这项工作得到了 Envisible 公司的支持。这项研究还得到了大韩民国卫生部 & 福利部韩国卫生技术 & 发展项目 (HI15C1027) 的资助。这项研究也得到韩国国家研究基金会 (NRF-2016R1A1A1A05005348) 的支持。

注意: 所有的程序, 包括使用人的主题是通过非侵入性的方式, 简单地戴着一副眼镜。所有的数据都是通过测量在玻璃中插入的力信号, 而不是与皮肤直接接触而获得的。数据被无线传输到数据记录模块, 在这种情况下, 这是一个指定的智能手机的研究。所有协议都与体内/体外人类研究无关。没有用药物和血液样本进行实验。所有实验对象都获得了知情同意。
1. 传感器集成电路模块的制造
<ol><li>
采购电子元器件制造电路模块。
	<ol>
<li>采购两个球型负载单元, 每一个在 0 n 和 15 n 的范围内运行, 并产生一个低差电压输出, 最大 120 mV 跨度在3.3 伏励磁。 注意: 这些负载单元用于测量眼镜左右两侧的力信号。</li>
		<li>购买两个仪表放大器和两个 15 kΩ增益设定电阻。 注: 仪器放大器和增益设定电阻用于放大负载单元的力信号八倍, 高达 960 mV。</li>
		<li>购买具有无线功能的微控制器单元 (MCU) (例如、wi-fi 连接) 和10位模数转换器 (ADC)。 注: 单片机用于读取力信号, 并以无线方式将其传输到数据采集模块。由于一个模拟输入销用于两个模拟力输入, 因此在下一步1.1.4 中引入了复用器。</li>
		<li>购买双通道模拟复用器, 处理两个输入信号与单片机上的一个 ADC pin。</li>
		<li>购买锂离子聚合物 (脂) 电池, 具有3.7 伏标称电压, 300 麻将标称容量, 1 C 放电速率。 注: 电池容量被选择提供足够的电流每小时超过200麻将, 并运行系统可靠的约1.5 小时的实验。</li>
		<li>购买3.3 伏稳压器, 用于线性向下调节 3.7 v 电池电压, 使系统的 3.3 v 工作电压。</li>
		<li>购买五 12 kΩ表面安装器件 (SMD) 型电阻器作为单片机的拉起电阻。电阻的足迹是2.0 毫米 x 1.2 毫米 (大小 2012)。</li>
	</ol>
</li>
	<li>
制造印制电路板 (pcb)。此步骤是关于绘制电路板, 并使图稿 (即、电路板布局、. 伯德文件) 以及 PCB 制作的示意图 (即)。开发需要对创建图稿和示意图文件的过程有一个基本的理解。
	<ol>
<li>使用电子设计应用程序绘制包含电池的左电路示意图, 如图 1A所示。将结果另存为图稿 (. 伯德) 和示意图 (.) 文件。</li>
		<li>使用电子设计应用程序绘制包含 MCU 的正确电路示意图, 如图 1B所示。将结果另存为图稿 (. 伯德) 和示意图 (.) 文件。</li>
		<li>通过向 PCB 制造公司订货, 制造电路板。</li>
		<li>按图 2和图 3中所示, 将步骤1.1 中准备的每个电子组件焊锡到 pcb。 注意: 仪表放大器对焊接温度非常敏感。在焊接过程中, 要确保十年代的铅温度不超过摄氏300摄氏度, 否则会对元件造成永久性损坏。</li>
	</ol>
</li>
</ol>2. 3D 打印眼镜架
<ol><li>使用3D 建模工具绘制眼镜头片的3D 模型, 如图 4A所示。将结果导出到. stl 文件格式。</li>
	<li>使用3D 建模工具, 如图 4B和图 4C中所示, 绘制眼镜的左、右太阳穴的3D 模型。将结果导出到. stl 文件格式。</li>
	<li>使用3D 打印机和碳纤维灯丝打印头件和寺庙零件, 喷嘴温度为240摄氏度, 床温80摄氏度。 注: 可允许使用任何商用3D 打印机和任何类型的丝, 如丙烯腈丁二烯苯乙烯 (ABS) 和聚乳酸 (PLA)。喷嘴和床温可根据灯丝和印刷条件进行调整。</li>
	<li>使用180°c 设置的热风鼓风机加热寺庙的尖端, 并将其弯曲到内约15度, 以接触颞肌肉的表皮, 如常规眼镜。 注: 玻璃寺庙的弯曲程度不需要严格, 因为曲率的目的是通过帮助眼镜适合主题的头部时, 以增加一个形状因素。但是, 要小心, 因为过度弯曲会防止寺庙接触颞肌肉, 这使得无法收集重要的模式。</li>
	<li>重复步骤2.1–2.4 中的步骤, 以打印两个不同大小的眼镜框架, 以适应多个头部大小, 如图 4所示。</li>
</ol>3. 玻璃的所有部分的装配
<ol><li>使用 M2 螺栓在眼镜寺庙两侧插入多氯联苯, 如图 5所示。</li>
	<li>将 M2 螺栓插入铰链接头, 将头件和寺庙装配起来。</li>
	<li>使用3针连接线连接左、右 pcb, 如图 5所示。</li>
	<li>将电池连接到左侧的电路上, 并将其与胶带粘附在左太阳穴上。电池的安装端不重要, 因为它可能因 PCB 设计而异。</li>
	<li>将眼镜盖在尖端和鼻垫上的橡皮胶带上, 以增加与人体皮肤的摩擦, 如图 5所示。</li>
</ol>4. 开发数据采集系统
注: 数据采集系统由数据传送模块和数据接收模块组成。数据传送模块读取双方的时间和力信号, 然后将它们发送到数据接收模块, 收集接收到的数据并将其写入. tsv 文件。
