Name: measuring the kinematics of daily living movements with motion capture systems in virtual reality
Uploaded: 2018-04-05T14:00:00.000+00:00
Duration: 8 min 45 s
Description: Watch this Scientific Journal Video about Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality at JoVE.com

K.S. 和 A.L. 贡献平等。这项研究得到了由科学、信息和通信技术 & 未来规划 (NRF-2016R1D1A1B03931389) 资助的韩国国家研究基金会 (NRF) 的基础科学研究项目的支持。

根据赫尔辛基 (HYI-15-029-2) 的声明, 汉阳大学的机构审查委员会批准了这里描述的所有实验程序。6健康控制 (4 男性和2女性) 和6个 MCI 患者 (3 个男性和3位女性) 从汉阳大学医院的第三个医疗中心被招募了。
1. 招募参加者
<ol><li>招募 MCI 患者 (即, IADL 功能受损) 和健康控制 (即, 正常 IADL 功能), 年龄介于 70-80 岁之间。</li>
	<li>在有10年临床经验的神经科医生的帮助下, 回顾病人的病史, 排除患有神经系统/精神疾病或脑外科病史的病人。 注: 使用以下神经心理学测试: 迷你心理状态检查-痴呆筛查, 韩国日常生活工具活动, 免费和提示选择性提醒测试, 数字跨度测试-前/后, 轨迹制作测试-A/B13和阿尔伯特等的标准。14诊断 MCI。</li>
</ol>2. 安装 VR 软件并连接计算机
<ol><li>在专用机房中安装与图 1类似的硬件。在房间大小的沉浸式虚拟环境 (4 x 2.5 x 2.5 m3) 中执行此协议, 其中包含4台计算机、4个立体的三维 (3D) 投影仪和8个运动跟踪摄像机来跟踪占主导地位的手的位置和方向, 并头在两个 IADL 任务。 注意: 本文使用的 VR 技术是计算机硬件和软件, 提供身临其境和交互式3D 体验, 在虚拟环境中可以呈现逼真的对象和事件。硬件和软件的详细信息在材料表中进行了说明。</li>
	<li>确保所有计算机都配备了所需的软件 (Visual Studio 2012 可再发行包 (x86)、DirectX 和 MiddleVR, 或等同)。对于 MiddleVR即中间件软件, 请检查网站15以获取输入设备、立体、群集和交互的最新版本的库。</li>
	<li>将计算机连接到立体3D 投影仪。图形设置为 1920 x 1080 像素分辨率。</li>
	<li>创建 Windows 10 家庭组以将4台计算机连接到家庭网络。在主计算机上, 创建一个文件夹并与其他家庭组计算机共享它。</li>
	<li>在主计算机上, 启动中间件软件。单击 "群集" 按钮。将主计算机设置为服务器和其他计算机作为客户端。这将同步所有设备的状态。单击 "3D 节点" 按钮。指定虚拟环境屏幕的位置、方向和大小。</li>
	<li>根据网站15完成设置并保存配置文件。</li>
</ol>3. 在虚拟环境中设置运动捕获系统
<ol><li>在虚拟环境中装载8个运动跟踪摄像机以完全覆盖捕获卷。安全地固定摄像机, 以便在捕获过程中保持静止。确保虚拟环境中的对象在任何时候都至少能看到2个摄像头。</li>
