This work is supported by the Spanish State Research Agency and the European Regional Development Fund (ERDF) under the PALLAS (TIN2016-80515-R AEI/EFRD, EU) project.

All methods described below have been approved by the regional government of Galicia&#39;s committee for research ethics of Pontevedra-Vigo-Ourense (reg. code 2017/336). The protocol was implemented for first year students at the School of Telecommunication Engineering - University of Vigo, both in a comfortable laboratory room and in several lectures and practice sessions of a bachelor&#39;s degree course on Computer Architectures.
1. Prepare the Devices
<ol>
	<li>Connect the smartphone and tablet device to a stable internet connection.</li>
	<li>Turn on Bluetooth communications in the smartphone.</li>
	<li>In the smartphone, search in the corresponding official app store the wrist wearable application. Download and install it.</li>
	<li>In the smartphone, search for the PhysiologicalSignals app to capture physiological signals. Download and install it. 
	NOTE: Currently, the app is a beta version and access can be provided by request.</li>
	<li>In the tablet, search for the StressTest app to be used in the research laboratory experiments. Download and install it. 
	NOTE: Currently, the app is a beta version and access can be provided by request.</li>
	<li>Turn on the COTS wrist wearable device and place the wearable.</li>
	<li>In the smartphone, open the official COTS wrist wearable application. 
	NOTE: The app will proceed to synchronize the wearable device with the smartphone. In some devices, an e-mail address is required.</li>
	<li>In the smartphone, open the PhysiologicalSignals app.
	<ol>
		<li>In case of being notified of a sensor access request, accept it.</li>
		<li>Check the device. Wait for the PhysiologicalSignals app to display the word Weared in green. 
		NOTE: This indicate that the wearable device has been detected and therefore the transmission of information from the sensors to the smartphone is enabled. If this message does not appear repeat from step 1.6.</li>
	</ol>
	</li>
</ol>
2. The Laboratory Phase
<ol>
	<li>Prepare the laboratory setting. Choose a comfortable and non-disturbing room without distracting noise and with a comfortable temperature (between 22 &#176;C and 26 &#176;C).</li>
	<li>Turn on the wrist wearable device, place it around the subject&#180;s non-dominant wrist and place the headphones on the head of the student. Fit the wearable tightly but comfortably around the wrist.</li>
	<li>Connect the smartphone and tablet to a stable internet connection and verify that the Bluetooth connection is active.</li>
	<li>In the smartphone, launch the PhysiologicalSignals app.
	<ol>
		<li>Wait for the app to display the word Weared in green.</li>
		<li>Select the Change User option in the left configuration menu and provide the ID of the subject who will complete the tests and click Save.</li>
	</ol>
	</li>
	<li>In a laptop, access the dashboard and enter the test administrator&#39;s ID and password. 
	NOTE: Currently, for private and security concerns, access to the dashboard is only available under request.
	<ol>
		<li>Select the subject ID and the subject&#39;s stress tab.</li>
		<li>Check the physiological signals evolution and wait for the wearable device to reach thermal stability before starting the experiment. 
		NOTE: The thermal stability is identified as a plateau in the graph.</li>
	</ol>
	</li>
	<li>In the tablet, launch the StressTest application.
	<ol>
		<li>Explain to the subject the four laboratory tasks. Show some of the screens and actions to perform during each one of the tasks. 
		NOTE: This is very important, because the subject should feel stressed or relaxed in accordance to the performed activities, and not fear or concern about what is going to happen.</li>
		<li>Tell the student not to rest their arms on the table and to use the hand where the wearable device is placed to perform the activities.</li>
		<li>Enter the same user ID as in step 2.4.2 and click the arrow.</li>
	</ol>
	</li>
	<li>Launch the video task and give full control to the student.
	<ol>
		<li>Observe that the task is carried out without incident.</li>
		<li>When the task is finished, check that the subject provides the perceived stress.</li>
