯性测量单元的方法，以评估场上团队运动运动员的臀部和膝盖关节运动学

Current athlete monitoring practice in team sports is mainly based on positional data measured by global positioning or local positioning systems. The ...

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Research

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Moment of Inertia

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7.9K 次观看.  University of Groningen. 监测运动员对于提高团队运动成绩和降低受伤风险至关重要。目前监测运动员的方法不包括下肢。将多个惯性测量单元连接到下肢可以改善对运动员的监测。

视频: An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning process. Here a method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process. This discovery-learning task uses an infant activated mobile that rotates and plays music based on specified leg action of infants. Supine infants activate the mobile by moving their feet vertically across a virtual threshold. This paradigm is unique in that as infants independently discover that their leg actions activate the mobile, the infants&#8217; leg movements are tracked using a motion capture system allowing for the quantification of the learning process. Specifically, learning is quantified in terms of the duration of mobile activation, the position variance of the end effectors (feet) that activate the mobile, changes in hip-knee coordination patterns, and changes in hip and knee muscle torque. This information describes infant exploration and exploitation at the interplay of person and environmental constraints that support task-specific action. Subsequent research using this method can investigate how specific impairments of different populations of infants at risk for movement disorders influence the discovery-learning process for task-specific action.

A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process.

量化学习婴幼儿:跟踪腿操作期间发现，学习任务

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning ...

科研

行为学

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

The purpose of this study was two-fold: 1) demonstrate a technique that can be used to directly estimate the inertial properties of a below-knee prosthesis, and 2) contrast the effects of the proposed technique and that of using intact limb inertial properties on joint kinetic estimates during walking in unilateral, transtibial amputees. An oscillation and reaction board system was validated and shown to be reliable when measuring inertial properties of known geometrical solids. When direct measurements of inertial properties of the prosthesis were used in inverse dynamics modeling of the lower extremity compared with inertial estimates based on an intact shank and foot, joint kinetics at the hip and knee were significantly lower during the swing phase of walking. Differences in joint kinetics during stance, however, were smaller than those observed during swing. Therefore, researchers focusing on the swing phase of walking should consider the impact of prosthesis inertia property estimates on study outcomes. For stance, either one of the two inertial models investigated in our study would likely lead to similar outcomes with an inverse dynamics assessment.

Body segmental inertial properties are required for inverse dynamics modeling. Using an oscillation and reaction board technique, inertial properties of below-knee prostheses were measured. Using direct measures of prosthesis inertia in the inverse dynamics model of the prosthetic leg resulted in lower magnitudes of resultant joint forces and moments.

对于膝下义肢的估算惯性振荡性质与反应板技术

The purpose of this study was two-fold: 1) demonstrate a technique that can be used to directly estimate the inertial properties of a below-knee ...

生物工程

Biomechanical Analysis Methods to Assess Professional Badminton Players' Lunge Performance

Under the condition of simulating a badminton court in the laboratory, this study used the injury mechanism model to analyze the maximal right lunge movements of eight professional badminton players and eight amateur players. The purpose of this protocol is to study the differences in kinematics and joint moment of the right knee and ankle. A motion capture system and force plate were used to capture data of the joint movements of the lower extremity and the vertical ground reaction force (vGRF). Sixteen young men who did not have any sports injuries in the past 6 months took part in the study. The subjects performed a maximal right lunge from the start position with their right foot, stepping on and fully contacting with the force plate, hit the shuttlecock with an underhand stroke to the designated position in the backcourt, and then returned to the start/end position. All subjects wore the same badminton shoes to avoid a difference in impact from different badminton shoes. The amateur players showed a greater range of ankle movement and reverse joint moment on the frontal plane, and a larger internal joint rotation moment on the horizontal plane. The professional badminton players exhibited greater knee moment on the sagittal and frontal planes. Therefore, these factors should be considered in the development of the training program to reduce the risk of sports injuries in knee and ankle joints. This study simulates the real badminton court and calibrates the range of activities of each movement of the subjects so that the subjects complete the experimental action in a natural state with high quality. A limitation of this study is that it does not combine joint load and muscle activity. Another limitation is that the sample size is small and should be expanded in future studies. This research method can be applied to the lower limb biomechanical research of other footwork in the badminton project.

Here, we present a protocol to evaluate the differences in injury mechanisms between professional and amateur players when performing a badminton maximal right lunge movement by analyzing lower limb kinematics.

生物力学分析方法,以评估职业羽毛球运动员的隆起表现

Under the condition of simulating a badminton court in the laboratory, this study used the injury mechanism model to analyze the maximal right lunge ...