<ol><li>
按照步骤4.1 中的过程, 将数据传输应用程序上载到 PCB 模块的 MCU. 1–4.1. 3。
	<ol>
<li>使用计算机运行附加到辅助文件的 "GlasSense_Server" 项目。 注意: 此项目是用 Arduino 集成开发环境 (IDE) 构建的。它提供了读取时间和强迫信号与200样本/秒, 并将其传输到数据接收模块的能力。</li>
		<li>通过通用串行总线 (USB) 连接器将 PCB 模块连接到计算机。</li>
		<li>按 Arduino IDE 上的 "上载" 按钮, 将编程代码从步骤4.1.1 刷新到 MCU 中。</li>
	</ol>
</li>
	<li>
按照步骤4.2 中的过程, 将数据接收应用程序上载到智能手机 (用于以无线方式接收数据). 1–4.2. 3。
	<ol>
<li>使用计算机运行附加到辅助文件的 "GlasSense_Client" 项目。 注意: 这个项目是用 c# 编程语言构建的。它提供了接收数据和保存. tsv 文件的能力, 其中包含主题的信息, 如姓名、性别、年龄和体重指数 (BMI)。</li>
		<li>通过 USB 连接器将智能手机连接到计算机, 以生成数据接收应用程序。</li>
		<li>按 c# 项目上的 "文件 > 生成 & 运行" 按钮, 以生成用于智能手机的数据接收应用程序。</li>
	</ol>
</li>
</ol>5. 从用户研究中收集数据
注: 本研究收集了六项精选活动集: 久坐休息 (SR), 久坐咀嚼 (SC), 步行 (W), 散步时咀嚼 (CW), 久坐的谈话 (ST), 和久坐眨眼 (SW)。
<ol><li>选择对要测试的用户具有适当大小的眼镜。在铰链 (图 5) 上微调与支承螺栓的紧固。 注意: 力值不得超过 15 N, 因为本研究中使用的力传感器可能会失去操作范围以外的细微线性特性。通过松开或拧紧支承螺栓, 可以微调力值。</li>
	<li>
通过按步骤4.2.3 中构建的应用程序上的 "记录" 按钮记录所有主题的活动。
	<ol>
<li>在120秒块中记录一个活动, 并生成它的记录文件。<ol>
<li>在 SR 的情况下, 坐在椅子上的主题, 让他们使用智能手机或阅读一本书。允许头部运动, 但要避免全身运动。</li>
			<li>在 SC 和 CW 的情况下, 让受试者吃两种类型的食物质地 (烤面包和咀嚼果冻), 以反映不同的食物性质。在20毫米 x 20 毫米的切片上提供烤面包, 这是一个很好的吃的大小。</li>
			<li>在 W 的情况下, 让受试者在跑步机上以每小时4.5 公里/小时的速度行走。</li>
			<li>在 ST 的情况下, 坐下来的主题, 让他们读一本书大声以正常的语调和速度。</li>
			<li>在 SW 的情况下, 通知对象眨眼的时间钟的声音0.5 秒长每3秒。</li>
		</ol>
</li>
		<li>从步骤5.2.1 中收集的数据生成. tsv 格式的记录文件。 注意: 此文件包含接收数据的时间序列、左力信号、右力信号和代表当前面部活动的标签。图 6中描述了用户块中所有活动的时间信号的可视化。六个精选活动集 (SR、SC、W、CW、ST 和 SW) 分别被标记为1、2、3、4、5和6。这些标签用于比较协议8节中预测的类。</li>
		<li>在录音块后休息60秒。在休息时摘下眼镜, 在录音块重新启动时再次佩戴。</li>
		<li>对每个活动重复5.2.1 和5.2.2 四次的分块和中断步骤集。</li>
		<li>在 SW 的情况下, 在一个街区内, 让主体在左眼中反复眨眨眼, 然后在下一个街区内用右眼反复眨眼。</li>
	</ol>
</li>
	<li>重复步骤 5.1–5.2, 收集来自10个主题的数据。在本研究中, 我们使用了五男性和五位女性, 平均年龄是 27.9 @ 4.3 (标准偏差; s.d.) 年, 在19–33年不等, 并且平均 BMI 是 21.6 @ 3.2 (s.d.) 公斤/米2, 在17.9–27.4 公斤/m2不等。 注意: 在这项研究中, 没有任何医疗条件咀嚼食物, 眨眼和步行的对象被招募, 这一条件被用于纳入标准。</li>
</ol>6. 信号预处理与分割
注意: 左和右信号在以下过程中分别计算。
<ol><li>
准备一系列 2 s 长的时间框架。
	<ol>
<li>将120s 记录的信号分割成一组 2 s 帧, 方法是使用 MATLAB 以1秒的间隔跳过它们, 如图 6所示。 注: 2 s 长的分段框架用于提取7节中的特征。1 s 跳跃大小被确定划分信号由 3 s 眨眼间隔已经提到在步5.2.1。</li>
		<li>应用低通滤波器 (LPF), 使用 5th订单北海过滤器, 每个帧的截止频率为10赫兹。</li>
		<li>将步骤6.1.2 的结果保存为步骤7.1 中的下一个步骤的时间框架。</li>
	</ol>
</li>
	<li>
准备一系列的光谱框架。
	<ol>
<li>将每个帧的原始信号中的中点减去戴眼镜时的预紧。 注意: 以下频率分析不需要预加载值, 因为它不包括任何有关咀嚼、行走、眨眼、等的信息.但是, 它可以包含重要信息, 这可能不同于主题, 从眼镜的每一个设置, 甚至从一个人戴眼镜的那一刻起。</li>