	<li>使用安装手册16在主计算机上安装 OptiTrack 动机软件即动作捕获软件。将主计算机与具有6类以太网缆线的运动捕获系统连接起来。</li>
	<li>按照软件手册16中的详细说明, 校准动作捕捉系统, 步骤如下。<ol>
<li>从捕获卷中删除所有无关的反射或不必要的标记。</li>
		<li>单击 "蒙版可见" 按钮以掩盖不需要的反射或环境干扰。</li>
		<li>单击 "开始畹町" 按钮。使用校准魔杖来支持捕获样本帧, 以便在3D 空间中计算各自的位置和方向。</li>
		<li>单击 "计算" 按钮以使用收集的示例校准系统。</li>
		<li>检查校准结果 (按顺序从最坏到最好): 贫穷, 公平, 好, 伟大, 优秀, 和特殊。如果结果比较大, 请单击 "应用" 按钮。如果没有, 请单击 "取消" 按钮并重复畹町进程。</li>
		<li>将校准正方形放在要放置原点的3D 空间内。单击 "设置地面" 按钮以建立跟踪的3D 坐标系统原点。</li>
		<li>选择相关的反射标记为主导的手和头。单击 "刚性体" 按钮, 然后单击 "从选定标记创建" 按钮。</li>
	</ol>
</li>
	<li>在动作捕捉软件上, 打开 "流式" 菜单。验证列出的端口号为 3883, 并选择 "VRPN 流式引擎" 类别中的 "广播帧数据" 框。单击 "Ctrl" + "S" 以保存校准文件。</li>
	<li>在主计算机上, 启动中间件软件。单击 "设备" 按钮。添加 VRPN 跟踪器以从运动捕获系统获取跟踪数据, 然后保存配置文件。</li>
</ol>4. 准备一个虚拟环境以供使用
<ol><li>从虚拟环境中移除所有反射对象 (即、手表、戒指、耳环、金属、等)。</li>
	<li>打开计算机、立体3D 投影仪和运动捕捉系统 (每秒360帧)。</li>
	<li>4台计算机运行后, 启动 VRDaemon 软件。例如, 双击位于 "C:\Program 文件 (x86) \ MiddleVR \ bin" 中的 "VRDaemon.exe"。</li>
	<li>在主计算机上, 启动动作捕捉软件。单击标记为 "打开现有项目" 的顶部菜单附近的按钮。加载相机校准文件。</li>
	<li>在主计算机上, 启动中间件软件。单击 "模拟" 按钮。从共享文件夹加载适当的模拟和配置文件。</li>
	<li>在中间件软件上, 按 "运行" 按钮执行具有所选模拟和配置文件的沉浸式虚拟应用程序。</li>
</ol>5. 使参与者熟悉虚拟环境
<ol><li>为参加者提供重量约50克的立体眼镜。立体眼镜的显示频率为192赫兹. 确保立体眼镜舒适地放置在眼睛和耳朵上;请参见图 2A。</li>
	<li>附加反射标记, 重量少于1克到参与者的主导手和头部。小心地将反光标记紧密地附着在一起;请参见图 2B。通知参与者, 他们可以使用磁头移动在虚拟环境中自由移动或旋转, 并且可以单击具有主手的虚拟对象。虚拟环境中出现虚拟手, 以模仿参与者食指的位置;请参见图 3。</li>
	<li>让参与者在虚拟环境中自由移动 (即, 站起来, 坐下来, 向左走, 向右转) 5 分钟, 以便熟悉 VR 环境。然后让参与者点击虚拟按钮5分钟, 以熟悉如何与虚拟对象的主导手互动。如果参与者要求的话, 再提供10分钟的培训课程。</li>