	</ol>
	</li>
	<li>Launch the Stroop Color task (SCWT) consecutively for levels 1, 2 and 3.
	<ol>
		<li>For each level, observe that the subtask is carried out without incident.</li>
		<li>When each subtask is finished, check that the subject provides the perceived stress.</li>
		<li>Only for level 3 and only in case the subject does not solve it after 4 minutes, terminate the task by pressing the arrow located at the top of the screen.</li>
	</ol>
	</li>
	<li>Launch the Paced Auditory Serial Addition test (PASAT).
	<ol>
		<li>Observe that the task is carried out without incident.</li>
		<li>In case the subject does not solve the PASAT test after 4 minutes, terminate the task by pressing the arrow located at the top of the screen.</li>
		<li>When the task is finished, check that the subject provides the perceived stress.</li>
	</ol>
	</li>
	<li>Launch the Hyperventilation test.
	<ol>
		<li>Observe the evolution of HR using the dashboard. If physiological signals do not change significantly, ask the subject to increase inspiration and expiration rates gradually.</li>
		<li>In case the subject feels dizziness or uncomfortable halt this task. In any case, complete the task after four minutes.</li>
		<li>When the task is finished, check that the subject provides the perceived stress.</li>
	</ol>
	</li>
</ol>
3. The Classroom Phase
<ol>
	<li>Turn on the wrist wearable device and place the wearable around the subject&#180;s non-dominant wrist. Fit the wearable tightly but comfortably around the wrist.</li>
	<li>Connect the smartphone to a stable internet connection and verify the Bluetooth connection is active.</li>
	<li>In the smartphone, launch the PhysiologicalSignals app.
	<ol>
		<li>Wait for the app to display the word Weared in green.</li>
		<li>Select in the configuration menu the Change User option, provide the ID of the subject who will complete the tests and click Save.</li>
	</ol>
	</li>
	<li>In a laptop, access the dashboard and enter the test administrator&#39;s ID and password.
	<ol>
		<li>Select the subject ID and the subject&#39;s stress tab.</li>
		<li>Check the evolution of physiological signals.</li>
	</ol>
	</li>
	<li>Take annotations about any relevant event occurring in the classroom in relation to the student-teacher interaction. 
	NOTE: Relevant information and basic events will be used to label physiological samples afterwards. Example events are a question from the teacher to the student, or a theoretical explanation is initiated.</li>
	<li>At the end of the lecture, ask the subject to complete the questionnaire about their level of stress at specific times during the session, according to a 5-level scale.</li>
</ol>
4. Data Analysis
<ol>
	<li>In a laptop, access the dashboard and enter the test administrator&#39;s ID and password.
	<ol>
		<li>Select the subject ID and the subject&#39;s stress tab.</li>
		<li>Select the day of a classroom experiment.</li>
	</ol>
	</li>
	<li>Label the samples of the subject by identifying activities and perceived stress levels.
	<ol>
		<li>Identify lecture-room activities and their duration according to the starting and finishing times and their types.</li>
		<li>For each activity, select a perceived stress level.</li>
	</ol>
	</li>
	<li>For each subject and each session, download the file with the tagged samples. 
	NOTE: A comma-separated-values (CSV) file is created for each student, each row reflecting the values of the physiological signals with their standard deviation, slope and diff, the activity type, the activity-based stress (i.e., the stress associated by default to the activity) and the subject perceived stress.</li>
	<li>Launch the data analytics package.
	<ol>
		<li>Choose a set of classifiers (e.g., SVM, C4.5, k-NN, Random Forest, Na&#239;ve Bayes and Zero R) and import the CSV file for all students for each session.</li>
		<li>Train and evaluate classifiers using the 10-fold cross-validation technique. 
		NOTE: Depending on the analyses, activity type, activity-based stress or stress perceived, shall be selected as dependent variable for the analysis.</li>
		<li>Finally, check the results for accuracy and error rates.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