		<li>将汉宁窗口应用于每个帧, 以减少频率分析中的频谱泄漏。</li>
		<li>通过对每个帧应用快速傅立叶变换 (FFT), 生成并保存单侧频谱。</li>
	</ol>
</li>
	<li>定义一个与框架块 (或简单框架) 同时的时间和光谱框架的组合。</li>
</ol>7. 特征向量的生成
注: 在协议6节中生成的每帧都产生一个特征向量。左和右帧分别计算, 并在以下过程中组合为特征向量。所有的程序都是在 MATLAB 中实现的。
<ol><li>从协议步骤6.1 的时间框架中提取统计特征。表 1中给出了54个功能的总数量的列表。</li>
	<li>从协议步骤6.2 的光谱帧中提取统计特征。表 2中给出了30个功能的总数量的列表。</li>
	<li>结合上面的时间和光谱特征生成一个84维特征向量。</li>
	<li>从协议步骤5.2 的录制中标记生成的特征向量。</li>
	<li>从步骤7.1–7.4 中重复执行所有框架块的步骤, 并生成一系列特征向量。</li>
</ol>8. 将活动分类为班级
注意: 这一步是选择支持向量机 (SVM)23的分类器模型, 方法是确定显示给定问题 (即特征向量) 的最佳精度的参数。SVM 是一种众所周知的受监督机器学习技术, 它在类和核函数之间的最大边距表现出良好的泛化和鲁棒性。利用网格搜索和交叉验证方法, 定义了径向基函数 (RBF) 核的惩罚参数 C 和核参数γ。对机器学习技术和支持向量机的基本理解需要执行以下步骤。推荐一些参考材料23、24、25 , 以便更好地了解机器学习技术和 SVM 算法。本节中的所有过程都是使用 LibSVM25软件包实现的。
<ol><li>为网格搜索定义一对 (C、γ) 的网格。使用 C 的指数增长序列 (2-10, 2-5,.., 230) 和γ (2-30, 2-25,.., 210)。 注意: 这些序列是启发式确定的。</li>
	<li>定义一对 (C、γ) (例如, (2-10, 2-30))。</li>
	<li>
对于步骤8.2 中定义的网格, 执行10倍交叉验证方案。 注意: 该方案将整个特征向量划分为10部分子集, 然后从其他子集训练的分类器模型中测试一个子集, 然后一个一个地重复它。因此, 每个特征向量都可以按顺序进行测试。<ol>
<li>将整个特征向量划分为10部分子集。</li>
		<li>从子集定义测试集, 以及从其余9子集中设置训练集。</li>
		<li>定义缩放向量, 将特征向量的所有元素缩放到训练集的 [0、1] 范围。 注意: 缩放向量与特征向量具有相同的维数。它由一组乘数组成, 它将所有特征向量的同一行 (或列) 缩放到 [0、1] 的范围。例如, 特征向量的第一个特征被线性地缩放为训练特征向量的所有第一个特征的 [0、1] 的范围。请注意, 缩放向量是从训练集定义的, 因为应该假定测试集是未知的。这一步通过使特征相等范围和避免数值误差在计算过程中提高了分类的准确性。</li>
		<li>使用步骤8.2.3 中获得的缩放向量, 将训练集的每个特征缩放到 [0、1] 的范围。</li>
		<li>使用步骤8.2.3 中获得的缩放向量, 将测试集的每个特性缩放到 [0、1] 的范围。</li>
		<li>在步骤8.2 中, 通过支持向量机 (C、γ) 对训练集进行培训, 然后构建一个分类器模型。</li>
		<li>通过在步骤8.2 中定义对 (C、γ) 的 SVM 测试测试集, 以及从训练过程中获得的分类器模型。</li>
		<li>计算测试集的分类精度。从正确分类的特征向量的百分比计算出其精度。</li>
		<li>对所有子集重复步骤 8.2. 2–8.2 8, 并计算所有子集的平均精度。</li>
	</ol>
</li>
	<li>对 (C、γ) 的所有栅格点重复步骤 8.2–8.3 9。</li>
	<li>查找网格最高精度的本地最大值。8节的所有过程都在图 7中进行了说明。</li>
	<li>选)如果网格的步骤被认为是粗糙的, 请在步骤8.5 中找到的本地最大值附近的较细网格中重复步骤 8.1–8.5, 并查找细网格的新本地最大值。</li>
	<li>从以下等式计算每类活动的精度、回调和 F1评分: <img alt="Equation 1" src="/files/ftp_upload/56633/56633eq1.jpg">                                   等式1 <img alt="Equation 2" src="/files/ftp_upload/56633/56633eq2.jpg">                                             等式2 <img alt="Equation 3" src="/files/ftp_upload/56633/56633eq3.jpg">          等式3 其中 TP、FP 和 FN 分别表示每个活动的真实阳性、误报和假阴性。所有活动的混淆矩阵都在表 3中给出。</li>
</ol>

