Name: video movement analysis using smartphones (vimas): a pilot study
Uploaded: 2017-03-14T12:30:00.000+00:00
Duration: 7 min 51 s
Description: Watch this Scientific Journal Video about Video Movement Analysis Using Smartphones ViMAS: A Pilot Study at JoVE.com

The authors wish to thank all the participants who generously gave their time to participate in this study.

该协议是经韦恩州立大学的机构审查委员会。 1.实验准备<ol><li>位置摄像机捕捉整个6米的人行道。总共使用4三维运动的撷摄像头以捕捉行走超过6米的人行道。 <ol><li>放置每个摄像机在4个角的6米走道。定向每个摄像机在人行道的对角线端部彼此面对。 </li></ol></li><li>收集每个参与者的身高，体重和腿的长度的措施。 <ol><li>衡量质量公斤。 </li><li>测量从大转子腿长（米）到内侧踝双腿的用测量带。 </li><li>测量高度（米），由具有参与者站立赤脚旁边连接到墙上的卷尺。上放置参与者的头的顶部上的标尺读取贴在墙壁上的卷尺测量。 </li></ol></li><li&gt;上挂载与智能手机支架夹的三脚架适配器三脚架的智能手机。放置在2米或4-m的三脚架，垂直于人行道的中心。如果执行验证研究随机本次配股的顺序。 </li><li>对双侧髂前上嵴的参与者3智能标记（ASIS），大腿上1/3，小腿及足背的上1/3处集群。放置单个智能标记在左，右后上髂嵴之间中线。 <ol><li>安全与尼龙带/双面胶​​带智能标记。安全大腿和小腿的标记在冠状面。 </li></ol></li><li>地方贴指示在双边内侧和外侧股骨髁，内侧和外侧踝以及第一和第二脚趾之间的网络空间的三维运动捕获系统的校准骨性标志。 注:校准过程是具体到每一个实验室，以及3D动作捕捉设备和软件。 </LI&gt; <li>用于所述校准程序，是指由3D运动捕捉设备和/或软件用于分析数据的制造商提供的指导手册。这里所使用的过程涉及到使用仪器"魔杖"智能标记，注册置于该贴的3D位置。 </li></ol> 2.实验<ol><li>调整智能手机相机镜头的高度捕捉到可以仅参加者的下半身（ASIS为上缘），或全身上下（如肩峰上缘）。测量从地面以米相机镜头的高度。 </li><li>给学员练习的审判。有一个研究者操作智能电话，以及其他操作，控制三维运动捕获系统中的计算机。使用纸编号上注明智能电话录音试用号码。 </li><li>打开智能手机的应用程序。按下红色的"记录"按钮，在底部，在S中心creen，开始记录（在垂直方向时）。 注:如果智能手机已经放置在三角架上，按钮会出现在中间，在手机的右侧，靠近智能手机的home键。 </li><li>在指导他们的正常的步伐走路参与者，集中放置在对面墙上的标志，以协助他们走一条直线。并行智能电话放置在走道捕获参与者的侧轮廓。有参与者越过前两个三维运动捕获放置在两侧的人行道的开头相机，以及朝设置在两侧的人行道的另一端的另一两个3D运动捕获相机行走。 <ol><li>对于每一个试验中，给每个参与者一个倒计时开始（3，2，1，GO），并结束试（3，2，1，STOP）。 </li></ol></li><li>主体完成行走6米的距离后，选择红色的"记录"按钮一次完成录制此行走试验。 </li><li>检查LL标记每次试验后的位置。如果标记的位置就改变了，返回步骤1.6到三维运动捕捉系统重新校准到新的标志物的展示位置。 </li><li>有参加者在步骤2.1在每个摄像机的距离进行3项试验。 </li><li>移动与智能手机的三脚架与第二距离。返回参与者站到走不动路的中点。按照说明在协议步骤2.2 - 2.5。 </li><li>保存和删除智能标记之前验证3D动作捕捉录音和智能手机的录音。 </li></ol> 3.数据分析<ol><li>按照软件/制造商的说明来计算膝盖的角度。手动记录在步态周期的脚跟撞击和脚趾离阶段在屏幕上显示的膝角度。 </li><li>完成智能手机录音脚跟罢工和脚趾的分析了由一组两名调查员，谁必须在脚跟撞击和脚趾的时刻认同捕获的事件˚F事件和角度测量土地的痕迹。使用触控笔的膝盖角度测量标志性建筑的位置提高精度。下面的步骤是由两名调查员共同完成。 </li><li>要查看刚录制的审判，请在屏幕左下角的视频广场（在垂直方向）。 </li><li>使用滚动条在屏幕的底部，选择在其中受试者是最接近脚跟击或脚趾离（取的优选变量）在屏幕的中心框架上。 </li><li>要在角降，点击屏幕右上方的白色，勾画铅笔图标。 </li><li>选择角度选项，在下拉菜单中选择第二个选项。 </li><li>选择一个首选的颜色和角度壶。请注意，只有一个角度可以在同一时间进行测量。在这个协议中测得的角度纯粹由在矢状平面中的膝角度的。 </li><li>滑动或任何挖掘手写笔在屏幕的砸角度。 </li><li>放置在膝关节（外侧髁）角的中心，以沿着向上的股骨和向下向着侧踝到达矢量。 </li><li>如果需要的话，"放大"通过在屏幕的中心两个手指放置并拢慢慢地从彼此分开绘制它们。 注意:一旦满意的角度的位置，该工具自动计算在给定帧中的膝角度。 </li><li>为了确定在脚趾关闭或脚后跟着其他阶段的膝盖夹角，重复步骤3.4 - 3.10。 </li></ol> 4.临床协议<ol><li>测量和标记还是使用卷尺和标记/胶带6米的人行道。 </li><li>放置在一个平行的三脚架智能手机和接近6米的走道的中心。 </li><li>将三脚架从走道的中心有2米处捕捉到下肢，或4米处捕捉到躯干和下肢。所有的摄像机位置允许矢状面运动学LY捕获。 </li><li>计算使用下式地板智能手机相机透镜高度的高度: 附近的摄像头配置（2米）仅用于下肢捕获 相机镜头=身高（米0.87xPatient的参考腿长） - 0.12 远的摄像头配置（4米）为下肢和躯干捕获 相机镜头高度=患者的参考腿米定尺- 0.23 </li><li>重复第2 - 3记录，并使用智能手机应用分析数据。 </li></ol>