COTS wearable devices are among the most popular consumer electronics products available today. These devices are typically used to monitor physical activities, but their capabilities and performance could be of great interest in other areas. In this paper, a protocol to evaluate the use of COTS wearable devices for estimating stress in learning environments is discussed. The definition of such a protocol is especially relevant in order to analyze different solutions involving wearables and machine learning algorithms. T...

State-of-the-art wearable technologies are widely available, and their application environments are continuously expanding. We can find in the market many different devices, among which COTS wrist wearables1, such as smart watches and smart bands, are popular among athletes as a personal physical fitness monitoring tool2. By applying data analytic techniques, the data obtained using these devices can be processed to provide indicators such as general physical state, sleep quality or recovery factor. The demonstrated applicability in this area raised interest in the academic community about their possible application to other fields, especially in the health domain3,4, although the strict requirements of clinical trials limit their introduction. However, in a less demanding context such as education, we can find in the literature recent investigations involving the use of different types of wearable devices, both related to teaching activities5,6 and to the estimation of certain characteristics of the student such as sleep patterns7, or the analysis of students&#39; engagement in different educational activities8.
In our case, we focus on analyzing COTS wrist wearable devices as means to collect physiological signals that would eventually facilitate stress estimation, which in turn is a key aspect in educational contexts. Stress has a relevant influence in the development of academic activities and overall students&#39; performance. For example, stress levels are directly related to the onset of the burnout syndrome in students9,10,11, and high stress levels are especially relevant during the freshman year, where drop-out rates between 20% and 30%12,13 are common. Detecting and controlling stress indicators could dramatically improve academic performance.
The use of COTS wrist wearable devices is justified because they have sensors that provide information on physiological signals that have been widely used by the scientific community in stress assessment and detection. Some of the signals referred to in the literature used for this purpose include heart rate (HR)14, heart rate varaibility15, skin temperature (ST)16, respiration14, and galvanic skin response (GSR)17. These signals can be collected by COTS wrist wearables. However, they do not offer the same performance as clinical devices. There are differences related to the accuracy of sensors among devices18,19,20,21. Nevertheless, previous works18,19,20,21 have shown that, in a slow movement scenario, COTS wrist wearable sensors have error patterns similar to specialized devices.
The aim of this paper is to introduce a protocol to evaluate different solutions for stress estimation in students using COTS wrist wearables. There are many arrangements that can be proposed to estimate stress levels, involving the use of different wrist wearable devices and data analytics techniques, and more specifically machine learning algorithms. COTS wrist wearables are characterized by their high fragmentation, heterogeneity and interoperability problems22. Three companies have an aggregated market share of almost 50%23, but many other companies account for much smaller individual market shares, with an aggregated share above 50%. On the other hand, in terms of heterogeneity, not all wearables have the same number and type of sensors, with accelerometers and h sensors being the most common, and ST&#39;s and GSR&#39;s being only present in 5% of the devices studied. As for interoperability, there are different operating systems and data collection approaches that are not compatible with each other. As for the machine learning techniques that can be applied to estimate stress from the data collected by means of a wrist device, there are many options available24, including decision trees, neural networks, nearest neighbor approaches, Na&#239;ve Bayes classifiers, etc. To sum up, there is a great variety of solutions that may be developed for stress estimation, so it is instrumental to design an evaluation protocol to facilitate the comparison among different tentative options to eventually select the most suitable in a given context.
For the implementation of the protocol, several tools are needed (Figure 1). First, a COTS wrist wearable device is needed to fetch physiological data. This wearable device should have at least HR monitoring capabilities, but additional sensors are desirable (e.g., accelerometer, ST, GSR sensors). Second, a smartphone running the PhysiologicalSignal app is required to collect the data captured by the wearable device. Third, a tablet running the StressTest app is needed to run stress induction exercises (the smartphone could be used instead the tablet for this purpose). Fourth, some questionnaires to collect qualitative data on students&#39; perception on stress. Fifth, a server with a Web service25 to perform data collection and pre-processing, and a Web dashboard to show the evolution of the signals. And finally, a data analytics package26 to process the data collected about students using machine learning techniques.
The evaluation protocol is organized into two phases. The first one, the laboratory phase, is carried out in a comfortable room, where different stress levels (i.e., &#34;relax&#34;, &#34;concentrated stress&#34; and &#34;stress&#34;) are induced to a target subject (a student) through several common stress-inducing tasks. The second part takes place in the classroom, and it involves monitoring the student during the accomplishment of several academic activities: theoretical explanations, individual activities, short tests, exams, etc. During the implementation of this protocol, the subject&#39;s physiological signals are captured by means of a wrist device. Finally, these signals are processed by machine learning algorithms to provide estimations on the level of stress.
During the laboratory phase, the StressTest app is used to induce different stress levels. This app guides the subject to the completion of four different tasks. The first task is to create a baseline for stress analysis. In this task, the student visualizes a 4-minute relaxing video in which different shots of a sunset on a bridge are shown. The second task is an adaptation of the Stroop Color and Word Test27&#160;(SCWT). Every two seconds, the subject must choose the color in which the name of a color is painted (red, green, orange, blue and purple). Several buttons located at the bottom of the screen containing the initial letter of each color are available for the subject to choose the painted color at each time. For example, the button that refers to blue depicts the letter B. In our case, this test is divided into three different levels of difficulty. For the first level (SCWT1), the colored &#34;words of colors&#34; will appear in the same order as the buttons, so color and name match directly. This level is taken as baseline, as it does not involve any difficulty and the subject should only press the buttons properly, always in the same order. For the second level (SCWT2), the colored &#34;words of colors&#34; appear randomly, but the correspondence between name and color is maintained. Every time the subject fails a beep is emitted, and if two errors are made, the correct color score will be reset. For the last, most difficult level (SCWT3), name and color do not match. In this way this level is intended to be more complex and stressful for the subject. The third task consists on the Paced Auditory Serial Addition test (PASAT)28, which measures how the student experiences a concentration test. During this task, a sequence of consecutive numbers is played aloud, and the student must add the last two numbers and write the result in the provided on-screen box before listening to the next number. In this task, if the subject makes a mistake, a disturbing event occurs to generate stress (two numbers sound at the same time or a long period of silence in maintained). In this case, if three errors are committed, the sum account will be reset. The fourth task consists on a hyperventilation activity to induce the same variation in the physiological signals that would provoke a stressful situation17. At the end of each task and level, the subject has to indicate the level of perceived stress, using the application itself, according to a 5-value Likert scale.
During the classroom phase, students carry out their ordinary academic activities together with the rest of their classmates. The protocol focuses on the stress levels that occur during classroom-specific activities. At the end of the lecture, a brief questionnaire (Annex 1) is completed by the student to indicate the perceived level of stress in the several activities according to a 5-value scale.