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D., Sazonov, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+sensors+to+measure+activity+in+people+with+stroke.">Using sensors to measure activity in people with stroke.</a> Top Stroke Rehabil. 18 (6), 746-757 (2011).</li><li>Makeyev, O., Lopez-Meyer, P., Schuckers, S., Besio, W., Sazonov, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+food+intake+detection+based+on+swallowing+sounds.">Automatic food intake detection based on swallowing sounds.</a> Biomed Signal Process Control. 7 (6), 649-656 (2012).</li><li>P&#228;&#223;ler, S., Fischer, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Food+intake+activity+detection+using+an+artificial+neural+network.">Food intake activity detection using an artificial neural network.</a> Biomed Tech (Berl). , (2012).</li><li>Passler, S., Fischer, W. -. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Food+intake+monitoring:+Automated+chew+event+detection+in+chewing+sounds.">Food intake monitoring: Automated chew event detection in chewing sounds.</a> IEEE J Biomed Health Inform. 18 (1), 278-289 (2014).</li><li>Kadomura, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> CHI'13 Extended Abstracts on Human Factors in Computing Systems. , 1551-1556 (2013).</li><li>Fontana, J. M., Farooq, M., Sazonov, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+ingestion+monitor:+A+novel+wearable+device+for+monitoring+of+ingestive+behavior.">Automatic ingestion monitor: A novel wearable device for monitoring of ingestive behavior.</a> IEEE Trans Biomed Eng. 61 (6), 1772-1779 (2014).</li><li>Shen, Y., Salley, J., Muth, E., Hoover, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+the+Accuracy+of+a+Wrist+Motion+Tracking+Method+for+Counting+Bites+across+Demographic+and+Food+Variables.">Assessing the Accuracy of a Wrist Motion Tracking Method for Counting Bites across Demographic and Food Variables.</a> IEEE J Biomed Health Inform. , (2016).</li><li>Farooq, M., Sazonov, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> International Conference on Bioinformatics and Biomedical Engineering. , 464-472 (2017).</li><li>Grigoriadis, A., Johansson, R. S., Trulsson, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Temporal+profile+and+amplitude+of+human+masseter+muscle+activity+is+adapted+to+food+properties+during+individual+chewing+cycles.">Temporal profile and amplitude of human masseter muscle activity is adapted to food properties during individual chewing cycles.</a> J Oral Rehab. 41 (5), 367-373 (2014).</li><li>Strini, P. J. S. A., Strini, P. J. S. A., de Souza Barbosa, T., Gavi&#227;o, M. B. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessment+of+thickness+and+function+of+masticatory+and+cervical+muscles+in+adults+with+and+without+temporomandibular+disorders.">Assessment of thickness and function of masticatory and cervical muscles in adults with and without temporomandibular disorders.</a> Arch Oral Biol. 58 (9), 1100-1108 (2013).</li><li>Standring, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Gray's anatomy: the anatomical basis of clinical practice. , (2015).</li><li>Farooq, M., Sazonov, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+wearable+device+for+food+intake+and+physical+activity+recognition.">A novel wearable device for food intake and physical activity recognition.</a> Sensors. 16 (7), 1067 (2016).</li><li>Zhang, R., Amft, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Proceedings of the 2016 ACM International Symposium on Wearable Computers. , 50-52 (2016).</li><li>Farooq, M., Sazonov, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Segmentation+and+Characterization+of+Chewing+Bouts+by+Monitoring+Temporalis+Muscle+Using+Smart+Glasses+with+Piezoelectric+Sensor.">Segmentation and Characterization of Chewing Bouts by Monitoring Temporalis Muscle Using Smart Glasses with Piezoelectric Sensor.</a> IEEE J Biomed Health Inform. , (2016).</li><li>Huang, Q., Wang, W., Zhang, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Your+Glasses+Know+Your+Diet:+Dietary+Monitoring+using+Electromyography+Sensors.">Your Glasses Know Your Diet: Dietary Monitoring using Electromyography Sensors.</a> IEEE Internet of Things Journal. , (2017).</li><li>Chung, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+glasses-type+wearable+device+for+monitoring+the+patterns+of+food+intake+and+facial+activity.">A glasses-type wearable device for monitoring the patterns of food intake and facial activity.</a> Scientific Reports. 7, 41690 (2017).</li><li>Cristianini, N., Shawe-Taylor, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> An introduction to support Vector Machines: and other kernel-based learning methods. , (2000).</li><li>Giannakopoulos, T., Pikrakis, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Introduction to Audio Analysis: A MATLAB Approach. , (2014).</li><li>Chang, C. -. 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作者没有什么可透露的。