	<li>使用模拟器疾病调查表17检查参与者是否对 VR 疾病免疫。 注意: 在立体显示器上的同步运动跟踪可以引起 VR 疾病, 这会导致不适、头痛、胃觉、恶心、呕吐、脸色苍白、出汗、疲劳、嗜睡、迷失方向和冷漠。如果参与者抱怨疲劳或模拟器疾病评分过高, 请停止该协议。</li>
</ol>6. 执行 "任务 1: 提款"
注意: 平衡任务1和任务2的序列以删除结转效果。
<ol><li>向参与者解释任务的详细信息, 并提供8操作步骤以完成虚拟环境中的任务。步骤是 (1) 将卡插入 ATM 机中, (2) 选择 "撤回" 菜单, (3) 选择要撤回的金额, (4) 选择账单类型, (5) 输入 PIN (个人标识号), (6) 选择收据选项, (7) 取出卡, (8) 将钱从ATM (请参见图 4)。</li>
	<li>在主计算机上, 启动中间件软件。在 "模拟" 选项卡上, 为任务1和配置文件选择一个模拟文件。按下 "运行" 按钮;"任务 1: 提款" 将在虚拟环境中运行。 注意: 对于任务1文件, 请参阅补充文件 1中所附的 "任务1撤回钱. zip" 文件。请注意, 虚拟任务是用统一的3D 引擎开发的。</li>
	<li>如果 "任务 1: 提款" 在虚拟环境中运行, 请指示参与者执行如下操作: "从 ATM 中提取7万韩元 (相当于约60美元)" 购物。选择两种不同类型的便笺, 一个5万韩元的5万韩元和两个1万的 krw 音符为2万韩元。您的交易记录的密码是今天的日期。例如, 如果实验是在11月11日进行的, 那么 PIN 是1111。请保留收据以供进一步参考。</li>
	<li>完成任务后, 请检查 CSV 文件中保存的运动学数据 (逗号分隔值), 以便从共享文件夹进行进一步分析。 注: 使用动作捕捉系统时, 在 "任务 1: 取款" 记录的位置和方向的主要手时, 执行一个记录频率为 1 ms。</li>
	<li>在开始 "任务 2: 乘公共汽车" 之前, 给参与者大约5分钟的休息。</li>
</ol>7. 执行 "任务 2: 乘公共汽车"
<ol><li>向参与者解释任务的细节, 并提供有关如何完成 "任务 2: 乘坐公交车" 的说明: "请在汽车站等, 然后乘上目标巴士。目标总线信息将在 VR 屏幕上按特定的行号、颜色和目的地提供。当目标总线到达时, 一定要走出汽车站和目标总线的前门。8种不同的目标总线将随机生成并呈现。请参见图 5。</li>
	<li>在主计算机上, 启动中间件软件。在 "模拟" 选项卡上, 为任务2和配置文件选择一个模拟文件。按下 "运行" 按钮, 然后 "任务 2: 乘公交车" 将在虚拟环境中运行。 注意: 对于任务2文件, 请参阅补充文件 2中所附的 "任务2中的总线. zip" 文件。请注意, 虚拟任务是用统一的3D 引擎开发的。</li>
	<li>如果 "任务 2: 乘坐总线" 在虚拟环境中运行, 请指示参与者在总线停止等待。单击键盘上的 "空格键" 键, 使公共汽车到达汽车站。</li>
	<li>完成任务后, 请检查 CSV 文件中保存的运动学数据, 以便从共享文件夹进行进一步分析。 注: 使用动作捕捉系统时, 在 "任务 2: 乘公交车" 时记录头部的位置和方位, 在执行任务时, 记录频率为1毫秒。</li>
	<li>协议已完成。帮助参与者去除立体眼镜, 从主导的手和头部分离反射标记。</li>
</ol>