<table border="0" cellpadding="0" cellspacing="0" width="968">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:219px;">Microsoft Band 2</td>
			<td style="width:191px;">Microsoft</td>
			<td style="width:119px;"></td>
			<td style="width:439px;">Wearable</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:219px;">Nexus 5</td>
			<td style="width:191px;">LG/Google</td>
			<td style="width:119px;"></td>
			<td>Smartphone</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">PhysiologicalSignals</td>
			<td style="width:191px;">Developed by the paper authors</td>
			<td style="width:119px;"></td>
			<td>App to collect data from wearables</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">StressTest</td>
			<td style="width:191px;">Developed by the paper authors</td>
			<td style="width:119px;"></td>
			<td>App to develop laboraty experiment: Video visualization, Stroop color and word test, paced auditory serial addition test (PASAT) , hyperventilation activity&#160;</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;">Quizs</td>
			<td style="width:191px;">Developed by the paper authors</td>
			<td style="width:119px;"></td>
			<td>Questionnaires to collect qualitative data on students&#8217; perception on stress</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:219px;">Server</td>
			<td style="width:191px;">Developed by the paper authors</td>
			<td style="width:119px;"></td>
			<td>Server to store, analyze and display data</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Weka application</td>
			<td style="width:191px;">University of Waikato</td>
			<td style="width:119px;"></td>
			<td>Application to process the data using machine learning techniques</td>
		</tr>
	</tbody>
</table>

evaluation of commercial-off-the-shelf wrist wearables to estimate stress on students