在这项研究中, 我们首先提出了眼镜的设计和制造过程, 它能感觉到食物的摄取和运动的规律。由于本研究主要集中在数据分析上, 将食物摄取量与其他物理活动 (如步行和眨眼) 区分开来, 传感器和数据采集系统需要执行移动记录。因此, 该系统包括传感器、具有无线通信能力的 MCU 和电池。该议定书提供了一种新颖和实用的方法来衡量颞肌肉活动的模式, 由于食物摄取和眨眼的非接触方式: 工具和?...

持续和客观地监测食物摄入量对于维持人体的能量平衡至关重要, 因为过量的能量积聚可能导致 overweightness 和肥胖症1, 这可能导致各种医疗并发症2。能源失衡的主要因素是食物摄入过量和体力活动不足3。通过可穿戴设备4、5、6, 对日常能源支出监测进行了各种研究, 包括在最终消费者级别和医疗阶段7。然而, 对食品摄入量监测的研究仍处于实验室设置之中, 因为很难直接和客观地检测食物摄取活动。在这里, 我们的目的是提供一个设备设计和评估, 以监测食品摄入量和体育活动模式在实际水平的日常生活中。
通过咀嚼和吞咽声音来监测食物摄取的各种间接方法8,9,10, 手腕11,12,13, 图像分析14和肌电图 (肌电信号)15。然而, 这些方法很难适用于日常生活应用, 因为它们固有的局限性: 使用声音的方法容易受到环境声音的影响;使用手腕运动的方法在不消耗食物的情况下很难与其他体力活动区分开来;使用图像和肌电信号的方法受到运动和环境边界的限制。这些研究表明, 使用传感器自动检测食物摄取量的能力, 但仍然有限制的实际适用性的日常生活以外的实验室设置。
在本研究中, 我们利用颞肌肉活动的模式作为对食物摄取量的自动和客观的监测。一般来说, 颞肌肉重复收缩和松弛作为咀嚼肌肉的一部分, 在食物摄取量16,17;因此, 可以通过检测颞肌肉活动的周期性模式来监测食物摄取活动。最近, 有几项研究利用了颞肌肉活动18,19,20,21, 使用了肌电信号或压电应变传感器, 并直接附加到人类皮肤.然而, 这些方法对肌电信号电极或应变传感器的皮肤位置很敏感, 并且由于身体运动或出汗而容易脱离皮肤。因此, 我们提出了一个新的和有效的方法, 使用一对眼镜, 通过两个负载细胞, 通过插入在我们以前的研究中的铰链中的颞肌肉活动22。该方法在不接触皮肤的情况下, 具有很高的准确度检测食物摄取活动的潜力。因为我们使用的是一种普通的眼镜式设备, 所以它也不太显眼, 也不侵入。
在本研究中, 我们提出了如何实现眼镜型设备的一系列详细协议, 以及如何使用颞肌肉活动模式来监测食物摄取量和体力活动。这些协议包括硬件设计和制造过程, 由3维打印的眼镜框架、一个电路模块和一个数据采集模块组成, 包括用于数据处理和分析的软件算法。此外, 我们还检查了几个特色活动 (例如, 咀嚼, 散步和眨眼) 的分类, 以证明作为一个实际系统的潜力, 可以区分食物摄入量和其他体力活动之间的细微差别。模式.

