作者承认埃文拜斯海姆，妮可·卡佩拉，安德鲁·赫伯特 - 科普利技术和数据收集方面的援助。项目资金由加拿大自然科学和（NSERC）工程研究理事会和黑莓公司，包括在研究中使用的智能手机接收。

该协议被批准为渥太华健康科学网络研究伦理委员会。 1.准备<ol><li>为参加者提供了研究的轮廓，回答任何问题，并取得知情同意。记录参与者的特征 （如年龄，性别，身高，体重，腰围，从髂前上棘至内踝脚高度），识别码和日期上的数据表。确保用于捕捉视频第二智能手机设置成每秒捕获率至少30帧。 </li><li>牢固地安装一个手机皮套参与者的右前皮带或裤子的腰。启动，将用于收集传感器数据 （即，数据记录或WMMS应用）上的移动性测量智能手机和确保应用程序被适当地运行应用程序的智能手机。将智能手机在机座，与设备的背面（背面钙照相机）朝外。 </li><li>启动数字视频记录在第二的智能手机。匿名，记录对比视频没有显示这个人的脸，但确保记录所有活动的过渡。这款手机可手持。 </li></ol> 2.活动电路<ol><li>按照参与者和视频他们的行动，在第二的智能手机，而他们执行以下操作，由研究者所说: <ol><li>从站立姿势，摇动智能手机，表明审判的开始。 </li><li>继续静置至少10秒。这个站在阶段可用于电话取向校准14。 </li><li>步行到附近的椅子上坐下。 </li><li>站起来，走60米时电梯。 </li><li>站并等待电梯，然后走进电梯。 </li><li>乘电梯到二楼。 </li><li>转身走进了家居环境。 </li><li>走进浴室和SImulate刷牙。 </li><li>模拟梳理头发。 </li><li>模拟洗手。 </li><li>干手用毛巾擦干。 </li><li>步行到厨房。 </li><li>取菜从机架上，并将其放置在柜台上。 </li><li>装满水的水壶从厨房水槽。 </li><li>将炉子元件上的水壶。 </li><li>将面包烤面包机。 </li><li>走到饭厅。 </li><li>坐在一张餐桌。 </li><li>模拟吃过一顿饭在餐桌上。 </li><li>站立和行走，回到厨房水槽。 </li><li>用清水冲洗干净的菜，并将其放置在机架中。 </li><li>步行从厨房回到电梯。 </li><li>站并等待电梯，然后走进电梯。 </li><li>乘电梯到了一楼。 </li><li>走50米到楼梯间。 </li><li>打开门，进入楼梯间。 </li><li>步行上楼梯（13个步骤，围绕着陆，13步）。 </li><li>打开楼梯间的门到走廊。 </li><li>右转，走在大厅为15米。 </li><li>掉头就走15米回到楼梯间。 </li><li>打开门，进入楼梯间。 </li><li>走楼梯（13个步骤，围绕着陆，13步）。 </li><li>退出楼梯，走进一个房间。 </li><li>躺在床上。 </li><li>起床步行10米的斜坡。 </li><li>走了坡道，转身，然后顺着斜坡（20米）。 </li><li>继续走进大厅，打开大门之外。 </li><li>走在铺好的路有100米。 </li><li>转身走回了房间。 </li><li>走进房间，站在起点。 </li><li>继续站立，然后摇动智能手机，表明试验结束。 </li></ol></li></ol> 3.试验完成<ol><li>停止录像的智能手机，并要求参与者以删除并返回智能手机和皮套。停止对SMA数据记录或WMMS应用rtphone。从两款手机获得的运动数据文件和视频文件复制到电脑进行后期处理。 </li></ol> 4.后处理<ol><li>通过确定时间摇动作开始时同步视频和原始传感器数据之间的定时。此摇动移动对应于不同的加速度计信号和视频帧。检查是否同步误差通过从开始抖动时间减去端抖动时间，对于传感器和视频数据源。时间差应为两个数据集之间的相似。 </li><li>通过记录从开始震荡时间的时间差，以视频帧活动之间的过渡确定状态变化的实际，从黄金标准的视频倍。使用视频编辑软件，以获得时间内0.033秒（ 即每秒的视频速率30帧）。使用WMMS软件生成类似的变化州的来自传感器的数据。 </li><li>生成两个数据集，一是与真正的活动和所述第二与预测的活动，通过标记为每个视频帧中的活动（基于的状态时序的变化），然后计算预测活动在距WMMS输出每个视频帧的时间。对于WMMS绩效评估，计算出真阳性，假阴性，真阴性，假阳性的金标准活动之间WMMS预测活动。使用这些参数来计算敏感性，特异性和F-评分结果的措施。 注意:3个数据窗上被分析的窗口的任一侧的公差设定可以用于确定状态变化的结果，和2个数据窗进行分类的结果。例如，自1998年1第二数据窗户被在本研究中使用的WMMS，3秒前和当前窗口后测定以便将该容差范围内连续变化被忽略。代价是，这种情况发生在不到3秒的状态的变化可以忽略不计的总呼玛因为这些国家ň运行分析会被认为是暂时的。 </li></ol>