Wearable commercial-off-the-shelf (COTS) devices have become popular during the last years to monitor sports activities, primarily among young people. These devices include sensors to gather data on physiological signals such as heart rate, skin temperature or galvanic skin response. By applying data analytics techniques to these kinds of signals, it is possible to obtain estimations of higher-level aspects of human behavior. In the literature, there are several works describing the use of physiological data collected using clinical devices to obtain information on sleep patterns or stress. However, it is still an open question whether data captured using COTS wrist wearables is sufficient to characterize the learners' psychological state in educational settings. This paper discusses a protocol to evaluate stress estimation from data obtained using COTS wrist wearables. The protocol is carried out in two phases. The first stage consists of a controlled laboratory experiment, where a mobile app is used to induce different stress levels in a student by means of a relaxing video, a Stroop Color and Word test, a Paced Auditory Serial Addition test, and a hyperventilation test. The second phase is carried out in the classroom, where stress is analyzed while performing several academic activities, namely attending to theoretical lectures, doing exercises and other individual activities, and taking short tests and exams. In both cases, both quantitative data obtained from COTS wrist wearables and qualitative data gathered by means of questionnaires are considered. This protocol involves a simple and consistent method with a stress induction app and questionnaires, requiring a limited participation of support staff.

The protocol discussed was put into practice in a Computer Architectures course in the first year of the Telecommunication Engineering degree at the University of Vigo. This course has more than 200 students enrolled who are organized into 10 working groups. To carry out this experiment, students from four of the groups were invited to enroll at the beginning of the academic year. The project attracted considerable interest among the students, and around 30 students volunteered to partici...

Watch this Scientific Journal Video about Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students at JoVE.com

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

A protocol to evaluate solutions based on commercial-off-the-shelf (COTS) wrist wearables to estimate stress in students is proposed. The protocol is carried out in two phases, an initial laboratory-based stress induction test, and a monitoring stage taking place in the classroom while the student is performing academic activities.

Wearable commercial-off-the-shelf (COTS) devices have become popular during the last years to monitor sports activities, primarily among young people. ...

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Research

JoVE Journal

Engineering

7.4K Views.  University of Vigo, Campus Lagoas-Marcosende. A protocol to evaluate solutions based on commercial-off-the-shelf (COTS) wrist wearables to estimate stress in students is proposed. The protocol is carried out in two phases, an initial laboratory-based stress induction test, and a monitoring stage taking place in the classroom while the student is performing academic activities.

Video: Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Preparation and Evaluation of Hybrid Composites of Chemical Fuel and Multi-walled Carbon Nanotubes in the Study of Thermopower Waves

When a chemical fuel at a certain position in a hybrid composite of the fuel and a micro/nanostructured material is ignited, chemical combustion occurs along the interface between the fuel and core materials. Simultaneously, dynamic changes in thermal and chemical potentials across the micro/nanostructured materials result in concomitant electrical energy generation induced by charge transfer in the form of a high-output voltage pulse. We demonstrate the entire procedure of a thermopower wave experiment, from synthesis to evaluation. Thermal chemical vapor deposition and the wet impregnation process are respectively employed for the synthesis of a multi-walled carbon nanotube array and a hybrid composite of picric acid/sodium azide/multi-walled carbon nanotubes. The prepared hybrid composites are used to fabricate a thermopower wave generator with connecting electrodes. The combustion of the hybrid composite is initiated by laser heating or Joule-heating, and the corresponding combustion propagation, direct electrical energy generation, and real-time temperature changes are measured using a high-speed microscopy system, an oscilloscope, and an optical pyrometer, respectively. Furthermore, the crucial strategies to be adopted in the synthesis of hybrid composite and initiation of their combustion that enhance the overall thermopower wave energy transfer are proposed.

A protocol for conducting thermopower wave experiments is presented. The synthesis of hybrid composites of a chemical fuel and micro/nanostructured material, manufacturing of a thermopower wave generator, and methods for measuring the corresponding physical phenomena are described.