<table><tbody><tr><td>FSS1500NSB</td><td>Honeywell, USA</td><td></td><td>Load cell</td></tr><tr><td>INA125U</td><td>Texas Instruments, USA</td><td></td><td>Amplifier</td></tr><tr><td>ESP-07</td><td>Shenzhen Anxinke Technology, China</td><td></td><td>MCU with Wi-Fi module</td></tr><tr><td>74LVC1G3157</td><td>Nexperia, The Netherlands</td><td></td><td>Multiplexer</td></tr><tr><td>MP701435P</td><td>Maxpower, China</td><td></td><td>LiPo battery</td></tr><tr><td>U1V10F3</td><td>Pololu, USA</td><td></td><td>Voltage regulator</td></tr><tr><td>Ultimaker 2</td><td>Ultimaker, The Netherlands</td><td></td><td>3D printer</td></tr><tr><td>ColorFabb XT-CF20</td><td>ColorFabb, The Netherlands</td><td></td><td>Carbon fiber filament</td></tr><tr><td>iPhone 6s Plus</td><td>Apple, USA</td><td></td><td>Data acquisition device</td></tr><tr><td>Jelly Belly</td><td>Jelly Belly Candy Company, USA</td><td></td><td>Food texture for user study</td></tr></tbody></table>

design and evaluation of smart glasses for food intake and physical activity classification

本研究提出了设计和制造一种眼镜式可穿戴装置的一系列协议, 用于检测在进食和其他体力活动期间颞肌肉活动的模式。我们制作了一个 3 d 印刷框架的眼镜和一个负载单元集成印制电路板 (PCB) 模块插入两个铰链的框架。该模块用于获取力信号, 并进行无线传输。这些程序为系统提供了更高的机动性, 可以评估在实际穿戴条件如步行和晃。分类的表现也通过区别食物摄取的方式从那些体育活动被评估。一系列的算法被用来对信号进行预处理, 产生特征向量, 并识别几种功能性活动 (咀嚼和眨眼) 的模式, 以及其他体育活动 (静坐休息、说话和走路)。结果表明, 各特征活动的分类平均为1评分为91.4%。我们认为, 这种方法可能有助于自动和客观地监测摄食行为, 以更高的准确度作为治疗摄食问题的实用手段。

通过该协议中概述的过程, 我们通过区分头片长度、lH (133 和138毫米) 和寺庙、LT (110 和125毫米) 来编写3D 打印帧的两个版本, 如图 4所示。因此, 我们可以覆盖几个磨损条件, 这可以从不同的主题的头部大小, 形状,等.受试者选择了其中一个框架, 以适合他们的头部供用户学习。在铰链接头和支承螺栓孔之间的垂直距离 (Lh) ?...

Watch this Scientific Journal Video about Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification at JoVE.com

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

食品摄取及运动分级智能眼镜的设计与评价

本研究提出了一种设计和制造一种眼镜式可穿戴装置的协议, 它通过在眼镜的两个铰链中插入负载细胞来检测食物摄取的模式和其他特征的物理活动。

This study presents a series of protocols of designing and manufacturing a glasses-type wearable device that detects the patterns of temporalis muscle ...

design-evaluation-smart-glasses-for-food-intake-physical-activity

Research

JoVE Journal

Engineering

11.1K 次观看.  Seoul National University. 本研究提出了一种设计和制造一种眼镜式可穿戴装置的协议, 它通过在眼镜的两个铰链中插入负载细胞来检测食物摄取的模式和其他特征的物理活动。

视频: Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

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

Control of Eating Behavior Using a Novel Feedback System

Subjects eat food from a plate that sits on a scale connected to a computer that records the weight loss of the plate during the meal and makes up a curve of food intake, meal duration and rate of eating modeled by a quadratic equation. The purpose of the method is to change eating behavior by providing visual feedback on the computer screen that the subject can adapt to because her/his own rate of eating appears on the screen during the meal. The data generated by the method is automatically analyzed and fitted to the quadratic equation using a custom made algorithm. The method has the advantage of recording eating behavior objectively and offers the possibility of changing eating behavior both in experiments and in clinical practice. A limitation may be that experimental subjects are affected by the method. The same limitation may be an advantage in clinical practice, as eating behavior is more easily stabilized by the method. A treatment that uses this method has normalized body weight and restored the health of several hundred patients with anorexia nervosa and other eating disorders and has reduced the weight and improved the health of severely overweight patients.

Subjects eat food from a plate placed on a scale connected to a computer that records the weight loss of the plate during the meal. Feedback on the computer screen allows the subject to adapt her/his eating behavior to reference curves thus normalizing body weight.

用一种新的反馈系统控制进食行为

Subjects eat food from a plate that sits on a scale connected to a computer that records the weight loss of the plate during the meal and makes up a curve ...