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No relevant financial relationship exists with Hudl Technique (Ubersense). Adams Physical Therapy Clinic, Novi, MI were our clinical collaborators on this study.

此验证研究的目的是要确定，以便可以自由使用智能电话应用的有效性被临床上用作使用智能技术在临床上运动的步态分析的目的和成本有效的手段。该研究了与智能电话应用运动学措施现有验证研究是有限的，并没有评估在矢状平面步态期间记录的动态运动的措施。该验证研究是第一次在矢状面上有研究运动膝盖措施智能手机。此外，此验证研究是第一个已知的开发利用在临床设置复制的智能手机应用在矢状平面运动学步态分析的协议。 根据结果​​，有由3D运动捕捉系统和智能电话应用，这是成为EXP膝角度测量之间的显著差异ected。有一种相比于三维运动捕获系统的容量由智能电话获得的测量精度的限制。智能手机的精度限制是基于进行研究时所提供的特定设备的工艺参数。随着技术的不断进步，智能手机平台，可出现改善的捕获速率。以移动范围内缓慢进行分析和捕捉静止图像视频的能力也可以提高运动的分析上提供智能手机平台的精度。的功能，如高清视频录制，慢动作拍摄，变焦和对焦能力素质结合可以大大提高下肢的分析，而不期望的步态时刻的失真。即使测量精度的影响得到的膝的措施，被两个相机距离之间检测到测量误差没有显著差异。有适度协议脚趾断相，说明膝关节屈曲措施相比脚跟罢工期间，该协议提高到膝关节伸直或过伸测量的协议中膝角度测量。 平均来说，一个2 - 被检测的测量误差的6度的范围内。在这项研究中获得的误差范围内，出现错误的一个既定的，临床上可接受的范围对应良好。例如，由单个审查员截取的末端的多个测角措施有一系列的4到5度。 27，28而且，由多个审查员采取末端测角措施的平均值±标准偏差为5至6度。时相比，研究，调查在矢状平面机械起重捕捉，测量误差也反射前面提到的临床上可接受的度量。在诺里斯等人进行的一项研究。 5，标准误差意思是起重机械在臀部，膝盖和脚踝的测定进行了分析。有膝关节测量6.1度的误差。然而，诺里斯获得的措施利用摄像机与智能手机兼容的应用程序记录计算机与分析的措施。的测量误差不能直接相比，在这项研究中获得的措施，因为所有的措施都捕获和在智能手机上进行分析。为了复制本手稿中的结果，如所描述的协议的所有步骤必须遵守。特别是第2节是执行此验证技术，并获得使用智能手机应用程序的有效运动测度的关键。这些步骤概括了智能手机的摄像头放置方法和执行运动的措施。 除了智能手机使用了动态联测的验证，本研究试图简化和标准化使用智能手机技术为在诊所矢状面的步态分析。研究人员开发了这项研究的现实再现协议的临床空间内，使用最少的和随时可用的设备。该协议包括了所需的空间，需要的设备，并在必要的公式来计算病人的具体设置所需的下肢部分的适当拍摄参数。通过下面列出的设置中，研究者都比较相信医生将获得有效的运动措施，±5度的误差。