<ol><li>Lara, O. D., Labrador, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+Survey+on+Human+Activity+Recognition+using+Wearable+Sensors%5BTitle%5D)+AND+%22IEEE+Communications+Surveys+Tutorials%22%5BJournal%5D)">A Survey on Human Activity Recognition using Wearable Sensors.</a> IEEE Communications Surveys Tutorials. 15 (3), 1192-1209 (2013).</li>
<li>Foerster, F., Smeja, M., Fahrenberg, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Detection+of+posture+and+motion+by+accelerometry%3A+a+validation+study+in+ambulatory+monitoring%5BTitle%5D)+AND+%22Computers+in+Human+Behavior%22%5BJournal%5D)">Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring.</a> Computers in Human Behavior. 15 (5), 571-583 (1999).</li>
<li>Elsmore, T. F., Naitoh, P. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aElsmore%2C+TF+%22Monitoring+Activity+With+a+Wrist-Worn+Actigraph%3A+Effects+of+Amplifier+Passband+and+threshold+Variations%22">Monitoring Activity With a Wrist-Worn Actigraph: Effects of Amplifier Passband and threshold Variations</a>. , DTIC Online. (1993).</li>
<li>Kripke, D. F., Webster, J. B., Mullaney, D. J., Messin, S., Mason, W. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aKripke%2C+DF+%22Measuring+sleep+by+wrist+actigraph%22">Measuring sleep by wrist actigraph</a>. , DTIC Online. (1981).</li>
<li>Lockhart, J. W., Pulickal, T., Weiss, G. M. Applications of mobile activity recognition. Proceedings of the 2012 ACM Conference on Ubiquitous Computing. , 1054-1058 (2012).</li>
<li>Incel, O. D., Kose, M., Ersoy, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+Review+and+Taxonomy+of+Activity+Recognition+on+Mobile+Phones%5BTitle%5D)+AND+%22BioNanoScience%22%5BJournal%5D)">A Review and Taxonomy of Activity Recognition on Mobile Phones.</a> BioNanoScience. 3 (2), 145-171 (2013).</li>
<li>Yang, C. C., Hsu, Y. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+Review+of+Accelerometry-Based+Wearable+Motion+Detectors+for+Physical+Activity+Monitoring%5BTitle%5D)+AND+%22Sensors%22%5BJournal%5D)">A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring.</a> Sensors. 10 (8), 7772-7788 (2010).</li>
<li>He, Y., Li, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Physical+Activity+Recognition+Utilizing+the+Built-In+Kinematic+Sensors+of+a+Smartphone%5BTitle%5D)+AND+%22International+Journal+of+Distributed+Sensor+Networks%22%5BJournal%5D)">Physical Activity Recognition Utilizing the Built-In Kinematic Sensors of a Smartphone.</a> International Journal of Distributed Sensor Networks. 2013, (2013).</li>
<li>Vo, Q. V., Hoang, M. T., Choi, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Personalization+in+Mobile+Activity+Recognition+System+using+K-medoids+Clustering+Algorithm%5BTitle%5D)+AND+%22International+Journal+of+Distributed+Sensor+Networks%22%5BJournal%5D)">Personalization in Mobile Activity Recognition System using K-medoids Clustering Algorithm.</a> International Journal of Distributed Sensor Networks. 2013, (2013).</li>
<li>Dernbach, S., Das, B., Krishnan, N. C., Thomas, B. L., Cook, D. J. Simple and Complex Activity Recognition through Smart Phones. 8th International Conference on Intelligent Environments (IE), , 214-221 (2012).</li>
<li>Hache, G., Lemaire, E. D., Baddour, N. Mobility change-of-state detection using a smartphone-based approach. IEEE International Workshop on Medical Measurements and Applications Proceedings (MeMeA). , 43-46 (2010).</li>
<li>Wu, H. H., Lemaire, E. D., Baddour, N. Change-of-state determination to recognize mobility activities using a BlackBerry smartphone. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, , EMBC. 5252-5255 (2011).</li>
<li>Wu, H., Lemaire, E. D., Baddour, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Activity+Change-of-state+Identification+Using+a+Blackberry+Smartphone%5BTitle%5D)+AND+%22Journal+of+Medical+and+Biological+Engineering%22%5BJournal%5D)">Activity Change-of-state Identification Using a Blackberry Smartphone.</a> Journal of Medical and Biological Engineering. 32 (4), 265-271 (2012).</li>
<li>Tundo, M. D., Lemaire, E., Baddour, N. Correcting Smartphone orientation for accelerometer-based analysis. IEEE International Symposium on Medical Measurements and Applications Proceedings (MeMeA). , 58-62 (2013).</li>
<li>Tundo, M. D. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=%22Development+of+a+human+activity+recognition+system+using+inertial+measurement+unit+sensors+on+a+smartphone%22">Development of a human activity recognition system using inertial measurement unit sensors on a smartphone</a>. , Available from: 
 <a target="_blank" href="http://www.ruor.uottawa.ca/handle/10393/30963">http://www.ruor.uottawa.ca/handle/10393/30963</a> (2014).</li>
</ol>

