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, 以便完全覆盖捕获卷, 固定摄像机以防止捕获过程中的移动, 确保至少有两个摄像头可以同时捕获一个对象, 并从虚拟环境中删除任何无关的反射或不必要的标记。在让参与者熟悉 VR (步骤 5) 的同时, 为他们提供足够的培训以适应虚拟体验是至关重要的。如果参与者遇到任何 VR 疾病症状 (例如, 不适, 头痛, 恶心, 呕吐, 脸色苍白, 出汗, 疲劳, 嗜睡, 迷失方向和冷漠), 实验应该停止。最后, 利用 R 统计软件对运动学原始数据进行了翻译。
我们的协议的一个限制和挑战是, 虚拟 IADL 任务应该通过与实际的 IADL 任务进行比较来验证。尽管以前的研究表明, 虚拟和实际任务在反应时间、准确度8、临床和功能测量11方面都高度相关, 但目前的运动学测量协议应该与许多常规神经心理学评估。在此验证的基础上, 我们需要用不同的 IADL 任务来扩展此协议。另一个限制是, 该协议只分析典型的运动学度量, 因此应包括在虚拟环境中更复杂的运动学性能度量, 如加速度、运动精度和效率。
目前运动学测量协议的意义在于它快速、安全、易于执行、无侵入性, 可用于检测早期 IADL 的缺陷。前一项使用此协议的研究证实, 运动学测量与神经心理学测试结果相结合, 最好地区分 MCI 患者从健康的控制13。对特定功能性缺陷的量化可以很好地为定位神经损伤的来源和程度提供依据, 因此有助于对个性化疗法的临床决策18。在这方面, 本文提出的议定书可用于循证临床决策。
考虑到未来的应用, 本协议可用于其他神经心理疾病, 如外伤性脑损伤19。另外, 分析当前协议中的特定子任务可能会很有趣, 以确定哪些类型更具挑战性。此外, 最近的 vr 研究训练中风患者显示了改进的记忆和注意功能后, 基于 VR 的游戏干预20。将这项议定书应用到额外的神经心理学康复环境中是很有意义的。