人体步态评估是物理疗法的评价和临床决策过程的关键组成部分。 1步态评估是一个经常使用的临床工具，以评估患者的神经系统和肌肉骨骼缺陷步态障碍。然后步态重新评估可以提供有关干预的实现，他们在他们最初的评估确定的目标药效信息临床医生。有一个在美国国家认可的需要物理治疗师评估病人时，利用标准化的结果的测量。 2这就需要从保险报销政策的迅速变化的风景，以及一个强调移的物理治疗师更多地依赖基于证据的做法造成的。 3有许多成果的措施，以评估步态的不同方面，可以在许多方面，包括可观察:相UAL观察由临床医师，功能评估，视频记录的措施，电子人行道，三维运动分析软件等 。在临床环境中，经常执行观察（视觉）步态分析，因为它需要最少的设备和时间。 而观测步态分析在诊所内常用，它仍然是一种主观的评估。 4因此，因素，如治疗师的经验，视力，距离从受试者（摄像机的距离），测量工具，以及任何其他此类因素可以在评估引入变异性和误差。这种变异的可能性给出了测量的更可靠的方法，其可最终通过使用有效的仪器来克服的关键需求。 五 公司自成立以来，录像及相关技术已被用于检查各种功能限制资源从受损运动能力以及视觉反馈形式ulting。这是真正的剧烈的问候步态评估。 Stuberg 等。发现"录像设备处于临床常用的...并提供步态周期过程中的姿势和关节位置更多客观信息的医生。" 4随着技术的不断提高，因此具有视频分析的能力。这些功能提供物理治疗师一起评估临床步态的各种参数更大的能力。 即物理治疗师专注于两个关键参数包括运动学和时空参数。顾名思义，时空措施涉及的距离和时间的元素。具体到一个步态周期，时空措施将包括，但不限于，步幅长度，步长，节奏和速度。 6邻运动措施疗法手焦点在每个步态周期期间观察到的下肢的关节运动/旋转。 许多同行评议的文章已经发表引用该利用视频运动分析作为衡量的结果，特别是2D摄像系统，以评估运动，时空，或这两种类型的参数的组合。这些文章已评估各种临床人群包括具有中风（CVA）的历史的个人，创伤性脑损伤（TBI），脊髓损伤（SCI），帕金森氏病（PD），脑瘫（CP），和健康个体。下面介绍的原理图（ 图1）条规定获得通过，以确定已发布关于这一主题相关的同行评审文献的框架。 <img alt="图1" src="/files/ftp_upload/54659/54659fig1.jpg" /> 图1。 原理图文章的选择标准。钍Ë原理概述在选择同行评议的文章，以确定被报道步态分析的变量类型使用的步骤。 <a href="https://ecsource-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/54659/54659fig1large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> 大多数已经使用视频运动分析记录步态参数研究研究是验证研究。运动学验证研究可以进一步细分为三类:评估从特定的诊断/病理学所得，7检查在特定官能运动关节角度，8，9，并通过预干预动作的比较评估治疗的有效性异常运动和干预后的议案。 10，11同样，调查研究评估时空段米也可以细分为三类:从特定的病理学，12，13产生的异常运动的评定中，特定的功能活动期间的一个平台14检查，15，16和特定干预的效果确定。 