The authors declare that they have no competing financial interests.

用可穿戴式移动监控系统，人类活动的认可已收到更多的关注，近年来，由于在可穿戴计算和智能手机和定量结果的措施，帮助临床决策和卫生干预措施评价系统需求的技术进步。本文介绍的方法是有效的评估WMMS发展至今，被发现活动分类错误不会一直存在，如果没有被列入评估范围广泛的日常活动和散步的情​​景。 该WMMS评价协议包括两个主要部分:数据采集与伴随的黄金标准数据集和数据后处理现实的，但控制的条件下。数字视频是测试整个协议活动WMMS算法预测时，提供黄金标准数据的可行的解决方案。在协议中的关键步骤是（i），以确保金标准视频捕获智能手机震动，因为这允许黄金标准视频与从参与者戴式电话和采集的数据的同步（ⅱ），以确保金标准的视频记录的所有转变执行经审讯参与者（ 即录制金标准的视频的人必须按照试验参与者的时候是在正确的位置）。 评估协议采用行走活动，每天的生活环境，以及各种地形和转换。所有操作完成连续而参与者佩戴的智能电话连续地从加速计，陀螺仪，磁力和GPS传感器记录的数据，和一个第二智能手机被用于视频由试验参与者执行的所有活动。该协议可以通过调整活动基于所述测试位置的顺序，只要各种日常生活芳连续执行的活动进行修改Ë中。十到十五分钟后，需要完成的电路中，根据不同的参与者。在试点测试中，一些与会者残疾人只能完成一个循环，因此审单的测试应该用一些人口考虑，以确保一个完整的数据集。 所提出的WMMS评价方法的局限是定时分辨率仅限于用于记录金标准比较视频和识别困难状态变化的截然不同的视频定时日常生活活动摄像头的视频帧速率。识别变化时，状态变化时，由多个帧导致由于活动的解释，可能是金标准和WMMS结果之间的差异开始，而不是WMMS错误。公差在每个变化的状态，在没有比较制成，可以实施，以有助于解释这些差异。 通常，增加的数量活动被分类和分类 ​​的难度（ 即楼梯，小的移动）降低了平均灵敏度，特异性和F1得分。此可以预期，因为增加的活动的数量增加的机会假阳性和假阴性。评估协议，只有使用有利于对算法的活动将产生的结果是误导性的，不太可能在现实条件下的评估，以产生类似的结果。因此，相对于现有方法的意义在于，该协议将导致对WMMS系统比在文献中以前的报告更保守的结果。然而，结果将更好地反映实践成果。 WMMS评价该方法可用于评估一系列的测量或协助人体运动可穿戴技术。