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作者声明没有利益冲突。

在身临其境的 VR 环境中, 我们详细介绍了一种运动捕捉系统的日常生活运动测量协议。首先, 实验设置指导如何设置, 准备, 并使参与者熟悉身临其境的虚拟现实环境。其次, 我们在 VR 中开发了两个标准化的 IADL 任务。第三, 步骤3和步骤5在协议部分是最关键的步骤, 以尽量减少 VR 疾病。在虚拟环境中设置运动捕获系统 (步骤 3) 时, 很重要的一点是, 将跟踪摄像机安装到足够高的高度, 以便完全覆盖捕...

日常生活的工具活动 (IADL), 例如处理金融交易, 使用公共交通和烹调, 是医学标记, 因为他们需要多重神经心理学功能1。因此, IADL 能力受损被认为是神经疾病的前体, 如轻度认知障碍 (MCI) 和痴呆症2。黄金对 IADL 任务的全面审查3表示, 更具认知性要求的任务, 例如管理财务和使用公共交通, 是 MCI 和痴呆症的最早预测因素。
迄今为止, 最常用的 IADL 评估是自报告的调查表、基于线人的调查表和基于性能的评估4。基于问卷的 IADL 评估方法具有成本效益和易用性, 但容易引起主观偏见。例如, 当自我报告时, 患者倾向于过度或低估他们的 IADL 能力5。同样, 由于观察者的误解或知识缺口4, 举报人误判 IADL 能力。因此, 要求患者执行特定 IADL 任务的基于绩效的评估是可取的, 尽管许多任务不适用于一般临床设置6。
最近, 虚拟现实 (VR) 研究表明, 这项技术可以在医学和医疗领域有重要的应用, 其中包括从训练到康复到医疗评估的所有内容7。所有参与者都可以在相同的 VR 条件下进行测试, 这会模仿真实世界。例如, Allain et 。8开发了一个虚拟的咖啡制作任务, 并表明有认知障碍的患者执行的任务很差。科林格et 。9为邮件和购物任务开发了另一个虚拟现实环境, 并发现 vr 中任务完成时间与神经心理测试结果之间有意义的关系。以前的 VR 研究 IADL 评估主要集中在简单的性能指标, 如反应时间或准确性时, 使用传统的输入设备, 如鼠标和键盘8,9。因此, 需要更详细的 IADL 性能数据, 以便有效地筛选 MCI4的患者。
实时运动捕获数据的运动学分析是定量记录与 IADL 任务相关的详细性能数据的有力方法。例如, 白色et 。10开发了一个虚拟厨房, 它在日常生活任务期间捕获参与者的联合角度数据, 并使用捕获的数据定量地评估物理治疗的有效性。Dimbwadyo-Terrer et11开发了一个身临其境的 VR 环境来评估在进行基本日常生活任务时上肢的表现, 并表明在 vr 环境中记录的运动学数据与上肢功能尺度高度相关。这些运动学分析与运动捕捉系统可以提供进一步的机会, 以快速评估患者的认知障碍12。将详细的运动学数据纳入 MCI 患者的筛查, 大大改善了与健康对照组比较的患者的分类13。
在这里, 我们描述了一个协议, 以评估在身临其境的 VR 环境中的运动捕捉系统的日常生活运动的运动学。该协议包括两个复杂的 IADL 任务: "任务 1: 取款" (处理金融交易) 和 "任务 2: 乘坐公交车" (使用公共交通)。在完成任务的同时, 一个运动捕捉系统跟踪了支配手和头部的位置和方向。完成任务1后, 收集了主手轨迹、移动距离和完成时间。在任务2中, 收集了头部轨迹、移动距离和完成时间。本文的 "代表性结果" 部分详细介绍了 MCI 患者 (即、IADL 功能受损) 与健康控制 (即、IADL 功能完好) 的初步测试。