A Novel Application of Musculoskeletal Ultrasound Imaging

Ultrasound is an attractive modality for imaging muscle and tendon motion during dynamic tasks and can provide a complementary methodological approach for biomechanical studies in a clinical or laboratory setting. Towards this goal, methods for quantification of muscle kinematics from ultrasound imagery are being developed based on image processing. The temporal resolution of these methods is typically not sufficient for highly dynamic tasks, such as drop-landing. We propose a new approach that utilizes a Doppler method for quantifying muscle kinematics. We have developed a novel vector tissue Doppler imaging (vTDI) technique that can be used to measure musculoskeletal contraction velocity, strain and strain rate with sub-millisecond temporal resolution during dynamic activities using ultrasound. The goal of this preliminary study was to investigate the repeatability and potential applicability of the vTDI technique in measuring musculoskeletal velocities during a drop-landing task, in healthy subjects. The vTDI measurements can be performed concurrently with other biomechanical techniques, such as 3D motion capture for joint kinematics and kinetics, electromyography for timing of muscle activation and force plates for ground reaction force. Integration of these complementary techniques could lead to a better understanding of dynamic muscle function and dysfunction underlying the pathogenesis and pathophysiology of musculoskeletal disorders.

We describe a new ultrasound-based vector tissue Doppler imaging technique to measure muscle contraction velocity, strain and strain rate with sub-millisecond temporal resolution during dynamic activities. This approach provides complementary measurements of dynamic muscle function and could lead to a better understanding of mechanisms underlying musculoskeletal disorders.

肌肉骨骼超声成像的一种新应用

Ultrasound is an attractive modality for imaging muscle and tendon  motion during dynamic tasks and can provide a complementary methodological  approach ...

医学

Biplanar Videoradiography Dataset for Model-based Pose Estimation Development and New User Training

Measuring the motion of the small foot bones is critical for understanding pathological loss of function. Biplanar videoradiography is well-suited to measure in vivo bone motion, but challenges arise when estimating the rotation and translation (pose) of each bone. The bone's pose is typically estimated with marker- or model-based methods. Marker-based methods are highly accurate but uncommon in vivo due to their invasiveness. Model-based methods are more common but are currently less accurate as they rely on user input and lab-specific algorithms. This work presents a rare in vivo dataset of the calcaneus, talus, and tibia poses, as measured with marker-based methods during running and hopping. A method is included to train users to improve their initial guesses into model-based pose estimation software, using marker-based visual feedback. New operators were able to estimate bone poses within 2&#176; of rotation and 1 mm of translation of the marker-based pose, similar to an expert user of the model-based software, and representing a substantial improvement over previously reported inter-operator variability. Further, this dataset can be used to validate other model-based pose estimation software. Ultimately, sharing this dataset will improve the speed and accuracy with which users can measure bone poses from biplanar videoradiography.

<meta charset="utf8"></head><body>用于基于模型的姿态估计开发和新用户培训的双平面视频射线照相数据集

This work presents an in vivo dataset with bone poses estimated with marker-based methods. A method is included here to train operators in improving their initial estimates for model-based pose estimation and reducing inter-operator variability.

用于基于模型的姿态估计开发和新用户培训的双平面视频射线照相数据集

Measuring the motion of the small foot bones is critical for understanding pathological loss of function. Biplanar videoradiography is well-suited to ...

工程学

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Bone stress injuries are common in sports and military trainings. Repetitive large ground impact forces during training could be the cause. It is essential to determine the effect of high ground impact forces on lower-body bone deformation to better understand the mechanisms of bone stress injuries. Conventional strain gauge measurement has been used to study in vivo tibia deformation. This method is associated with limitations including invasiveness of the procedure, involvement of few human subjects, and limited strain data from small bone surface areas. The current study intends to introduce a novel approach to study tibia bone strain under high impact loading conditions. A subject-specific musculoskeletal model was created to represent a healthy male (19 years, 80 kg, 1,800 mm). A flexible finite element tibia model was created based on a computed tomography (CT) scan of the subject&#39;s right tibia. Laboratory motion capture was performed to obtain kinematics and ground reaction forces of drop-landings from different heights (26, 39, 52 cm). Multibody dynamic computer simulations combined with a modal analysis of the flexible tibia were performed to quantify tibia strain during drop-landings. Calculated tibia strain data were in good agreement with previous in vivo studies. It is evident that this non-invasive approach can be applied to study tibia bone strain during high impact activities for a large cohort, which will lead to a better understanding of injury mechanism of tibia stress fractures.