17研究研究，评估既运动学和时空参数主要目的是确定如矫形器具体的治疗干预措施的有效性 17或体重/部分体重支持跑步机训练。 18，19这些文章的初步描述性分析确定研究的52.1％（仅那些在运动（30.4％看）和那些检查的参数的组合（21.7％）的总和）Researched运动学参数与2D摄像系统。这是相比于物品的69.5％（总和的文章，研究了时空参数（47.8％）和参数的组合（21.7％）），该评估时空参数。 在记录和评估运动学和时空步态参数的方法上的差异也被视为在临床实践中使用的观测步态分析的类型方面。时空参数与更大的频率评估的研究所示。有三种一般在原因的趋势一致:成本低，易于使用，并且一个标准协议的存在来测量这些参数。观测运动学测量已经显示出具有非常低的帧内评价者（60％）和评定者间可靠性 - 在临床环境（40％94％）。 4此广泛了解，在标记的位置是由于变化的骨性标志，并用来评估关节角度的特定工具。在标记的位置放置的微小差异可以显著改变产生的角度。时空测量有更高的可靠性（范围69％ - 97％），使用特别是当纸，铅笔和停止时钟的方法来评估步态。 20 在过去几十年的技术进步已经显著改变医疗被实践的方式。随着近年来智能手机的出现，接入互联网，在线调研文章和其他电子资源现在更容易获得医生在任何时间。 Martin 等人。报道称，"一般智能手机的使用在临床实践中，医学教育和研究正在增加。" 21在这项研究中，35岁以下的医生超过50％的受访者认为他们已经使用在临床实践中智能手机来实现。这种趋势增量缓解了2009年的时候在美国的医生有64％被发现使用智能手机在他们的临床实践。曼哈顿回顾研究进一步预测，这种增长将持续到2012年22爬升到医生和保健医生落实在临床实践中使用的智能手机的81％，而进一步的研究还没有进行，以确定是否该上升趋势确实继续攀升，这是合理的假设，在医疗保健的已知实现技术的，即在临床实践中使用的智能手机平台将变得更加普遍。 目前使用的在物理治疗实践智能手机应用尚未建立。目前还没有研究评估由物理治疗师迄今使用的智能视频分析应用。然而，各种智能手机应用程序已被用于单个物理治疗师在邻突破口辅助工具在这两个恢复使用和各学科训练的运动员utpatient整形设置。智能手机应用还提供可以测量的关节角度，其中一些已被证实。 23,24个别治疗师在智能手机上为病人的视觉反馈和可能缺乏的患者的步态循环的各种部件的容易击穿使用各种分析应用开始，基于轶事证据。但是，这些措施的有效性仍然不明。这并就这些智能视频分析应用中存在有限的研究主要集中在运动学步态参数验证，特别是踝关节，膝关节和髋关节的角度，在冠状面，25和设备的评定者间的可靠性。 26有迄今没有研究已经验证了使用智能手机的视频分析应用记录kinem在矢状面，这是在临床步态分析最常执行步态欧蒂龙。 本研究的目的是测试由智能手机应用记录运动学措施的同时效度并比较它们由3D运动捕捉系统在矢状面中记录的措施。我们预测，比起由3D运动捕捉系统记录的措施时，会出现由智能手机应用记录在测量之间没有显著差异。次要目的是从受试者（接近2米距离测试，如果两个不同的智能手机相机的刊登位置;在智能手机相机的两个不同的广告刊登位置之间措施远距离4 -nt差的研究的最终目的。是起草使用智能手机应用临床视频步态分析的协议。