<table><tbody><tr><td>Computer</td><td>N/A</td><td>N/A</td><td>Computer requirements:&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; - Single socket H3 (LGA 1150) supports&lt;br/&gt; - Intel&amp;reg; Xeon&amp;reg; E3-1200 v3, 4th gen. Core i7/i5/i3 processors&lt;br/&gt; - Intel&amp;reg; C226 Express PCH&lt;br/&gt; - Up to 32GB DDR3 ECC/non-ECC 1600MHz UDIMM in 4 sockets&lt;br/&gt; - Dual Gigabit Ethernet LAN ports&lt;br/&gt; - 8x SATA3 (6Gbps)&lt;br/&gt; - 2x PCI-E 3.0 x16, 3x PCI-E 2.0 x1, and 2x PCI 5V 32-bit slots&lt;br/&gt; - 6x USB 3.0 (2 rear + 4 via headers)&lt;br/&gt; - 10x USB 2.0 (4 rear + 6 via headers)&lt;br/&gt; - HD Audio 7.1 channel connector by Realtek ALC1150&lt;br/&gt; - 1x DOM power connector and 1x SPDIF Out Header&lt;br/&gt; - 800W High Efficiency Power Supply&lt;br/&gt; - Intel Xeon E3-1230v3&lt;br/&gt; - DDR3 PC12800 8GB ECC&lt;br/&gt; - WD 1TB BLUE WD 10EZEX&amp;nbsp; 3.5&quot;&lt;br/&gt; - NVIDIA QUADRO K5000 &amp;amp; SYNC</td></tr><tr><td>Stereoscopic 3D Projector</td><td>Barco</td><td>F35 AS3D WUXGA</td><td>Resolution: &lt;br /&gt;- WQXGA (2,560 x 1,600) &lt;br /&gt;- Panorama (2,560 x 1,080) &lt;br /&gt;- WUXGA (1,920 x 1,200), 1080p (1,920 x 1,080)</td></tr><tr><td>Stereoscopic Glasses</td><td>Volfoni</td><td>Edge 1.2</td><td>For further information, visit http://volfoni.com/en/edge-1-2/</td></tr><tr><td>Motion Capture Systems</td><td>NaturalPoint OptiTrack</td><td>17W</td><td>For further information, visit http://optitrack.com/products/prime-17w/</td></tr><tr><td>OptiTrack (Motion capture software)</td><td>NaturalPoint OptiTrack</td><td>Motive 2.0</td><td>For further information, visit https://optitrack.com/downloads/motive.html</td></tr><tr><td>MiddleVR (Middleware software)</td><td>MiddleVR</td><td>MiddleVR For Unity</td><td>For further information, visit http://www.middlevr.com/middlevr-for-unity/</td></tr><tr><td>VRDaemon (Middleware software)</td><td>MiddleVR</td><td>MiddleVR For Unity</td><td>For further information, visit http://www.middlevr.com/middlevr-for-unity/</td></tr><tr><td>Unity3D (Game engine)</td><td>Unity Technologies</td><td>Personal</td><td>For further information, visit https://unity3d.com/unity</td></tr></tbody></table>

measuring the kinematics of daily living movements with motion capture systems in virtual reality

无法完成日常生活中的工具性活动 (IADL) 是各种神经心理疾病的前兆。基于问卷的 IADL 评估很容易使用, 但容易引起主观偏见。在这里, 我们描述了一个新的虚拟现实 (VR) 测试, 以评估两个复杂的 IADL 任务: 处理金融交易和使用公共交通。当参与者在 VR 设置中执行任务时, 运动捕获系统会跟踪占主导地位的手和头部在三维笛卡尔坐标系中的位置和方向。运动学原始数据被收集和转换成 ' 运动性能测量, '即, 运动轨迹, 移动距离和完成时间。运动轨迹是空间中特定身体部分 (例如、主导的手或头) 的路径。移动距离指的是轨迹的总距离, 完成 IADL 任务所花费的时间。这些运动措施可能会歧视患者的认知障碍, 从健康的控制。这个运动测量协议的发展允许检测早期 IADL 相关的认知障碍。

用统计软件 R 分析了 "任务 1: 提取资金" 的 CSV 文件, 计算了主手轨迹、移动距离和完成时间。主导手运动的轨迹是可视化的 (图 6)。占主导地位的手的移动距离是通过在执行任务1的同时总结出顺序手位置之间的总距离来计算的。位置之间的距离是欧式距离。完成时间意味着完成整个任务所花费的时间 (即, 从步骤 1 "将卡插入 atm" 到步骤 8 "从 atm...

Watch this Scientific Journal Video about Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality at JoVE.com

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality

我们设计了一个虚拟现实测试, 以评估日常生活中的工具活动 (IADL) 与一个运动捕捉系统。我们提出了一个详细的运动学分析, 以解释参与者的各种运动, 包括弹道, 移动距离, 并完成时间, 以评估 IADL 能力。

虚拟现实中运动捕捉系统的日常生活运动运动学测量

The inability to complete instrumental activities of daily living (IADL) is a precursor to various neuropsychological diseases. Questionnaire-based ...