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 border="0" cellpadding="0" cellspacing="0" width="961">
	<tbody>
		<tr height="357">
			<td height="357" style="height:357px;width:216px;">Computer</td>
			<td style="width:161px;">N/A</td>
			<td style="width:127px;">N/A</td>
			<td style="width:457px;">Computer requirements:&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160; - Single socket H3 (LGA 1150) supports 
			- Intel&#174; Xeon&#174; E3-1200 v3, 4th gen. Core i7/i5/i3 processors 
			- Intel&#174; C226 Express PCH 
			- Up to 32GB DDR3 ECC/non-ECC 1600MHz UDIMM in 4 sockets 
			- Dual Gigabit Ethernet LAN ports 
			- 8x SATA3 (6Gbps) 
			- 2x PCI-E 3.0 x16, 3x PCI-E 2.0 x1, and 2x PCI 5V 32-bit slots 
			- 6x USB 3.0 (2 rear + 4 via headers) 
			- 10x USB 2.0 (4 rear + 6 via headers) 
			- HD Audio 7.1 channel connector by Realtek ALC1150 
			- 1x DOM power connector and 1x SPDIF Out Header 
			- 800W High Efficiency Power Supply 
			- Intel Xeon E3-1230v3 
			- DDR3 PC12800 8GB ECC 
			- WD 1TB BLUE WD 10EZEX&#160; 3.5&#34; 
			- NVIDIA QUADRO K5000 &#38; SYNC</td>
		</tr>
		<tr height="84">
			<td height="84" style="height:84px;width:216px;">Stereoscopic 3D Projector</td>
			<td style="width:161px;">Barco</td>
			<td style="width:127px;">F35 AS3D WUXGA&#160;</td>
			<td style="width:457px;">Resolution:&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160; - WQXGA (2,560 x 1,600)&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160; - Panorama (2,560 x 1,080)&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160; - WUXGA (1,920 x 1,200), 1080p (1,920 x 1,080)&#160;</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Stereoscopic Glasses</td>
			<td style="width:161px;">Volfoni&#160;</td>
			<td style="width:127px;">Edge 1.2</td>
			<td>For further information, visit http://volfoni.com/en/edge-1-2/</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Motion Capture Systems</td>
			<td style="width:161px;">NaturalPoint OptiTrack</td>
			<td style="width:127px;">17W</td>
			<td style="width:457px;">For further information, visit http://optitrack.com/products/prime-17w/</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">OptiTrack (Motion capture software)</td>
			<td style="width:161px;">NaturalPoint OptiTrack</td>
			<td style="width:127px;">Motive 2.0</td>
			<td style="width:457px;">For further information, visit https://optitrack.com/downloads/motive.html</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">MiddleVR (Middleware software)</td>
			<td style="width:161px;">MiddleVR</td>
			<td style="width:127px;">MiddleVR For Unity</td>
			<td style="width:457px;">For further information, visit http://www.middlevr.com/middlevr-for-unity/</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">VRDaemon (Middleware software)</td>
			<td style="width:161px;">MiddleVR</td>
			<td style="width:127px;">MiddleVR For Unity</td>
			<td style="width:457px;">For further information, visit http://www.middlevr.com/middlevr-for-unity/</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Unity3D (Game engine)</td>
			<td style="width:161px;">Unity Technologies</td>
			<td style="width:127px;">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 中取钱")。有关统计分析的 R 代码, 请参阅补充文件 3中附加的 "任务 1 R 代码 docx" 文件。
分析了 "任务 2: 乘公交车" 的 CSV 文件, 用 R 统计软件计算头部轨迹、移动距离和完成时间。头部运动的轨迹可视化 (图 7)。在执行任务2时, 计算头的移动距离是通过对序贯头位置之间的总距离求和。两个位置之间的距离是欧式距离。完成时间是指从八目标总线开始到整个任务结束所花的时间。有关统计分析的 R 代码, 请参阅补充文件 4中附加的 "任务 2 R 代码 docx" 文件。
使用 MCI 和健康控制的患者的人体测量特性和运动措施见表 1。这种 VR 测试与运动捕获系统为测量复杂 IADL 任务的运动学提供了新的机会。根据这里提出的协议, 研究人员可以获得 "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) 的运动学性能数据。
实际上, 通过以下几种统计分析 (即、多元方差分析、皮尔逊相关分析和正向逐步线性判别分析) 对此协议进行了案例控制研究, 可以在我们的实证研究13。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57284/57284fig1.jpg"> 图 1: 一个房间大小的沉浸式虚拟环境<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57284/57284fig1large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57284/57284fig2.jpg"> 图 2: 评估前的准备工作.(A) 该主题戴着立体眼镜。(B) 反射标记附着在主要的手和头上。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57284/57284fig2large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57284/57284fig3.jpg"> 图 3: 虚拟环境中的虚拟手表示形式.(A) 白色球体表示食指的位置。参与者单击虚拟数字 "2" 按钮。(B)参与者单击虚拟数字 "4" 按钮。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57284/57284fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57284/57284fig4.jpg"> 图 4: 任务 1: 从 ATM 中提取资金.(A) 参与者在 atm 中输入 PIN 码. (B) 参与者从自动取款机提款.<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57284/57284fig4large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/57284/57284fig5.jpg"> 图 5: 任务 2: 乘公共汽车.(A) 参与者在公共汽车站等候。(B) 参与者走出公共汽车站并进入目标总线。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57284/57284fig5large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/57284/57284fig6.jpg"> 图 6: 任务 1: 3D 笛卡尔空间中的手运动轨迹.(A) 健康的控件。(B) MCI 患者。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57284/57284fig6large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/57284/57284fig7.jpg"> 图 7: 任务 2: 3D 笛卡尔空间的头部运动轨迹.(A) 健康的控件。(B) MCI 患者。<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57284/57284fig7large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody>
<tr>
<td></td>
			<td>MCI 患者</td>
			<td>健康的控制</td>
		</tr>
<tr>
<td>编号 (男性)</td>
			<td>6 (3)</td>
			<td>6 (4)</td>
		</tr>
<tr>
<td>年龄 (年)</td>
			<td>72.4 @ 1。9</td>
			<td>72.6 @ 1。7</td>
		</tr>
<tr>
<td>任务 1: 提取资金</td>
			<td></td>
			<td></td>
		</tr>
<tr>
<td>移动距离 (m)</td>
			<td>34.7 @ 9。1</td>
			<td>52.5 @ 10。5</td>
		</tr>
<tr>
<td>完成时间 (分钟)</td>
			<td>1.8 @ 0。3</td>
			<td>1.3 @ 0。2</td>
		</tr>
<tr>
<td>任务 2: 乘公共汽车</td>
			<td></td>
			<td></td>
		</tr>
<tr>
<td>移动距离 (m)</td>
			<td>100.3 @ 11。4</td>
			<td>128.5 @ 14。2</td>
		</tr>
<tr>
<td>完成时间 (分钟)</td>
			<td>13.5 @ 0。2</td>
			<td>13.5 @ 0。2</td>
		</tr>
</tbody></table>表 1:人体测量学特征和运动学测量. 价值是指。
补充文件 1: 任务1提取资金. zip<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57284/Task_1_Withdraw_Money.zip">请单击此处下载此文件.</a>
补充文件 2: 任务2乘公共汽车. 邮编<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57284/Task_2_Take_a_Bus.zip">请单击此处下载此文件.</a>
补充文件 3: 任务 1 R 代码. docx.<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57284/Task_1_R_Code.docx">请单击此处下载此文件.</a>
补充文件 4: 任务 2 R 代码. docx.<a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57284/Task_2_R_Code.docx">请单击此处下载此文件.</a>