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Despite the evidence that cardiac rehabilitation (CR) reduces the risk of recurrent cardiac events, only a minority of eligible patients are willing to join existing programs at cardiac rehabilitation centers. The unique remote patient monitoring system presented here enables healthcare providers to monitor CR patients at home in real-time and at low cost. The system combines mobile technology, artificial intelligence, and supportive services, expanding the delivery of medical care to the patient&#39;s home. The primary aim of the study is to increase the long-term adherence to physical activity in patients who participate in CR via the addition of a home-based digitally monitored CR component to the standard CR program in patients with ischemic heart disease (IHD), with the idea of forming new habitual health behaviors and increasing the long-term motivation for physical exercise (PE) habits at home. Secondary aims are to assess the program&#39;s impact on the physical activity level measured by average steps per day, minutes of exercise per week, blood pressure, metabolic parameters, body mass index, and waist-to-hip ratio, as well as a quality-of-life (QoL) questionnaire.The study has two arms: (1) home-based monitored exercise using a smart digital garment and wristband, in addition to motivation and reinforcement via text messages; (2) standard CR facility-based exercise. The study design is a randomized, controlled trial comparing standard CR to a home-based monitoring and reinforcement program. The study program is designed for 12 weeks.Clinical tests and anthropometric measurements are performed before and after the study, measuring height, weight, waist circumference, visceral fat and body mass index (BMI), blood pressure, and HbA1c and lipid profile. Patients have to complete a baseline survey including socio-demographic characteristics and QoL questionnaire SF-36. At the end of the study, patients complete a survey regarding the use of the smart digital garment&#39;s benefits and usability. The study is currently underway.

The aim is to promote a new approach to cardiac rehabilitation (CR), using a unique remote patient monitoring system that will enable healthcare providers to monitor CR patients at home at low cost, with the intention of making CR services more accessible and improving compliance. The study is currently underway.

一种新型的家庭心脏康复监控数字平台

Despite the evidence that cardiac rehabilitation (CR) reduces the risk of recurrent cardiac events, only a minority of eligible patients are willing to ...

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 ...

Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

To collect dietary intake data in a fast and reliable manner, a flexible and innovative smartphone application (app) called Traqq was developed (iOS/Android). This app can be used as a food record and&#160;24-h recall (or shorter recall periods). Different sampling schemes can be created on either prespecified or random days/times within a predetermined period for both methods, with&#160;push notifications to urge the participants to register their food intake. In case of non-response, notifications are automatically rescheduled to ensure complete data collection. For use as a food record, respondents can access the app and log their food intake throughout the day. Food records close automatically at the end of the day; recalls close after submission of the consumed items. The recall as well as the food record module provide access to an extensive food list based on the Dutch food composition database (FCDB), which can be accustomed to fit different research purposes. When selecting a food item, respondents are simultaneously prompted to insert portion size, i.e., in household measures (e.g., cups, spoons, glasses), standard portion sizes (e.g., small, medium, large), or weight in grams, and eating occasion/time of consumption. Portion size options can be adjusted, e.g., only entry in grams in case of a weighed food record or time of consumption instead of eating occasion). The app also includes a My Dishes function, which allows the respondent to create their own recipes or product combinations (e.g., a daily breakfast) and only report the total quantity consumed. Subsequently, the app accounts for yield and retention factors. The data are stored on a secure server. If desired, additional questions, i.e., in general or those related to specific food items or eating occasions can be incorporated. This paper describes the development of the system (app and backend), including expert evaluations and usability testing.

This article describes the protocol for the development of an innovative smartphone-based dietary assessment application Traqq, including expert evaluations and usability testing.

创新智能手机饮食评估工具的迭倍开发:Traqq

To collect dietary intake data in a fast and reliable manner, a flexible and innovative smartphone application (app) called Traqq was developed ...

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

The vast majority of dietary and eating behavior assessment methods are based on self-reports. They are burdensome and also prone to measurement errors. Recent technological innovations allow for the development of more accurate and precise dietary and eating behavior assessment tools that require less effort for both the user and the researcher. Therefore, a new sensor-based device to assess food intake and eating behavior was developed. The device is a regular dining tray equipped with a video camera and three separate built-in weighing stations. The weighing stations measure the weight of the bowl, plate, and drinking cup continuously over the course of a meal. The video camera positioned to the face records eating behavior characteristics (chews, bites), which are analyzed using artificial intelligence (AI)-based automatic facial expression software. The tray weight and the video data are transported at real-time to a personal computer (PC) using a wireless receiver. The outcomes of interest, such as the amount eaten, eating rate and bite size, can be calculated by subtracting the data of these measures at the timepoints of interest. The information obtained by the current version of the tray can be used for research purposes, an upgraded version of the device would also facilitate the provision of more personalized advice on dietary intake and eating behavior. Contrary to the conventional dietary assessment methods, this dietary assessment device measures food intake directly within a meal and is not dependent on memory or the portion size estimation. Ultimately, this device is therefore suited for daily main meal food intake and eating behavior measures. In the future, this technology based dietary assessment method can be linked to health applications or smart watches to obtain a complete overview of exercise, energy intake, and eating behavior.

This protocol shows and explains a new technology-based dietary assessment method. The method consists of a dining tray with multiple built-in weighing scales and a video camera. The device is unique in the sense that it incorporates automated measures of food and drink intake and eating behavior over the course of a meal.