measuring-kinematics-daily-living-movements-with-motion-capture

Universidad San Sebastian

Research

JoVE Journal

Behavior

7.6K 次观看.  Hanyang University. 我们设计了一个虚拟现实测试, 以评估日常生活中的工具活动 (IADL) 与一个运动捕捉系统。我们提出了一个详细的运动学分析, 以解释参与者的各种运动, 包括弹道, 移动距离, 并完成时间, 以评估 IADL 能力。

视频: Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality

Development of a Virtual Reality Assessment of Everyday Living Skills

Cognitive impairments affect the majority of patients with schizophrenia and these impairments predict poor long term psychosocial outcomes.&#160; Treatment studies aimed at cognitive impairment in patients with schizophrenia not only require demonstration of improvements on cognitive tests, but also evidence that any cognitive changes lead to clinically meaningful improvements.&#160; Measures of &#8220;functional capacity&#8221; index the extent to which individuals have the potential to perform skills required for real world functioning. &#160;Current data do not support the recommendation of any single instrument for measurement of functional capacity.&#160; The Virtual Reality Functional Capacity Assessment Tool (VRFCAT) is a novel, interactive gaming based measure of functional capacity that uses a realistic simulated environment to recreate routine activities of daily living. Studies are currently underway to evaluate and establish the VRFCAT&#8217;s sensitivity, reliability, validity, and practicality.&#160;This new measure of functional capacity is practical, relevant, easy to use, and has several features that improve validity and sensitivity of measurement of function in clinical trials of patients with CNS disorders.

A challenge for proving treatment efficacy for cognitive impairments in schizophrenia is finding the optimizing measurement of skills related to everyday functioning.&#160; The Virtual Reality Functional Capacity Assessment Tool (VRFCAT) is an interactive gaming based computerized measure aimed at skills associated with everyday functioning, including baseline impairments and treatment related changes.

每天生活技能的虚拟现实评估的发展

Cognitive impairments affect the majority of patients with schizophrenia and these impairments predict poor long term psychosocial outcomes.&#160; ...

科研

行为学

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

The study of neuromuscular control of movement in humans is accomplished with numerous technologies. Non-invasive methods for investigating neuromuscular function include transcranial magnetic stimulation, electromyography, and three-dimensional motion capture. The advent of readily available and cost-effective virtual reality solutions has expanded the capabilities of researchers in recreating &#8220;real-world&#8221; environments and movements in a laboratory setting. Naturalistic movement analysis will not only garner a greater understanding of motor control in healthy individuals, but also permit the design of experiments and rehabilitation strategies that target specific motor impairments (e.g. stroke). The combined use of these tools will lead to increasingly deeper understanding of neural mechanisms of motor control. A key requirement when combining these data acquisition systems is fine temporal correspondence between the various data streams. This protocol describes a multifunctional system&#8217;s overall connectivity, intersystem signaling, and the temporal synchronization of recorded data. Synchronization of the component systems is primarily accomplished through the use of a customizable circuit, readily made with off the shelf components and minimal electronics assembly skills.

Transcranial magnetic stimulation, electromyography, and 3D motion capture are commonly used non-invasive techniques for investigating neuromuscular function in humans. In this paper, we describe a protocol that synchronously samples data generated by all three of these tools along with the unique addition of virtual reality stimulus presentation and feedback.

多功能设置研究人类电机控制用经颅磁刺激，肌电图，运动捕捉和虚拟现实

The study of neuromuscular control of movement in humans is accomplished with numerous technologies. Non-invasive methods for investigating neuromuscular ...