无处不在的感知已经成为一个引人入胜的研究领域，由于日益强大，体积小，成本低的计算和传感设备1。使用可穿戴式传感器移动度监视已经产生了极大的兴趣，因为消费级微电子能够检测高精度地1的运动特性。使用可穿戴式传感器人体行为识别（HAR）是最近的一个研究领域，在20世纪80年代进行的，90年代2的初步研究- 4。 现代智能手机包含必要的传感器和实时计算能力的移动性活动识别。在设备上的实时分析允许活动分类和数据上传，而无需用户或研究者干预。与流动性分析软件的智能手机可以提供健身追踪，健康监测，跌倒检测，家庭或工作的自动化，自我管理练习的研究E中5。智能手机也算是惯性测量平台，用于检测人类的移动活动和移动模式，采用板载传感器输出6计算数学产生的信号特征。共同特点生成方法包括启发式，时域，频域和小波分析为基础的方法7。 检测指定的活动1,5,6,7当现代智能手机HAR系统显示较高的预测精度。这些研究中评价方法以及精度发生变化，因为大多数的研究有自己的训练组，环境设置，和数据收集协议。灵敏度，特异性，准确性，召回，精度，和F-分数通常用于描述预测质量。然而，几乎没有任何信息可以在对检测实时的活动变化的能力"并发活动"的认可和评价方法1，对于试图进行分类的几项活动HAR系统。评估方法HAR系统的精度研究之间有很大差异。不管是分类算法和应用的特点，中金标准评估方法的描述是模糊的大多数HAR研究。 在每天的生活环境活动识别还没有被广泛研究。大多数智能电话为基础的活动识别系统以受控方式进行评估，导致评估协议可能是有利的算法，而不是现实的真实世界环境。在他们的评估方案，参加者往往只执行用于预测的行动，而不是将大范围的现实活动的参与者进行连续，模仿现实生活中的事件。 一些智能手机HAR研究8,9组类似的活动结合起来，如楼梯，步行，但是从数据集中排除其他活动。预测精度，然后由得怎么样算法确定的目标的活动来决定。德恩巴赫等 9名学员写他们即将搬家之前执行，中断状态变化的连续转换活动。 HAR系统的评估应该评估的算法，而参与者执行在日常生活环境的自然行为。这将使现实生活中的评价，认为复制日常使用的应用程序。一个现实的电路包括许多变化，对国家以及对系统不能预见的动作组合。那么一个研究者可以评估算法应对这些额外的动作，从而评估该算法的鲁棒性异常动作。 本文提出一种使用受控的课程，反映现实生活的日常生活环境中可穿戴式移动监控系统（WMMS）评估协议。 WMMS评价就可以在控制，但现实条件进行。在这个协议中，我们使用了在渥太华大学和渥太华医院研究所11-15开发出了第三代WMMS。该WMMS是专为智能手机与一个三轴加速度计和陀螺仪。在流动性算法占用户的变化，提供了误报的数量变化，状态识别的减少，并在活动分类提高灵敏度。最大限度地减少误报很重要，因为WMMS触发时被检测到的状态的活性变化短片拍摄，上下文敏感的活性评价进一步提高WMMS分类。不必要的录像创建存储和电池的使用效率低下。该WMMS算法是使用不同的预测的水平，其中增加的预测水平意味着增加量构造为低计算学习模式并评价可识别动作。

<table><tbody><tr><td>Smartphone or wearable measurement device</td><td>Blackberry</td><td>Z10</td><td></td></tr><tr><td>Smartphone for video recording</td><td>Blackberry</td><td>Z10 or 9800</td><td></td></tr><tr><td>Phone holster</td><td>Any</td><td></td><td></td></tr><tr><td>Data logger application for the smartphone</td><td>BlackBerry World - TOHRC Data Logger for BlackBerry 10</td><td></td><td>&lt;a href=&quot;http://appworld.blackberry.com/webstore/content/32013891/?countrycode=CA&quot;&gt;http://appworld.blackberry.com/webstore/content/32013891/?countrycode=CA&lt;/a&gt;</td></tr><tr><td>Wearable mobility measurement</td><td>Custom Blackberry 10 and Matlab software for mobility monitoring</td><td></td><td>&lt;a href=&quot;http://www.irrd.ca/cag/smartphone/&quot;&gt;http://www.irrd.ca/cag/smartphone/&lt;/a&gt;</td></tr><tr><td>Video editing or analysis software</td><td>Motion Analysis Tools</td><td></td><td>&lt;a href=&quot;http://www.irrd.ca/cag/mat/&quot;&gt;http://www.irrd.ca/cag/mat/&lt;/a&gt;</td></tr></tbody></table>

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.