<table><tbody><tr><td>Hudl Technique App</td><td>Hudl&amp;nbsp;</td><td>Online app</td><td>Freely downladable app from adroid /apple store</td></tr><tr><td>Optotrak Certus 3D motion capture system</td><td>Northern Digital inc</td><td>Optotrak certus System</td><td>http://www.ndigital.com/msci/products/optotrak-certus/</td></tr><tr><td>Smartphone</td><td>Apple</td><td>Iphone 5</td><td>www.apple.com</td></tr></tbody></table>

video movement analysis using smartphones (vimas): a pilot study

利用在临床实践中的智能手机正在稳步与可用于评估人的步态低成本/免费提供的"应用"的可用性日益增加。这个手稿的主要目的是测试在矢状面相比，由智能手机应用记录的三维运动捕捉系统运动学措施的同时效度。次要目标是开发用于智能手机上的摄像头的设置了视频移动分析医生的协议。 矢状面膝关节角罢工脚跟和脚趾的时候使用智能手机应用程序，并在32名健康受试者三维动作捕捉系统事件期间测量。三个试验在接近（2-m）的和远（4--m）的智能手机相机的距离进行。的距离的顺序是随机的。进行回归分析来估计根据任一主体的高度或腿长摄像机的高度。 绝对完美TE测量误差相比，脚跟击（5.81±5.26度）脚趾离（3.12±5.44度）期间最少。有显著（P &lt;0.05），但膝关节角度的应用和3D动作捕捉措施的适度协议。也有不显著（P&gt; 0.05）在两个照相机位置之间的绝对测量误差之间的差异。在步态周期的脚趾离和脚跟罢工事件5度 - 3之间平均的测量误差。 使用智能手机应用程序都可以在门诊执行步态或人体运动分析的有效工具。还需要进一步研究，以确定在测量上肢和躯干的运动精度。

所有32例完成6步行试验;然而，从与会者的6个数据并没有包括在数据分析中，由于产生的标记的可见性差的技术问题。膝关节角度的绝对测量误差时相比足跟撞击（5.81±5.26度）（ 表1b）脚趾离事件（3.12±5.44度）为最少。有智能手机应用和膝关节角度的三维运动捕捉措施间差异无统计学显著协议（P&gt; 0.05）。此外，还有两个摄像机位置（2.0M和4.0米）之间的绝对测量误差之间没有显著差异（P&gt; 0.05）。参加者的腿的长度占身高的40.4％的变异在其智能手机的摄像头被放置在较远的距离（4.0 M，P &lt;0.0001）和50％的变异在近距离（2.0 M，P &lt;0.0001）。 表1A）。在活动脚趾离1，脚跟触1和脚趾离2，为近及远的距离，智能手机应用表现出中等协议。在脚趾离2，观察改进协议，双方在近及远的距离，以提高观测值ICC（趾关近2 = ICC 0.447，P &lt;0.05;脚趾离2远东ICC = 0.454，​​P &lt;0.05）。 <table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody><tr><td>相机位置</td><td>步态阶段</td><td>平均误差</td><td>标准。偏差</td></tr><tr><td rowspan="4">近</td><td>足跟触1 </td><td> 5.74 </td><td> 8.49 </td></tr><tr><td>脚后跟冲击2 </td><td> 6.36 </td><td> 4.14 </td></tr> &lt;TR&gt; <td>脚趾离1 </td><td> 3.93 </td><td>五</td></tr><tr><td>脚趾离2 </td><td> 2.49 * </td><td> 4.99 </td></tr><tr><td rowspan="4">远</td><td>足跟触1 </td><td> 4.97 </td><td> 5.58 </td></tr><tr><td>脚后跟冲击2 </td><td> 5.47 </td><td> 3.6 </td></tr><tr><td>脚趾离1 </td><td> 2.71 </td><td> 5.64 </td></tr><tr><td>脚趾离2 </td><td> 2.54 * </td><td> 4.69 </td></tr></tbody></table> 表1:显着性值（P值）内相关分析。 *表示P &lt;0.05。 <table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody><tr><td>相机位置</td><td>步态阶段</td><td>内相关</td><td>意义</td></tr><tr><td rowspan="4">近</td><td>足跟触1 </td><td> 0.168 </td><td> 0.368 </td></tr><tr> &lt;TD&gt;脚趾离1 </td><td> 0.324 </td><td> 0.126 </td></tr><tr><td>脚后跟冲击2 </td><td> 0.335 </td><td> 0.07 </td></tr><tr><td>脚趾离2 </td><td> 0.447 </td><td> 0.018 * </td></tr><tr><td rowspan="4">远</td><td>足跟触1 </td><td> 0.157 </td><td> 0.327 </td></tr><tr><td>脚趾离1 </td><td> 0.284 </td><td> 0.084 </td></tr><tr><td>脚后跟冲击2 </td><td> 0.248 </td><td> 0.119 </td></tr><tr><td>脚趾离2 </td><td> 0.454 </td><td> .046 * </td></tr></tbody></table> 表2: 膝角度测量误差。从应用程序和动作捕捉系统之间措施的差异而产生奥特曼地块提供视觉证据表明，差异具有随机性，不让人看到任何错误成正比（ 图2A和2B）。该地块FOř2.0米和4.0 m以内的距离显示更散落在中心平均差线数据。这表明，摄像机的位置没有贡献在测量误差。 <img alt="图2" src="/files/ftp_upload/54659/54659fig2.jpg" /> 图2. 布兰德和奥特曼地块显示的应用程序和3D运动记录的数据之间的差异捕捉系统在脚趾离在远端和近端摄像机位置。一）关闭摄像机位置远脚趾2. 二）摄像头位置附近脚趾离2。

Watch this Scientific Journal Video about Video Movement Analysis Using Smartphones ViMAS: A Pilot Study at JoVE.com

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 ...