自动食物摄入和饮食行为评估方法的概念开发和使用

The vast majority of dietary and eating behavior assessment methods are based on self-reports. They are burdensome and also prone to measurement errors. ...

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

In medicine or industry, the analysis of high-dimensional data sets is increasingly required. However, available technical solutions are often complex to use. Therefore, new approaches like immersive analytics are welcome. Immersive analytics promise to experience high-dimensional data sets in a convenient manner for various user groups and data sets. Technically, virtual-reality devices are used to enable immersive analytics. In Industry 4.0, for example, scenarios like the identification of outliers or anomalies in high-dimensional data sets are pursued goals of immersive analytics. In this context, two important questions should be addressed for any developed technical solution on immersive analytics: First, is the technical solutions being helpful or not? Second, is the bodily experience of the technical solution positive or negative? The first question aims at the general feasibility of a technical solution, while the second one aims at the wearing comfort. Extant studies and protocols, which systematically address these questions are still rare. In this work, a study protocol is presented, which mainly investigates the usability for immersive analytics in Industry 4.0 scenarios. Specifically, the protocol is based on four pillars. First, it categorizes users based on previous experiences. Second, tasks are presented, which can be used to evaluate the feasibility of the technical solution. Third, measures are presented, which quantify the learning effect of a user. Fourth, a questionnaire&#160;evaluates the stress level when performing tasks. Based on these pillars, a technical setting was implemented that uses mixed reality smartglasses to apply the study protocol. The results of the conducted study show the applicability of the protocol on the one hand and the feasibility of immersive analytics in Industry 4.0 scenarios on the other. The presented protocol includes a discussion of discovered limitations.

This protocol delineates the technical setting of a developed mixed reality application that is used for immersive analytics. Based on this, measures are presented, which were used in a study to gain insights into usability aspects of the developed technical solution.

评估工业 4.0 方案中沉浸式分析的混合现实解决方案的可用性方面

In medicine or industry, the analysis of high-dimensional data sets is increasingly required. However, available technical solutions are often complex to ...

工程学

Deep Neural Networks for Image-Based Dietary Assessment

Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and increase its quality. Today, automated solutions are able to record a person's dietary intake in a much simpler way, such as by taking an image with a smartphone camera. In this article, we will focus on such image-based approaches to dietary assessment. For the food image recognition problem, deep neural networks have achieved the state of the art in recent years, and we present our work in this field. In particular, we first describe the method for food and beverage image recognition using a deep neural network architecture, called NutriNet. This method, like most research done in the early days of deep learning-based food image recognition, is limited to one output per image, and therefore unsuitable for images with multiple food or beverage items. That is why approaches that perform food image segmentation are considerably more robust, as they are able to identify any number of food or beverage items in the image. We therefore also present two methods for food image segmentation - one is based on fully convolutional networks (FCNs), and the other on deep residual networks (ResNet).

The goal of the work presented in this article is to develop technology for automated recognition of food and beverage items from images taken by mobile devices. The technology comprises of two different approaches - the first one performs food image recognition while the second one performs food image segmentation.

基于图像的饮食评估深度神经网络

Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and ...

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

The present work focuses on how to build a wearable brain-computer interface (BCI). BCIs are a novel means of human-computer interaction that relies on direct measurements of brain signals to assist both people with disabilities and those who are able-bodied. Application examples include robotic control, industrial inspection, and neurorehabilitation. Notably, recent studies have shown that steady-state visually evoked potentials (SSVEPs) are particularly suited for communication and control applications, and efforts are currently being made to bring BCI technology into daily life. To achieve this aim, the final system must rely on wearable, portable, and low-cost instrumentation. In exploiting SSVEPs, a flickering visual stimulus with fixed frequencies is required. Thus, in considering daily-life constraints, the possibility to provide visual stimuli by means of smart glasses was explored in this study. Moreover, to detect the elicited potentials, a commercial device for electroencephalography (EEG) was considered. This consists of a single differential channel with dry electrodes (no conductive gel), thus achieving the utmost wearability and portability. In such a BCI, the user can interact with the smart glasses by merely staring at icons appearing on the display. Upon this simple principle, a user-friendly, low-cost BCI was built by integrating extended reality (XR) glasses with a commercially available EEG device. The functionality of this wearable XR-BCI was examined with an experimental campaign involving 20 subjects. The classification accuracy was between 80%-95% on average depending on the stimulation time. Given these results, the system can be used as a human-machine interface for industrial inspection but also for rehabilitation in ADHD and autism.

This paper discusses how to build a brain-computer interface by relying on consumer-grade equipment and steady-state visually evoked potentials. For this, a single-channel electroencephalograph exploiting dry electrodes was integrated with augmented reality glasses for stimuli presentation and output data visualization. The final system was non-invasive, wearable, and portable.

面向工业和医疗保健的单通道非侵入式可穿戴脑机接口

The present work focuses on how to build a wearable brain-computer interface (BCI). BCIs are a novel means of human-computer interaction that relies on ...

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 ...