When a chemical fuel at a certain position in a hybrid composite of the fuel and a micro/nanostructured material is ignited, chemical combustion occurs ...

A Cost-effective and Reliable Method to Predict Mechanical Stress in Single-use and Standard Pumps

Pumps are mainly used when transferring sterile culture broths in biopharmaceutical and biotechnological production processes. However, during the pumping process shear forces occur which can lead to qualitative and/or quantitative product loss. To calculate the mechanical stress with limited experimental expense, an oil-water emulsion system was used, whose suitability was demonstrated for drop size detections in bioreactors1. As drop breakup of the oil-water emulsion system is a function of mechanical stress, drop sizes need to be counted over the experimental time of shear stress investigations. In previous studies, the inline endoscopy has been shown to be an accurate and reliable measurement technique for drop size detections in liquid/liquid dispersions. The aim of this protocol is to show the suitability of the inline endoscopy technique for drop size measurements in pumping processes. In order to express the drop size, the Sauter mean diameter d32 was used as the representative diameter of drops in the oil-water emulsion. The results showed low variation in the Sauter mean diameters, which were quantified by standard deviations of below 15%, indicating the reliability of the measurement technique.

Shear stress investigations on an oil-water emulsion system result in drop breakup over the experimental time. To count drop sizes in pumping processes, the suitability of inline endoscopy was successfully demonstrated in this protocol.

Pumps are mainly used when transferring sterile culture broths in biopharmaceutical and biotechnological production processes. However, during the pumping ...

Evanescent Field Based Photoacoustics: Optical Property Evaluation at Surfaces

Here, we present a protocol to estimate material and surface optical properties using the photoacoustic effect combined with total internal reflection. Optical property evaluation of thin films and the surfaces of bulk materials is an important step in understanding new optical material systems and their applications. The method presented can estimate thickness, refractive index, and use absorptive properties of materials for detection. This metrology system uses evanescent field-based photoacoustics (EFPA), a field of research based upon the interaction of an evanescent field with the photoacoustic effect. This interaction and its resulting family of techniques allow the technique to probe optical properties within a few hundred nanometers of the sample surface. This optical near field allows for the highly accurate estimation of material properties on the same scale as the field itself such as refractive index and film thickness. With the use of EFPA and its sub techniques such as total internal reflection photoacoustic spectroscopy (TIRPAS) and optical tunneling photoacoustic spectroscopy (OTPAS), it is possible to evaluate a material at the nanoscale in a consolidated instrument without the need for many instruments and experiments that may be cost prohibitive.

Here we present a protocol to estimate material and surface optical properties using the photoacoustic effect combined with total internal reflection. This technique evanescent field-based photoacoustics can be used to create a photoacoustic metrology system to estimate materials&#39; thicknesses, bulk and thin film refractive indices, and explore their optical properties.

Here, we present a protocol to estimate material and surface optical properties using the photoacoustic effect combined with total internal reflection. ...

The Evolution of Silica Nanoparticle-polyester Coatings on Surfaces Exposed to Sunlight

Corrosion of metallic surfaces is prevalent in the environment and is of great concern in many areas, including the military, transport, aviation, building and food industries, amongst others. Polyester and coatings containing both polyester and silica nanoparticles (SiO2NPs) have been widely used to protect steel substrata from corrosion. In this study, we utilized X-ray photoelectron spectroscopy, attenuated total reflection infrared micro-spectroscopy, water contact angle measurements, optical profiling and atomic force microscopy to provide an insight into how exposure to sunlight can cause changes in the micro- and nanoscale integrity of the coatings. No significant change in surface micro-topography was detected using optical profilometry, however, statistically significant nanoscale changes to the surface were detected using atomic force microscopy. Analysis of the X-ray photoelectron spectroscopy and attenuated total reflection infrared micro-spectroscopy data revealed that degradation of the ester groups had occurred through exposure to ultraviolet light to form COO&#903;, -H2C&#903;, -O&#903;, -CO&#903; radicals. During the degradation process, CO and CO2 were also produced.