研究方案与十五个身强力壮的参与者便利抽样的平均体重为68.9（±11.1）公斤进行，高度为173.9（±11.4）厘米，年龄为26（±9）岁，来自渥太华医院招募和渥太华的工作人员和学生的大学。智能电话以可变40-50 Hz的速率捕获传感器数据。采样率的变化是典型的智能手机传感器的采样。第二个智能手机使用了在1280×720（720P）分辨率录制数字视频。 枪套被固定在参与者的右前带或不带喘气的位置进一步规范。这表明一个自然的方法，将器件置于臀部的马倌。与设备放置在枪套和应用程序运行的数据记录器，每个人走过的电路一次，在一个自选定的速度。该电路并没有事先向参加者和诉讼活动的描述是在试验过程中所讲顺序调查。 该WMMS包括决策树与上，下边界条件，类似吴上班， 等人。13。修订后的分类使用了1秒的窗口大小和功能的线性加速度信号（范围，简单移动平均金额，标准偏差之和）和重力信号（差值为Y，方差总的平均差异）15。分别计算评估三组分类:（一）流动性或不动，（二）坐，立，卧，或步行，以及（iii）坐，立，卧，行走，爬楼梯，或小立运动。日常生活活动被打成小的动作。代表性的结果示于表1。 <table><tbody><tr><td colspan="5"> 分类 </td><td colspan="2"> TP </td><td colspan="2"> FN </td><td colspan="2">TN </td><td colspan="2"> FP </td><td colspan="3"> 灵敏度（％） </td><td colspan="3"> 特异性（％） </td><td colspan="3"> F1-分数（％） </td></tr><tr><td colspan="5">分类设置1 </td><td colspan="2"> 350 </td><td colspan="2"> 55 </td><td colspan="2"> 8701 </td><td colspan="2"> 91 </td><td colspan="3"> 86.30±7.2 </td><td colspan="3"> 98.96±0.6 </td><td colspan="3"> 86.17±6.3 </td></tr><tr><td colspan="5">分类设置2 </td><td colspan="2"> 359 </td><td colspan="2"> 47 </td><td colspan="2"> 8660 </td><td colspan="2"> 131 </td><td colspan="3"> 88.35±7.80 </td><td colspan="3"> 98.51±0.62 </td><td colspan="3"> 80.19±6.36 </td></tr><tr><td colspan="5">分类集3 </td><td colspan="2"> 423 </td><td colspan="2"> 75 </td><td colspan="2"> 8540 </td><td colspan="2"> 159 </td><td合并单元格="3"&gt; 84.92±6.38 </td><td colspan="3"> 98.17±0.62 </td><td colspan="3"> 78.42±5.96 </td></tr><tr><td colspan="5"> 分类设置1 </td><td colspan="2"></td><td colspan="2"></td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">不动到手机</td><td colspan="2"> 177 </td><td colspan="2"> 19 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">移动到不动</td><td colspan="2"> 171 </td><td colspan="2"> 36 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在移动</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 3990 </td><td colspan="2"> 73</td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在不动</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 4711 </td><td colspan="2"> 18 </td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5"> 分类设置2 </td><td colspan="2"></td><td colspan="2"></td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">站在步行</td><td colspan="2"> 134 </td><td colspan="2"> 17 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">步行至站</td><td colspan="2"> 137 </td><td colspan="2"> 26 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"&gt; </td><td colspan="3"></td></tr><tr><td colspan="5">步行到坐</td><td colspan="2"> 29 </td><td colspan="2"> 0 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">坐走</td><td colspan="2">三十</td><td colspan="2"> 0 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">步行到烈</td><td colspan="2"> 11 </td><td colspan="2"> 4 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">骗走</td><td colspan="2"> 15 </td><td colspan="2"> 0 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在支架</tD&gt; <td colspan="2"></td><td colspan="2"></td><td colspan="2"> 2872 </td><td colspan="2"> 73 </td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在坐</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 644 </td><td colspan="2"> 9 </td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在烈</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 447 </td><td colspan="2"> 9 </td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在步行</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 4697 </td><td colspan="2"> 40 </td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5"> 分类集3 </td><td colspan="2"></td><td colspan="2"></td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">站在步行</td><td colspan="2"> 70 </td><td colspan="2"> 7 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">步行至站</td><td colspan="2"> 74 </td><td colspan="2"> 14 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">步行到坐</td><td colspan="2"> 29 </td><td colspan="2"> 0 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">坐走</td><td colspan="2">三十</td><td colspan="2"> 0 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">步行到烈</td><td colspan="2"> 15 </td><td colspan="2"> 0 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">骗走</td><td colspan="2"> 15 </td><td colspan="2"> 0 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">步行到小动</td><td colspan="2"> 68 </td><td colspan="2"> 7 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">小朝行走</td><td colspan="2"> 61 </td><td colspan="2"> 13 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td合并单元格="5"&gt;走到楼梯</td><td colspan="2"> 13 </td><td colspan="2"> 2 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">楼梯走</td><td colspan="2"> 13 </td><td colspan="2"> 2 </td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">小动，以小动</td><td colspan="2"> 35 </td><td colspan="2">三十</td><td colspan="2"></td><td colspan="2"></td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在支架</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 1584 </td><td colspan="2"> 25 </td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在坐</td><td colspan="2"></td><td列跨度="2"&gt; </td><td colspan="2"> 643 </td><td colspan="2"> 10 </td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在烈</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 447 </td><td colspan="2"> 15 </td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在步行</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 4398 </td><td colspan="2"> 56 </td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在楼梯</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 246 </td><td colspan="2"> 0 </td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在刷牙</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 190 </td><td colspan="2"> 12 &lt;/ TD&gt; <td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在梳头</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 158 </td><td colspan="2"> 2 </td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在洗手</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 152 </td><td colspan="2"> 6 </td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在干手器</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 119 </td><td colspan="2"> 4 </td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在移动菜</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 93 </td><td colspan="2">五</td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在填充水壶</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 190 </td><td colspan="2">五</td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在吐司</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 70 </td><td colspan="2"> 1 </td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr><tr><td colspan="5">在洗碗</td><td colspan="2"></td><td colspan="2"></td><td colspan="2"> 250 </td><td colspan="2"> 18 </td><td colspan="3"></td><td colspan="3"></td><td colspan="3"></td></tr></tbody></table> 表1结果状态改变的决心;其中，真阳性（TP），假阴性（FN），真阴性（TN）假阳性（FP），总的变化 - 状态，敏感性，特异性，一次F1-得分。在指TN和FP的变化，国家的规定动作中。 从表1中，移动与不可移动分类组具有86.30％±7.2％的灵敏度的98.96％，特异性±0.6％，而第二预测组具有88.35％±7.80％的灵敏度的98.51％，特异性±0.62％ 。对于第三类组，灵敏度为84.92％±6.38％，特异性为98.17±0.62。 F1的分数为第一，第二和第三分类集是86.17±6.3，80.19±6.36，和78.42±5.96，分别。 <img alt="图1" src="/files/ftp_upload/53004/53004fig1.jpg" /> 图1-状态的变化的敏感性，特异性和F1-分数三个分类集。