Movement Retraining using Real-time Feedback of Performance

Any modification of movement - especially movement patterns that have been honed over a number of years - requires re-organization of the neuromuscular patterns responsible for governing the movement performance. This motor learning can be enhanced through a number of methods that are utilized in research and clinical settings alike. In general, verbal feedback of performance in real-time or knowledge of results following movement is commonly used clinically as a preliminary means of instilling motor learning. Depending on patient preference and learning style, visual feedback (e.g. through use of a mirror or different types of video) or proprioceptive guidance utilizing therapist touch, are used to supplement verbal instructions from the therapist. Indeed, a combination of these forms of feedback is commonplace in the clinical setting to facilitate motor learning and optimize outcomes.
Laboratory-based, quantitative motion analysis has been a mainstay in research settings to provide accurate and objective analysis of a variety of movements in healthy and injured populations. While the actual mechanisms of capturing the movements may differ, all current motion analysis systems rely on the ability to track the movement of body segments and joints and to use established equations of motion to quantify key movement patterns. Due to limitations in acquisition and processing speed, analysis and description of the movements has traditionally occurred offline after completion of a given testing session.
This paper will highlight a new supplement to standard motion analysis techniques that relies on the near instantaneous assessment and quantification of movement patterns and the display of specific movement characteristics to the patient during a movement analysis session. As a result, this novel technique can provide a new method of feedback delivery that has advantages over currently used feedback methods.

Retraining abnormal movement patterns following injury or disease is a key component of physical rehabilitation. Recent advances in technology have permitted accurate assessment of movement during a variety of tasks, with near instantaneous quantification of results. This provides new opportunities for modification of faulty movement patterns in real time.

的运动再培训采用实时反馈的性能

Any modification of movement - especially movement patterns that have been honed over a number of years - requires re-organization of the neuromuscular ...

医学

Virtual Reality Experiments with Physiological Measures

Virtual reality (VR) experiments are increasingly employed because of their internal and external validity compared to real-world observation and laboratory experiments, respectively. VR is especially useful for geographic visualizations and investigations of spatial behavior. In spatial behavior research, VR provides a platform for studying the relationship between navigation and physiological measures (e.g., skin conductance, heart rate, blood pressure). Specifically, physiological measures allow researchers to address novel questions and constrain previous theories of spatial abilities, strategies, and performance. For example, individual differences in navigation performance may be explained by the extent to which changes in arousal mediate the effects of task difficulty. However, the complexities in the design and implementation of VR experiments can distract experimenters from their primary research goals and introduce irregularities in data collection and analysis. To address these challenges, the Experiments in Virtual Environments (EVE) framework includes standardized modules such as participant training with the control interface, data collection using questionnaires, the synchronization of physiological measurements, and data storage. EVE also provides the necessary infrastructure for data management, visualization, and evaluation. The present paper describes a protocol that employs the EVE framework to conduct navigation experiments in VR with physiological sensors. The protocol lists the steps necessary for recruiting participants, attaching the physiological sensors, administering the experiment using EVE, and assessing the collected data with EVE evaluation tools. Overall, this protocol will facilitate future research by streamlining the design and implementation of VR experiments with physiological sensors.

Virtual reality (VR) experiments can be difficult to implement and require meticulous planning. This protocol describes a method for the design and implementation of VR experiments that collect physiological data from human participants. The Experiments in Virtual Environments (EVE) framework is employed to accelerate this process.

用生理学方法进行虚拟现实实验

Virtual reality (VR) experiments are increasingly employed because of their internal and external validity compared to real-world observation and ...

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living

A decline in the performance of instrumental activities of daily living (IADLs) has been proposed as a prodromal marker of neurological disease. Existing clinical and performance-based IADL assessments are not feasible for integration into clinical medicine. Virtual reality (VR) is a powerful yet underutilized tool that could advance the diagnosis and treatment of neurological disease. An impediment to the adoption and scaling of VR in clinical neurology is VR-related sickness resulting from sensory inconsistencies between the visual and vestibular systems (i.e., locomotion problem).
The Cleveland Clinic Virtual Reality Shopping (CC-VRS) platform attempts to solve the locomotion problem by coupling an omnidirectional treadmill with high-resolution VR content, enabling the user to physically navigate a virtual grocery store to simulate shopping. The CC-VRS consists of Basic and Complex shopping experiences; both require walking 150 m and retrieving five items. The Complex experience has additional scenarios that increase the cognitive and motor demands of the task to better represent the continuum of activities associated with real-world shopping. The CC-VRS platform provides objective and quantitative biomechanical and cognitive outcomes related to the user&#39;s IADL performance. Initial data indicate that the CC-VRS results in minimal VR-sickness and is feasible and tolerable for older adults and patients with Parkinson&#39;s disease (PD). The considerations underlying the development, design, and hardware and software technology are reviewed, and initial models of integration into primary care and neurology are provided.