Two types of surfaces, polyester-coated steel and polyester coated with a layer of silica nanoparticles, were studied. Both surfaces were exposed to sunlight, which was found to cause substantial changes in the chemistry and nanoscale topography of the surface.

Corrosion of metallic surfaces is prevalent in the environment and is of great concern in many areas, including the military, transport, aviation, ...

Scanning Light Scattering Profiler (SLPS) Based Methodology to Quantitatively Evaluate Forward and Backward Light Scattering from Intraocular Lenses

The scanning light scattering profiler (SLSP) methodology has been developed for the full-angle quantitative evaluation of forward and backward light scattering from intraocular lenses (IOLs) using goniophotometer principles. This protocol describes the SLSP platform and how it employs a 360&#176; rotational photodetector sensor that is scanned around an IOL sample while recording the intensity and location of scattered light as it passes through the IOL medium. The SLSP platform can be used to predict, non-clinically, the propensity for current and novel IOL designs and materials to induce light scatter. Non-clinical evaluation of light scattering properties of IOLs can significantly reduce the number of patient complaints related to unwanted glare, glistening, optical defects, poor image quality, and other phenomena associated with the unintended light scattering. Future studies should be conducted to correlate SLSP data with clinical results to help identify which measured light scatter is most problematic for patients that have undergone cataract surgery subsequent to IOL implantation.

Scanning Light Scattering Profiler SLPS Based Methodology to Quantitatively Evaluate Forward and Backward Light Scattering from Intraocular Lenses

This protocol describes the scanning light scattering profiler (SLSP) that enables the full-angle quantitative evaluation of forward and backward scattering of light from intraocular lenses (IOLs) using goniophotometer principles.

The scanning light scattering profiler (SLSP) methodology has been developed for the full-angle quantitative evaluation of forward and backward light ...

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

This study presents a series of protocols of designing and manufacturing a glasses-type wearable device that detects the patterns of temporalis muscle activities during food intake and other physical activities. We fabricated a 3D-printed frame of the glasses and a load cell-integrated printed circuit board (PCB) module inserted in both hinges of the frame. The module was used to acquire the force signals, and transmit them wirelessly. These procedures provide the system with higher mobility, which can be evaluated in practical wearing conditions such as walking and waggling. A performance of the classification is also evaluated by distinguishing the patterns of food intake from those physical activities. A series of algorithms were used to preprocess the signals, generate feature vectors, and recognize the patterns of several featured activities (chewing and winking), and other physical activities (sedentary rest, talking, and walking). The results showed that the average F1 score of the classification among the featured activities was 91.4%. We believe this approach can be potentially useful for automatic and objective monitoring of ingestive behaviors with higher accuracy as practical means to treat ingestive problems.

This study presents a protocol of designing and manufacturing a glasses-type wearable device that detects the patterns of food intake and other featured physical activities using load cells inserted in both hinges of the glasses.

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

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences

As the number of magnetic resonance imaging (MRI) scanners and patients with medical implants is constantly growing, radiologists increasingly encounter metallic implant-related artifacts in MRI, resulting in reduced image quality. Therefore, the MRI suitability of implants in terms of artifact volume, as well as the development of pulse sequences to reduce image artifacts, are becoming more and more important. Here, we present a comprehensive protocol which allows for a standardized evaluation of the artifact volume of implants on MRI. Furthermore, this protocol can be used to analyze the vulnerability of different pulse sequences to artifacts. The proposed protocol can be applied to T1- and T2-weighted images with or without fat-suppression and all passive implants. Furthermore, the procedure enables the separate and three-dimensional identification of signal loss and pile-up artifacts. As previous investigations differed greatly in evaluation methods, the comparability of their results was limited. Thus, standardized measurements of MRI artifact volumes are necessary to provide better comparability. This may improve the development of the MRI suitability of implants and better pulse sequences to finally improve patient care.

We describe a standardized method to evaluate magnetic resonance imaging artifacts caused by implants to estimate the suitability of the implants for magnetic resonance imaging and/or the vulnerability of different pulse sequences to metallic artifacts simultaneously.