Watch this Scientific Journal Video about Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment at JoVE.com

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

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 ...

evaluation-smartphone-based-human-activity-recognition-system-daily

Nanyang Technological University

Research

JoVE Journal

Behavior

8.9K 次观看.  University of Ottawa. 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.

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

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the daily life of older adults over extended periods of time. The technology is aggregated into a system that incorporates the principles of being minimally obtrusive, while generating secure, privacy protected, continuous objective data in real-world (home-based) settings for months to years. The system includes passive infrared presence sensors placed throughout the home, door contact sensors installed on exterior doors, connected physiological monitoring devices (such as scales), medication boxes, and wearable actigraphs. Driving sensors are also installed in participants' cars and computer (PC, tablet or smartphone) use is tracked. Data is annotated via frequent online self-report options that provide vital information with regard to the data that is difficult to infer via sensors such as internal states (e.g., pain, mood, loneliness), as well as data referent to activity pattern interpretation (e.g., visitors, rearranged furniture). Algorithms have been developed using the data obtained to identify functional domains key to health or disease activity monitoring, including mobility (e.g., room transitions, steps, gait speed), physiologic function (e.g., weight, body mass index, pulse), sleep behaviors (e.g., sleep time, trips to the bathroom at night), medication adherence (e.g., missed doses), social engagement (e.g., time spent out of home, time couples spend together), and cognitive function (e.g., time on computer, mouse movements, characteristics of online form completion, driving ability). Change detection of these functions provides a sensitive marker for the application in health surveillance of acute illnesses (e.g., viral epidemic) to the early detection of prodromal dementia syndromes. The system is particularly suitable for monitoring the efficacy of clinical interventions in natural history studies of geriatric syndromes and in clinical trials.

Unobtrusive sensors and pervasive computing technology incorporated into the daily home life of older adults enables meaningful health and activity changes to be recorded continuously for months to years, providing ecologically valid, high frequency, multi-domain data for research or clinical use.