During landing, lower-body bones experience large mechanical loads and are deformed. It is essential to measure bone deformation to better understand the mechanisms of bone stress injuries associated with impacts. A novel approach integrating subject-specific musculoskeletal modeling and finite element analysis is used to measure tibial strain during dynamic movements.

动态运动过程中骨应变的学科特异性肌肉模型研究

Bone stress injuries are common in sports and military trainings. Repetitive large ground impact forces during training could be the cause. It is ...

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

The use of smartphones in clinical practice is steadily increasing with the availability of low cost/freely available &#34;apps&#34; that could be used to assess human gait. The primary aim of this manuscript is to test the concurrent validity of kinematic measures recorded by a smartphone application in comparison to a 3D motion capture system in the sagittal plane. The secondary aim was to develop a protocol for clinicians on the set up of the smartphone camera for video movement analysis.
The sagittal plane knee angle was measured during heel strike and toe off events using the smart phone app and a 3D motion-capture system in 32 healthy subjects. Three trials were performed at near (2-m) and far (4-m) smartphone camera distances. The order of the distances was randomized. Regression analysis was performed to estimate the height of the camera based on either the subject&#39;s height or leg length.
Absolute measurement errors were least during toe off (3.12 &#177; 5.44 degrees) compared to heel strike (5.81 &#177; 5.26 degrees). There were significant (p &#60; 0.05) but moderate agreements between the application and 3D motion capture measures of knee angles. There were also no significant (p &#62; 0.05) differences between the absolute measurement errors between the two camera positions. The measurement errors averaged between 3 - 5 degrees during toe off and heel strike events of the gait cycle.
The use of smartphone apps can be a useful tool in the clinic for performing gait or human movement analysis. Further studies are needed to establish the accuracy in measuring movements of the upper extremity and trunk.

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

This manuscript describes the method to test the concurrent validity of kinematic measures recorded by the smartphone application in comparison to a 3D motion capture system in the sagittal plane. This protocol will enable clinicians to set up smartphones for video capture of human movement.

视频运动分析使用智能手机（ViMAS）的初步研究

The use of smartphones in clinical practice is steadily increasing with the availability of low cost/freely available &#34;apps&#34; that could be used to ...

Biplanar Videoradiography to Study the Wrist and Distal Radioulnar Joints

Accurate measurement of skeletal kinematics in vivo is essential for understanding normal joint function, the influence of pathology, disease progression, and the effects of treatments. Measurement systems that use skin surface markers to infer skeletal motion have provided important insight into normal and pathological kinematics, however, accurate arthrokinematics cannot be attained using these systems, especially during dynamic activities. In the past two decades, biplanar videoradiography (BVR) systems have enabled many researchers to directly study the skeletal kinematics of the joints during activities of daily living. To implement BVR systems for the distal upper extremity, videoradiographs of the distal radius and the hand are acquired from two calibrated X-ray sources while a subject performs a designated task. Three-dimensional (3D) rigid-body positions are computed from the videoradiographs via a best-fit registrations of 3D model projections onto&#160;to each BVR view. The 3D models are density-based image volumes of the specific bone derived from independently acquired computed-tomography data. Utilizing graphics processor units and high-performance computing systems, this model-based tracking approach is shown to be fast and accurate in evaluating the wrist and distal radioulnar joint biomechanics. In this study, we first summarized the previous studies that have established the submillimeter and subdegree agreement of BVR with an in vitro&#160;optical motion capture system in evaluating the wrist and distal radioulnar joint kinematics. Furthermore, we used BVR to compute the center of rotation behavior of the wrist joint, to evaluate the articulation pattern of the components of the implant upon one another, and to assess the dynamic change of ulnar variance during pronosupination of the forearm. In the future, carpal bones may be captured in greater detail with the addition of flat panel X-ray detectors, more X-ray sources (i.e., multiplanar videoradiography), or advanced computer vision algorithms.

<meta charset="utf8"></head><body>双平面视频 X 线照相研究腕关节和远端桡尺关节

Biplanar videoradiography (BVR) is an advanced imaging technique for understanding the three-dimensional movement of skeletal bones and implants. Combining density-based image volumes and videoradiographs of the distal upper extremity, BVR is used to study the in vivo motion of the wrist and distal radioulnar joint, as well as joint arthroplasties.

双平面视频 X 线照相研究腕关节和远端桡尺关节

Accurate measurement of skeletal kinematics in vivo is essential for understanding normal joint function, the influence of pathology, disease progression, ...

一种基于惯性测量单元的方法，以评估场上团队运动运动员的臀部和膝盖关节运动学

摘要

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生物力学分析方法,以评估职业羽毛球运动员的隆起表现

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