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

16.7K 次观看.  Wayne State University. 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: Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

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 ...

科研

行为学

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

As individuals increasingly live in cities, methods to study their everyday movements and the data that can be collected becomes important and valuable. Eye-tracking informatics are known to connect to a range of feelings, health conditions, mental states and actions. But because vision is the result of constant eye-movements, teasing out what is important from what is noise is complex and data intensive. Furthermore, a significant challenge is controlling for what people look at compared to what is presented to them.
The following presents a methodology for combining and analyzing eye-tracking on a video of a natural and complex scene with a machine learning technique for analyzing the content of the video. In the protocol we focus on analyzing data from filmed videos, how a video can be best used to record participants&#39; eye-tracking data, and importantly how the content of the video can be analyzed and combined with the eye-tracking data. We present a brief summary of the results and a discussion of the potential of the method for further studies in complex environments.

The objective of the protocol is to detail how to collect video data for use in the laboratory; how to record eye-tracking data of participants looking at the data and how to efficiently analyze the content of the videos that they were looking at using a machine learning technique.

将眼动追踪数据与从自由观看城市公园环境中的步行视频中获得的视频内容进行分析

As individuals increasingly live in cities, methods to study their everyday movements and the data that can be collected becomes important and valuable. ...

环境科学

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

Prehension, the act of reaching to grasp an object, is central to the human experience. We use it to feed ourselves, groom ourselves, and manipulate objects and tools in our environment. Such behaviors are impaired by many sensorimotor disorders, yet our current understanding of their neural control is far from complete. Current technologies for investigating human reach-to-grasp movements often utilize motion tracking systems that can be expensive, require the attachment of markers or sensors to the hands, impede natural movement and sensory feedback, and provide kinematic output that can be difficult to interpret. While generally effective for studying the stereotypical reach-to-grasp movements of healthy sighted adults, many of these technologies face additional limitations when attempting to study the unpredictable and idiosyncratic reach-to-grasp movements of young infants, unsighted adults, and patients with neurological disorders. Thus, we present a novel, inexpensive, and highly reliable yet flexible protocol for quantifying the temporal and kinematic structure of idiosyncratic reach-to-grasp movements in humans. High speed video cameras capture multiple views of the reach-to-grasp movement. Frame-by-frame video analysis is then used to document the timing and magnitude of pre-defined behavioral events such as movement start, collection, maximum height, peak aperture, first contact, and final grasp. The temporal structure of the movement is reconstructed by documenting the relative frame number of each event while the kinematic structure of the hand is quantified using the ruler or measure function in photo editing software to calibrate 2 dimensional linear distances between two body parts or between a body part and the target. Frame-by-frame video analysis can provide a quantitative and comprehensive description of idiosyncratic reach-to-grasp movements and will enable researchers to expand their area of investigation to include a greater range of naturalistic prehensile behaviors, guided by a wider variety of sensory modalities, in both healthy and clinical populations.

This protocol describes how to use frame-by-frame video analysis to quantify idiosyncratic reach-to-grasp movements in humans. A comparative analysis of reaching in sighted versus unsighted healthy adults is used to demonstrate the technique, but the method can also be applied to the study of developmental and clinical populations.

人体特异 Reach-to-Grasp 运动的帧-帧视频分析

Prehension, the act of reaching to grasp an object, is central to the human experience. We use it to feed ourselves, groom ourselves, and manipulate ...

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

The inability to complete instrumental activities of daily living (IADL) is a precursor to various neuropsychological diseases. Questionnaire-based assessments of IADL are easy to use but prone to subjective bias. Here, we describe a novel virtual reality (VR) test to assess two complex IADL tasks: handling financial transactions and using public transportation. While a participant performs the tasks in a VR setting, a motion capture system traces the position and orientation of the dominant hand and head in a three-dimensional Cartesian coordinate system. Kinematic raw data are collected and converted into &#39;kinematic performance measures,&#39; i.e., motion trajectory, moving distance, and time to completion. Motion trajectory is the path of a particular body part (e.g., dominant hand or head) in space. Moving distance refers to the total distance of the trajectory, and time to completion is how long it took to complete an IADL task. These kinematic measures could discriminate patients with cognitive impairment from healthy controls. The development of this kinematic measuring protocol allows detection of early IADL-related cognitive impairments.