建立社区范围的生命实验室以捕获不显眼和持续的远程活动和健康数据的方法

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the ...

科研

行为学

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 ...

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

The current protocol aims to showcase the technology integration, providing a detailed description of adopting the HealthCloud app, developed by the Healthy Landscape and Healthy People Lab, National Taiwan University (HLHP-NTU), on smartphones and smartwatches to collect data on users' real-time psychological and physiological responses and environmental information. A flexible and integrated research method was proposed because it can be difficult to measure multi-dimensional aspects of personal data in on-site studies in landscape and outdoor recreation research. An on-site study conducted in 2020 at the National Taiwan University campus was used as an application example. A dataset of 385 participants was used after excluding invalid samples. During the experiment, participants were asked to walk around campus for 30 min when their heart rate and psychological-scale items were measured, together with several environmental metrics. This work aimed to provide a possible solution to help on-site studies track real-time human responses that match ambient factors. Due to the app's flexibility, its use on wearable devices shows excellent potential for multidisciplinary research studies.

The present protocol introduces a non-commercial self-developed application for collecting real-time on-site data, including psychological scales, GPS location, heart rate, and blood-oxygen saturation level, as well as the application's operating procedures. An empirical study conducted in Taiwan in 2020 was used as an application example.

与可穿戴设备配对以监控个人健康状况的应用程序

The current protocol aims to showcase the technology integration, providing a detailed description of adopting the HealthCloud app, developed by the ...

环境科学

提高课堂参与度:基于 AI 的学生注意力水平分析

Artificial Intelligence-Based System for Detecting Attention Levels in Students

The attention level of students in a classroom can be improved through the use of Artificial Intelligence (AI) techniques. By automatically identifying the attention level, teachers can employ strategies to regain students' focus. This can be achieved through various sources of information.
One source is to analyze the emotions reflected on students' faces. AI can detect emotions, such as neutral, disgust, surprise, sadness, fear, happiness, and anger. Additionally, the direction of the students' gaze can also potentially indicate their level of attention. Another source is to observe the students' body posture. By using cameras and deep learning techniques, posture can be analyzed to determine the level of attention. For example, students who are slouching or resting their heads on their desks may have a lower level of attention. Smartwatches distributed to the students can provide biometric and other data, including heart rate and inertial measurements, which can also be used as indicators of attention. By combining these sources of information, an AI system can be trained to identify the level of attention in the classroom. However, integrating the different types of data poses a challenge that requires creating a labeled dataset. Expert input and existing studies are consulted for accurate labeling. In this paper, we propose the integration of such measurements and the creation of a dataset and a potential attention classifier. To provide feedback to the teacher, we explore various methods, such as smartwatches or direct computers. Once the teacher becomes aware of attention issues, they can adjust their teaching approach to re-engage and motivate the students. In summary, AI techniques can automatically identify the students' attention level by analyzing their emotions, gaze direction, body posture, and biometric data. This information can assist teachers in optimizing the teaching-learning process.

作者聚焦:解决测量课堂注意力的技术和主观挑战

This paper proposes an artificial intelligence-based system to automatically detect whether students are paying attention to the class or are distracted. This system is designed to help teachers maintain students' attention, optimize their lessons, and dynamically introduce modifications in order for them to be more engaging.

基于人工智能的系统，用于检测学生的注意力水平

The attention level of students in a classroom can be improved through the use of Artificial Intelligence (AI) techniques. By automatically identifying ...

工程学

Home-Based Monitor for Gait and Activity Analysis

Current outcomes in neuromuscular disorder clinical trials include motor function scales, timed tests, and strength measures performed by trained clinical evaluators. These measures are slightly subjective and are performed during a visit to a clinic or hospital and constitute therefore a point assessment. Point assessments can be influenced by daily patient condition or factors such as fatigue, motivation, and intercurrent illness. To enable home-based monitoring of gait and activity, a wearable magneto-inertial sensor (WMIS) has been developed. This device is a movement monitor composed of two very light watch-like sensors and a docking station. Each sensor contains a tri-axial accelerometer, gyroscope, magnetometer, and a barometer that record linear acceleration, angular velocity, the magnetic field of the movement in all directions, and barometric altitude, respectively. The sensors can be worn on the wrist, ankle, or wheelchair to record the subject&#8217;s movements during the day. The docking station enables data uploading and recharging of sensor batteries during the night. Data are analyzed using proprietary algorithms to compute parameters representative of the type and intensity of the performed movement. This WMIS can record a set of digital biomarkers, including cumulative variables, such as total number of meters walked, and descriptive gait variables, such as the percentage of the most rapid or longest stride that represents the top performance of patient over a predefined period of time.