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

Nanyang Technological University

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

Two-photon Calcium Imaging in Mice Navigating a Virtual Reality Environment

In recent years, two-photon imaging has become an invaluable tool in neuroscience, as it allows for chronic measurement of the activity of genetically identified cells during behavior1-6. Here we describe methods to perform two-photon imaging in mouse cortex while the animal navigates a virtual reality environment. We focus on the aspects of the experimental procedures that are key to imaging in a behaving animal in a brightly lit virtual environment. The key problems that arise in this experimental setup that we here address are: minimizing brain motion related artifacts, minimizing light leak from the virtual reality projection system, and minimizing laser induced tissue damage. We also provide sample software to control the virtual reality environment and to do pupil tracking. With these procedures and resources it should be possible to convert a conventional two-photon microscope for use in behaving mice.

Here we describe the experimental procedures involved in two-photon imaging of mouse cortex during behavior in a virtual reality environment.

在小鼠导航虚拟现实环境的双光子钙成像

In recent years, two-photon imaging has become an invaluable tool in neuroscience, as it allows for chronic measurement of the activity of genetically ...

科研

行为学

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; ...

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 ...

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). 
To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated.
The mobile versus immobile classification set had a sensitivity of 86.30% &#177; 7.2% and specificity of 98.96% &#177; 0.6%, while the second prediction set had a sensitivity of 88.35% &#177; 7.80% and specificity of 98.51% &#177; 0.62%. For the third classification set, sensitivity was 84.92% &#177; 6.38% and specificity was 98.17 &#177; 0.62. F1 scores for the first, second and third classification sets were 86.17 &#177; 6.3, 80.19 &#177; 6.36, and 78.42 &#177; 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

在日常生活环境评价一个智能手机为基础的人类活动识别系统

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that ...