We designed a virtual reality test to assess instrumental activities of daily living (IADL) with a motion capture system. We propose a detailed kinematic analysis to interpret the participant&#39;s various movements, including trajectory, moving distance, and time to completion to evaluate IADL capabilities.

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 the Switch Cost of Smartphone Use While Walking

This paper presents a study protocol to measure the task-switching cost of using a smartphone while walking. This method involves having participants walk on a treadmill under two experimental conditions: a control condition (i.e., simply walking) and a multitasking condition (i.e., texting while walking). During these conditions, the participants must switch between the tasks related to the experimental condition and a direction determining task. This direction task is done with a point-light walker figure, seemingly walking towards the left or the right of the participant. Performance on the direction task represents the participant&#8217;s task-switching costs. There were two performance measures: 1) correct identification of the direction and 2) response time. EEG data are recorded in order to measure the alpha oscillations and cognitive engagement occurring during the task switch. This method is limited in its ecological validity: pedestrian environments have many stimuli occurring simultaneously and competing for attention. Nonetheless, this method is appropriate for pinpointing task-switching costs. The EEG data allow the study of the underlying mechanisms in the brain that are related to differing task-switching costs. This design allows the comparison between task switching when doing one task at a time, as compared to task switching when multitasking, prior to the stimulus presentation. This allows understanding and pinpointing both the behavioral and neurophysiological impact of these two different task-switching conditions. Furthermore, by correlating the task-switching costs with the brain activity, we can learn more about what causes these behavioral effects. This protocol is an appropriate base for studying the switching cost of different smartphone uses. Different tasks, questionnaires, and other measures can be added to it in order to understand the different factors involved in the task-switching cost of smartphone use while walking.

This study design measures the task-switching cost of using a smartphone while walking. Participants undergo two experimental conditions: a control condition (walking) and a multitasking condition (texting while walking). Participants switch between these tasks and a direction determining task. EEG data as well as behavioral measures are recorded.

测量边走边使用智能手机的开关成本

This paper presents a study protocol to measure the task-switching cost of using a smartphone while walking. This method involves having participants walk ...

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency sensors, such as eye-trackers, electrodermal activity wristbands, EEG bands, and motion sensors provide observations at the millisecond level. This level of precision allows researchers to collect large datasets on social interactions. In this paper, I discuss how multiple eye-trackers can capture a fundamental construct in social interactions, joint visual attention (JVA). JVA has been studied by developmental psychologists to understand how children acquire language, learning scientists to understand how small groups of learners work together, and social scientists to understand interactions in small teams. This paper describes a methodology for capturing JVA in colocated settings using mobile eye-trackers. It presents some empirical results and discusses implications of capturing microobservations to understand social interactions.

Using multimodal sensors is a promising way to understand the role of social interactions in educational settings. This paper describes a methodology for capturing joint visual attention from colocated dyads using mobile eye-trackers.

使用移动眼动追踪器捕捉联合视觉注意力的方法

With the advent of new technological advances, it is possible to study social interactions at a microlevel with unprecedented accuracy. High frequency ...

An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

Keyboard input has played an essential role in human-computer interaction with a vast user base, and the keyboard design has always been one of the fundamental objects of studies on smart devices. With the development of screen technology, more precise data and indicators could be collected by smartphones to in-depth evaluate the keyboard design. The enlargement of the phone screen has led to unsatisfactory input experience and finger pain, especially for one-handed input. The input efficiency and comfort have attracted the attention of researchers and designers, and the curved keyboard with size-adjustable buttons, which roughly accorded with the physiological structure of thumbs, was proposed to optimize the one-handed usage on large-screen smartphones. However, its real effects remained ambiguous. Therefore, this protocol demonstrated a general and summarized method to evaluate the effect of curved QWERTY keyboard design on a 5-inch smartphone through a self-developed software with detailed variables, including objective behavioral data, subjective feedback, and the coordinate data of each touchpoint. There is sufficient existing literature on evaluating virtual keyboards; however, only a few of them systematically summarized and took reflection on the evaluation methods and processes. Therefore, this protocol fills in the gap and presents a process and method of the systematic evaluation of keyboard design with available codes for analysis and visualization.&#160;It needs no additional or expensive equipment&#160;and is easy to conduct and operate. In addition, the protocol also helps to get potential reasons for the disadvantages of the design and enlightens the optimization of designs. In conclusion, this protocol with the open-source resources could not only be an in-class demonstrative experiment to inspire the novice to start their studies but also contributes to improving the user experience and the revenue of input method editor companies.