This innovative device uses magneto-inertial sensors to permit gait and activity analysis in uncontrolled environments. Currently in the qualification process as an outcome measure in the European Medical Agency, one of the applications will be to serve as a clinical endpoint in clinical trials in neuromuscular diseases.

基于家庭的盖特和活动分析监视器

Current outcomes in neuromuscular disorder clinical trials include motor function scales, timed tests, and strength measures performed by trained clinical ...

生物工程

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 ...

医学

Design and Analysis for Fall Detection System Simplification

This paper presents a methodology based on multimodal sensors to configure a simple, comfortable and fast fall detection and human activity recognition system that can be easily implemented and adopted. The methodology is based on the configuration of specific types of sensors, machine-learning methods and procedures. The protocol is divided into four phases: (1) database creation (2) data analysis (3) system simplification and (4) evaluation. Using this methodology, we created a multimodal database for fall detection and human activity recognition, namely UP-Fall Detection. It comprises data samples from 17 subjects that perform 5 types of falls and 6 different simple activities, during 3 trials. All information was gathered using 5 wearable sensors (tri-axis accelerometer, gyroscope and light intensity), 1 electroencephalograph helmet, 6 infrared sensors as ambient sensors, and 2 cameras in lateral and front viewpoints. The proposed novel methodology adds some important stages to perform a deep analysis of the following design issues in order to simplify a fall detection system: a) select which sensors or combination of sensors are to be used in a simple fall detection system, b) determine the best placement of the sources of information, and c) select the most suitable machine learning classification method for fall and human activity detection and recognition. Even though some multimodal approaches reported in literature only focus on one or two of the above-mentioned issues, our methodology allows simultaneously solving these three design problems related to a human fall and activity detection and recognition system.

We present a methodology based on multimodal sensors to configure a simple, comfortable and fast fall detection and human activity recognition system. The goal is to build a system for accurate fall detection that can be easily implemented and adopted.

坠落检测系统简化的设计与分析

This paper presents a methodology based on multimodal sensors to configure a simple, comfortable and fast fall detection and human activity recognition ...

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

A key reason for referral to rehabilitation services after stroke and other neurological conditions is to improve one's ability to function in daily life. It has become important to measure a person's activities in daily life, and not just measure their capacity for activity in the structured environment of a clinic or laboratory. A wearable sensor that is now enabling measurement of daily movement is the accelerometer. Accelerometers are commercially-available devices resembling large wrist watches that can be worn throughout the day. Data from accelerometers can quantify how the limbs are engaged to perform activities in peoples' homes and communities. This report describes a methodology to collect accelerometry data and turn it into clinically-relevant information. First, data are collected by having the participant wear two accelerometers (one on each wrist) for 24 h or longer. The accelerometry data are then downloaded and processed to produce four different variables that describe key aspects of upper limb activity in daily life: hours of use, use ratio, magnitude ratio, and the bilateral magnitude. Density plots can be constructed that visually represent the data from the 24 h wearing period. The variables and their resultant density plots are highly consistent in neurologically-intact, community-dwelling adults. This striking consistency makes them a useful tool for determining if upper limb daily performance is different from normal. This methodology is appropriate for research studies investigating upper limb dysfunction and interventions designed to improve upper limb performance in daily life in people with stroke and other patient populations. Because of its relative simplicity, it may not be long before it is also incorporated in clinical neurorehabilitation practice.

This protocol describes a method to quantify upper limb performance in daily life using wrist-worn accelerometers.

一种使用加速度计量化上肢表现在日常生活的方法

A key reason for referral to rehabilitation services after stroke and other neurological conditions is to improve one's ability to function in daily life. ...

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

摘要

探索更多视频

建立社区范围的生命实验室以捕获不显眼和持续的远程活动和健康数据的方法

虚拟现实中运动捕捉系统的日常生活运动运动学测量

与可穿戴设备配对以监控个人健康状况的应用程序

基于人工智能的系统，用于检测学生的注意力水平

基于人工智能的系统，用于检测学生的注意力水平

基于人工智能的系统，用于检测学生的注意力水平

基于家庭的盖特和活动分析监视器

视频运动分析使用智能手机（ViMAS）的初步研究

坠落检测系统简化的设计与分析

一种使用加速度计量化上肢表现在日常生活的方法