Virtual reality (VR) is a powerful yet underutilized approach to advance the diagnosis and treatment of neurological disease. The Cleveland Clinic Virtual Reality Shopping platform combines state-of-the-art VR content with an omnidirectional treadmill to quantify instrumental activities of daily living-a proposed prodromal marker of neurological disease.

沉浸式克利夫兰诊所虚拟现实购物平台，用于评估日常生活中的工具性活动

A decline in the performance of instrumental activities of daily living (IADLs) has been proposed as a prodromal marker of neurological disease. Existing ...

工程学

集成 VR 和生物传感器进行定量运动肌肉活动评估

Setup for the Quantitative Assessment of Motion and Muscle Activity During a Virtual Modified Box and Block Test

The ability to move allows us to interact with the world. When this ability is impaired, it can significantly reduce one's quality of life and independence and may lead to complications. The importance of remote patient evaluation and rehabilitation has recently grown due to limited access to in-person services. For example, the COVID-19 pandemic unexpectedly resulted in strict regulations, reducing access to non-emergent healthcare services. Additionally, remote care offers an opportunity to address healthcare disparities in rural, underserved, and low-income areas where access to services remains limited. 
Improving accessibility through remote care options would limit the number of hospital or specialist visits and render routine care more affordable. Finally, the use of readily available commercial consumer electronics for at-home care can enhance patient outcomes due to improved quantitative observation of symptoms, treatment efficacy, and therapy dosage. While remote care is a promising means to address these issues, there is a crucial need to quantitatively characterize motor impairment for such applications. The following protocol seeks to address this knowledge gap to enable clinicians and researchers to obtain high-resolution data on complex movement and underlying muscle activity. The ultimate goal is to develop a protocol for remote administration of functional clinical tests. 
Here, participants were instructed to perform a medically-inspired Box and Block task (BBT), which is frequently used to assess hand function. This task requires subjects to transport standardized cubes between two compartments separated by a barrier. We implemented a modified BBT in virtual reality to demonstrate the potential of developing remote assessment protocols. Muscle activation was captured for each subject using surface electromyography. This protocol allowed for the acquisition of high-quality data to better characterize movement impairment in a detailed and quantitative manner. Ultimately, these data have the potential to be used to develop protocols for virtual rehabilitation and remote patient monitoring.

作者聚焦:利用虚拟现实和肌电图增强远程康复

The protocol described here aims to enhance the quantitative evaluation of upper limb deficits, with the goal of developing additional technology for remote assessment both in the clinic and at home. Virtual reality and biosensor technologies are combined with standard clinical techniques to provide insights into the functioning of the neuromuscular system.

用于在虚拟改良的 Box and Block 测试期间定量评估运动和肌肉活动的设置

The ability to move allows us to interact with the world. When this ability is impaired, it can significantly reduce one's quality of life and ...

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.

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.

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

虚拟现实中运动捕捉系统的日常生活运动运动学测量

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此视频中的章节

每天生活技能的虚拟现实评估的发展

多功能设置研究人类电机控制用经颅磁刺激，肌电图，运动捕捉和虚拟现实

的运动再培训采用实时反馈的性能

用生理学方法进行虚拟现实实验

沉浸式克利夫兰诊所虚拟现实购物平台，用于评估日常生活中的工具性活动

用于在虚拟改良的 Box and Block 测试期间定量评估运动和肌肉活动的设置

用于在虚拟改良的 Box and Block 测试期间定量评估运动和肌肉活动的设置

用于在虚拟改良的 Box and Block 测试期间定量评估运动和肌肉活动的设置

视频运动分析使用智能手机（ViMAS）的初步研究

Biplanar Videoradiography to Study the Wrist and Distal Radioulnar Joints