As the number of magnetic resonance imaging (MRI) scanners and patients with medical implants is constantly growing, radiologists increasingly encounter ...

Film Control to Study Contributions of Waves to Droplet Impact Dynamics on Thin Flowing Liquid Films

Droplet impact is a very common phenomenon in nature and attracts attention due to its aesthetic fascination and wide-ranging applications. Previous studies on flowing liquid films have neglected the contributions of spatial structures of waves to the impact outcome, while this has recently been shown to have a significant influence on the drop impact dynamics. In this report, we outline a step-by-step procedure to investigate the effect of periodic inlet forcing of a flowing liquid film leading to the production of spatiotemporally regular wave structures on drop impact dynamics. A function generator in connection with a solenoid valve is used to excite these spatiotemporally regular wave structures on the film surface while the impact dynamics of uniform-sized droplets are captured using a high-speed camera. Three distinct regions are then studied; viz. the capillary wave region preceding the large wave peak, the flat film region, and the wave hump region. The effects of important dimensionless quantities such as film Reynolds, drop Weber and Ohnesorge numbers parameterized by the film flow rate, drop speed, and drop size are also examined. Our results show interesting, hitherto undiscovered dynamics brought about by this application of film inlet forcing of the flowing film for both low and high inertia drops.

A protocol to study the contributions of waves to droplet impact dynamics on flowing liquid films is presented.

Droplet impact is a very common phenomenon in nature and attracts attention due to its aesthetic fascination and wide-ranging applications. Previous ...

Fabric Moisture Uniform Control to Study the Influence of Air Impingement Parameters on Fabric Drying Characteristics

Impinging dryness is now a widely used and effective way for fabric drying due to its high heat and mass transfer coefficient. Previous studies on fabric drying have neglected the contributions of moisture uniformity and diffusion coefficient to the drying process; though, they have recently been shown to have a significant influence on drying characteristics. This report outlines a step-by-step procedure to investigate the effects of air impingement parameters on a fabric&#39;s drying characteristics by controlling the uniformity of its area moisture distribution. A hot air blower unit equipped with an angle adjustable nozzle is used to generate air flow with different velocities and temperatures while the drying process is recorded and analyzed using an infrared thermograph. In addition, a uniform padder is adapted to ensure the fabric&#39;s moisture uniformity. Impinging drying is studied under different initial conditions by changing the air flow temperature, velocity, and direction, then the applicability and suitability of the protocol are evaluated.

Presented here is a protocol that guarantees uniform distribution of initial moisture inside of a fabric and investigates the effects of hot-air thermodynamic parameters (velocity, temperature, and direction) and thickness on the fabric&#39;s drying characteristics (e.g., temperature variation) under the condition of air impingement.

Impinging dryness is now a widely used and effective way for fabric drying due to its high heat and mass transfer coefficient. Previous studies on fabric ...

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Traditional U-turn designs can improve operational features obviously, while U-turn diversions and merge segments still cause traffic congestion, conflicts, and delays. An exclusive spur dike U-turn lane design (ESUL) is proposed here to solve the disadvantages of traditional U-turn designs. To evaluate the operation performance of ESUL, a traffic simulation protocol is needed. The whole simulation process includes five steps: data collection, data analysis, simulation model build, simulation calibration, and sensitive analysis. Data collection and simulation model build are two critical steps and are described later in greater detail. Three indexes (travel time, delay, and number of stops) are commonly used in the evaluation, and other parameters can be measured from the simulation according to experimental needs. The results show that the ESUL significantly diminishes the disadvantages of traditional U-turn designs. The simulation can be applied to solve microscopic traffic problems, such as in single or several adjacent intersections or short segments. This method is not suitable for larger scale road networks or evaluations without data collection.

This protocol describes the process of solving a microscopic traffic problem with simulation. The whole process contains a detailed description of data collection, data analysis, simulation model build, simulation calibration, and sensitive analysis. Modifications and troubleshooting of the method are also discussed.