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults

Nordic walking (NW) has become a safe and simple form of exercise in recent years, and in studying this gait pattern, various data collection techniques have been employed, each with positives and negatives. The aim was to determine the effect of NW on older adult gait and posture and to determine optimal use of different data collection systems in both short and long duration analysis. Gait and posture during NW and normal walking were assessed in 17 healthy older adults (age: 69 &#177; 7.3). Participants performed two trials of 6 Minute Walk Tests (6MWT) (1 with poles (WP) and 1 without poles (NP)) and 6 trials of a 5m walk (3 WP and 3 NP). Motion was recorded using two systems, a 6-sensor accelerometry system and an 8-camera 3-dimensional motion capture system, in order to quantify spatial-temporal, kinematic, and kinetic parameters.
With both systems, participants demonstrated increased stride length and double support and decreased gait speed and cadence WP compared to NP (p &#60;0.05). Also, with motion capture, larger single support time was found WP (p &#60;0.05). With 3-D capture, smaller hip power generation and moments of force were found at heel contact and pre-swing as well as smaller knee power absorption at heel contact, pre-swing, and terminal swing WP compared to NP, when assessed over one cycle (p &#60;0.05). Also, WP yielded smaller moments of force at heel contact and terminal swing along with larger moments at mid-stance of a gait cycle (p &#60;0.05). No changes were found for posture.
NW seems appropriate for promoting a normal gait pattern in older adults. Three-dimensional motion capture should primarily be used during short duration gait analysis (i.e. single gait cycle), while accelerometry systems should be primarily employed in instances requiring longer duration analysis such as during the 6MWT.

The aim was to substantiate optimal use of data collection techniques for Nordic walking gait and posture analysis. Three-dimensional motion capture should be used during short duration analysis (i.e. single gait cycle), while accelerometry should be employed for longer duration analysis (i.e. repeated cycles) like a 6 Minute Walk Test.

充实适当的运动捕捉技术的越野行走步态中老年人的评估和姿势

Nordic walking (NW) has become a safe and simple form of exercise in recent years, and in studying this gait pattern, various data collection techniques ...

Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another

As far as acquiring motor skills is concerned, training by voluntary physical movement is superior to all other forms of training (e.g. training by observation or passive movement of trainee&#39;s hands by a robotic device). This obviously presents a major challenge in the rehabilitation of a paretic limb since voluntary control of physical movement is limited. Here, we describe a novel training scheme we have developed that has the potential to circumvent this major challenge. We exploited the voluntary control of one hand and provided real-time movement-based manipulated sensory feedback as if the other hand is moving. Visual manipulation through virtual reality (VR) was combined with a device that yokes left-hand fingers to passively follow right-hand voluntary finger movements. In healthy subjects, we demonstrate enhanced within-session performance gains of a limb in the absence of voluntary physical training. Results in healthy subjects suggest that training with the unique VR setup might also be beneficial for patients with upper limb hemiparesis by exploiting the voluntary control of their healthy hand to improve rehabilitation of their affected hand.

We describe a novel virtual-reality based setup which exploits voluntary control of one hand to improve motor-skill performance in the other (non-trained) hand. This is achieved by providing real-time movement-based sensory feedback as if the non-trained hand is moving. This new approach may be used to enhance rehabilitation of patients with unilateral hemiparesis.

利用虚拟现实技术将运动技能知识从一只手转移到另一个

As far as acquiring motor skills is concerned, training by voluntary physical movement is superior to all other forms of training (e.g. training by ...

Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation

Stroke rehabilitation requires repetitive, intensive, goal-oriented therapy. Virtual reality (VR) has the potential to satisfy these requirements. Game-based therapy can promote patients&#39; engagement in rehabilitation therapy as a more interesting and a motivating tool. Mobile devices such as smartphones and tablet PCs can provide personalized home-based therapy with interactive communication between patients and clinicians. In this study, a mobile VR upper extremity rehabilitation program using game applications was developed. The findings from the study show that the mobile game-based VR program effectively promotes upper extremity recovery in patients with stroke. In addition, patients completed two weeks of treatment using the program without adverse effects and were generally satisfied with the program. This mobile game-based VR upper extremity rehabilitation program can substitute for some parts of the conventional therapy that are delivered one-on-one by an occupational therapist. This time-efficient, easy to implement, and clinically effective program would be a good candidate tool for tele-rehabilitation for upper extremity recovery in patients with stroke. Patients and therapists can collaborate remotely through these e-health rehabilitation programs while reducing economic and social costs.

Here, we present a protocol to develop and apply a mobile game-based virtual reality program for the recovery of upper limb dysfunction in patients with stroke. The present study shows that the mobile program is feasible and effectively promotes upper limb recovery in stroke patients.

基于移动游戏的上肢卒中康复虚拟现实程序

Stroke rehabilitation requires repetitive, intensive, goal-oriented therapy. Virtual reality (VR) has the potential to satisfy these requirements. ...

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and spatial cognition. With a local area network and dedicated software platform, experimenters can efficiently monitor the behavior of the participants that are simultaneously immersed in a desktop virtual environment and digitalize the collected data. These capabilities allow for experimental designs in spatial cognition and navigation research that would be difficult (if not impossible) to conduct in the real world. Possible experimental variations include stress during an evacuation, cooperative and competitive search tasks, and other contextual factors that may influence emergent crowd behavior. However, such a laboratory requires maintenance and strict protocols for data collection in a controlled setting. While the external validity of laboratory studies with human participants is sometimes questioned, a number of recent papers suggest that the correspondence between real and virtual environments may be sufficient for studying social behavior in terms of trajectories, hesitations, and spatial decisions. In this article, we describe a method for conducting experiments on decision-making and navigation with up to 36 participants in a networked desktop virtual reality setup (i.e., the Decision Science Laboratory or DeSciL). This experiment protocol can be adapted and applied by other researchers in order to set up a networked desktop virtual reality laboratory.

This paper describes a method for conducting multi-user experiments on decision-making and navigation using a networked computer laboratory.

面向多参与者的决策科学与导航实验的网络化桌面虚拟现实设置

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and ...

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 ...

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Virtual reality (VR) has shown great educational potential because it makes it possible to simulate any desired situation or event, thus playing an important role in addressing current educational challenges. Despite the unlimited learning possibilities that VR may offer, unless users are willing to apply virtual devices to education, the investment of time, money, and effort will be fruitless. It is therefore crucial to assess the educational interest of the first generation of VR users and to identify their current needs. To this end, in this study we designed an online questionnaire and applied it through the SaaS (Software as a service) of a private server. The sample consisted of 117 early VR adopters recruited via a main portal of communication and information technologies in Spain. In order to engage participants, we posted a thread in the main forum, which is dedicated to the advances and potential uses of VR. Once the responses were gathered, we analyzed the relationship between 12 variables (mean contrasts with Snedecor&#39;s F, and contingency analysis with chi-square and Sommer&#39;s d). The results showed that the current profile of a VR user is a male over 35 years old, with university studies, and who has purchased his viewer recently (&#60;1 year). As for the learning and teaching applications that these users were interested in, only 13.7% of the participants in this study use VR for educational purposes, although 28.2% were interested, indicating that perhaps the lack of applications or learning experiences may be hampering the use of VR within education. Almost half of the early adopters surveyed would like to learn using VR technology and are somehow optimistic about the relationship between VR and education, particularly those who are younger.

This article describes the profile of Spanish early adopters of virtual reality and their interests and preferences regarding learning and educational applications for this technology. To this aim, we designed an online questionnaire and interviewed 117 users of the main virtual reality forum on the Internet.

早期采用者虚拟现实学习应用的在线探索研究

Virtual reality (VR) has shown great educational potential because it makes it possible to simulate any desired situation or event, thus playing an ...