The presented protocol integrates various evaluation methods and demonstrates a method to evaluate the keyboard design on smartphones. Pairs matched by English characters are proposed as the input material, and the transition time between two keys is used as the dependent variable.

一种评估智能手机键盘设计的评估方法和工具包

Keyboard input has played an essential role in human-computer interaction with a vast user base, and the keyboard design has always been one of the ...

用于 秀丽隐杆线 虫草坪回避测定的基于智能手机的成像

A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay

When exposed to toxic or pathogenic bacteria, the nematode Caenorhabditis elegans displays a learned lawn avoidance behavior, in which the worms gradually leave their food source and prefer to remain outside the bacterial lawn. The assay is an easy way to test the worms' ability to sense external or internal cues to properly respond to harmful conditions. Though a simple assay, counting is time consuming, particularly with multiple samples, and assay durations that span overnight are inconvenient for researchers. An imaging system that can image many plates over a long period is useful but costly. Here, we describe a smartphone-based imaging method to record lawn avoidance in C. elegans. The method requires only a smartphone and a light emitting diode (LED) light box, to serve as a transmitted light source. Using free time-lapse camera applications, each phone can image up to six plates, with sufficient sharpness and contrast to manually count worms outside the lawn. The resulting movies are processed into 10 s audio video interleave (AVI) files for every hourly time point, then cropped to show each single plate to make them more amenable for counting. This method is a cost-effective way for those looking to examine avoidance defects and can potentially be extended to other C. elegans assays.

作者聚焦:一种基于智能手机的秀丽隐杆线虫草坪回避测定成像方法  

This article describes a simple, low-cost method of recording the lawn avoidance behavior of Caenorhabditis elegans, using readily available items such as a smartphone and a light emitting diode (LED) light box. We also provide a Python script to process the video file into a format more amenable for counting.

一种基于智能手机的秀 丽隐杆线虫 草坪回避测定成像方法

When exposed to toxic or pathogenic bacteria, the nematode Caenorhabditis elegans displays a learned lawn avoidance behavior, in which the worms gradually ...

视频运动分析使用智能手机（ViMAS）的初步研究

摘要

探索更多视频

此视频中的章节

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

将眼动追踪数据与从自由观看城市公园环境中的步行视频中获得的视频内容进行分析

人体特异 Reach-to-Grasp 运动的帧-帧视频分析

虚拟现实中运动捕捉系统的日常生活运动运动学测量

测量边走边使用智能手机的开关成本

使用移动眼动追踪器捕捉联合视觉注意力的方法

一种评估智能手机键盘设计的评估方法和工具包

一种基于智能手机的秀丽隐杆线虫草坪回避测定成像方法

一种基于智能手机的秀丽隐杆线虫草坪回避测定成像方法

一种基于智能手机的秀丽隐杆线虫草坪回避测定成像方法

视频运动分析使用智能手机（ViMAS）的初步研究

摘要

探索更多视频

此视频中的章节

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

将眼动追踪数据与从自由观看城市公园环境中的步行视频中获得的视频内容进行分析

人体特异 Reach-to-Grasp 运动的帧-帧视频分析

虚拟现实中运动捕捉系统的日常生活运动运动学测量

测量边走边使用智能手机的开关成本

使用移动眼动追踪器捕捉联合视觉注意力的方法

一种评估智能手机键盘设计的评估方法和工具包

一种基于智能手机的秀 丽隐杆线虫 草坪回避测定成像方法

一种基于智能手机的秀 丽隐杆线虫 草坪回避测定成像方法

一种基于智能手机的秀 丽隐杆线虫 草坪回避测定成像方法

一种基于智能手机的秀丽隐杆线虫草坪回避测定成像方法

一种基于智能手机的秀丽隐杆线虫草坪回避测定成像方法

一种基于智能手机的秀丽隐杆线虫草坪回避测定成像方法