Name: evaluation of commercial-off-the-shelf wrist wearables to estimate stress on students
Uploaded: 2018-06-16T13:30:00
Description: Watch this Scientific Journal Video about Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students at JoVE.com

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...

<ol>
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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. ...

Wearable devices have become very popular. Among them, the most used are wrist wearables such as smart bands or smart watches. These devices are frequently used by sports people as activity trackers for monitoring and tracking fitness-related metrics.Sensors available in these devices allow to collect data such as users to your location, movement patterns, steps taken, or heart rate, among others. This data can be processed using data analytics techniques to estimate the value of variables such as distance walked, calories consumed, maximal aerobic capacity, or sleep quality. These devices are starting to be introduced in other domains, such as health or education.In our case, we decided to explore the possibility of using commercial wrist wearers in an academic context. More specifically, our work focuses on estimating the stressing of students using these devices. For this, we measure variations in physiological signs, such as heart rate, skin temperature, or sweating, during the performance of different training activities.Then, material learning techniques are applied to these signs to estimate stress levels. Commercial off-the-shelf wrist wearable devices have sensors that provide information on physiological signals that have been widely used by the scientific community in stress assessment and detection. Among these signals, heart rate, respiratory rate, skin temperature, or galvanic skin response are the most common.To carry out this study, an evaluation protocol has been defined, in which the following tools are used. A commercial wrist wearable that is provided to the student to capture physiological signals, a smartphone running an application developed to collect data and submit it to an analysis server, a dashboard that facilitates the visualization of collected data, and finally, the data analytics package that supports the execution of machine learning algorithms. The protocol that will help to stimulate the stress is divided into two phases.First, a calibration process is carried out in a laboratory, according to fully-controlled conditions. For this, different levels of stress are induced in the student by means of normalized tests, which are the Stroop color and word test, the Paced Auditory Serial Addition Test, and a hyperventilation test. Changes in biological signals are analyzed in these controlled conditions, to be used as a reference for the next phase.The first task is to create a baseline for a stress analysis. In this task, the student visualizes a four-minute relaxing video, in which different shots of a sunset and bridge and so on. Then the student will be questioned about their perceived stress level.The second task is an adaption of the Stroop color and word test. In our case, this test is divided into three different levels of difficulty. For this task, the user must choose the color in which the name of a color is painted.Several buttons located at the bottom of the screen containing the initial letter of a color are available fore the user to choose the color. For example, the button that refers to blue, that picks letter B.Difficulty increases for each level. To increase the student's frustration and increase their stress level, every time an error is committed, an annoying beep will sound.And if two errors are made, the correct color count will be reset. For the last, most difficult level, name and color will not match. Once each of the three levels is completed, the student will be questioned about their perceived stress level.The third task consists on the Paced Auditory Serial Addition Test, which measures how the student experiences concentration. 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 onscreen box, before listening to the next number. Again, if three errors are committed, the score will be reset.The student will be questioned about their perceived stress level upon completion of the task. The fourth and the last test consists on a hyperventilation activity to induce the same variation in the physiological signals that would provoke a stressful situation. The student will be inquired about their perceived stress level.The second phase takes part in the academic room. The objective in this case is to collect the physiological signals of the students while they are performing educational activities. For example, changes in the physiological signals are measured when the teacher asks a question or when the student is completing an answer.Connect your smartphone and tablet device to a stable internet connection. Turn on Bluetooth communications in the smartphone. On the smartphone and tablet, download and install the official wrist wearable applications for physiological signals and stress test applications.Turn on the wrist wearable device and place the wearable. In the smart phone, open the official wrist wearable application. The application will proceed to synchronize the wearable device with the smartphone.For some devices, an email address is required. In the smartphone, open the physiological signals application. Wait for the physiological signals app to display the word Weared"in green.This indicates that the wearable device has been detected, and therefore the transmission of information from the sensors to the smartphone is enabled. Choose a comfortable and non-disturbing room. Turn on the wrist wearable device.Place it around the subject's non-dominant wrist, and place the headphones on the head of the subject. Fit tightly but comfortably, the wearable around the wrist. In the smartphone, launch the physiological signals application.Select the change user option in the left configuration menu, and provide the ID of the subject who will complete the test, and click save. In a laptop, access the dashboard and enter the test administrator's ID and password. Select the subject's ID and select the subject's stress step.Check the physiological signal's evolution, and wait for the wearable device to reach thermal stability to start the experiment. On the tablet, launch the stress test application. Explain to the subject the four laboratory tests.Show some of the screens and actions to perform during each of the tasks. This is very important, because the subject shall feel stressed or relaxed in accordance with the performed activities, and not because they experience fear or concern about what is going to occur. Enter the same user ID as in the physiological signals app, and click the arrow.Launch the video task and give full control to the student. When the task is finished, check that the subject provides the perceived stress. Launch the Stroop color task consecutively for levels one, two, and three.Only for level three, in case the subject does not solve it after four minutes, terminate the task by pressing the arrow located at the top of the screen. Launch the PASAT test. In case the subject does not solve the test after four minutes, terminate the task by pressing the arrow located at the top of the screen.Launch the hyperventilation test. Observe the evolution of heart rate, using the dashboard. If the physiological signals do not change significantly, increase inspiration and expiration rates gradually.In case the subject feels dizziness or uncomfortable, halt the task. In any case, complete the task after four minutes. Turn on the wrist wearable device and place the wearable.Launch the physiological signals application and choose the correct subject ID.in a laptop, access the dashboard and enter the test administrator's ID and password. Select the subject ID and select the subject's stress stop. Check the evolution of physiological signals.Take notations about any relevant event occurring in the classroom in relation to student-teacher interaction. Relevant information and basic events will be used to label physiological signal samples afterwards. Example events are a question from the teacher to the student or a theoretical explanation.At the end of the lecture, ask the subject to complete the questionnaire about their level of stress at the specific times during the session, according to a five level scale. In a laptop, access the dashboard and enter the test administrator's ID and password. Select the subject ID and select the subject's stress step.Select the day of the classroom experiment. Begin to file lecture room activities and their duration according to the starting and finishing times and their types. For each activity, select a perceived stress level.For each subject and each session, download the file with the text samples. A comma-separated value file is created for each student. It shows reflecting the values of the physiological signals with their standard deviation, slope, and difference with higher values, the activity type, the activity-based stress, and the subject's perceived stress.Launch the data analytics package. Choose a set of classifiers. For example, support vector that changed, and import the comma-separated value file for all students for each session.Train and evaluate a set of classifiers, using the 10-fold cross-validation technique. Finally, check the results of accuracy and error rates. The protocol discussed so far has been validated in a real-world scenario, using a course in computer architectures.Preliminary results show strong variations in physiological signal values captured in the laboratory, from relaxed video watching to the hyperventilation test. These variations are a consequence of the evolution of the students in the study from the relaxed status to a stressed one. In this context, machine learning classification provides success rates about 90%Success rate worsens when data is captured in the lecture room.Nevertheless, relevant differences are found when the student is subjected to particularly stressful situations, such as pop quizzes or finals. It is well known that stress may have a most relevant influence in the development of academic activities and overall students'performance. Higher stress levels are especially relevant during the freshman year, with estimated dropout rates between 20%and 30%The proposed protocol is intended to serve as an instrument to eventually define richer student models than those presently used in learning management systems or student information systems.We think that the student model, enhanced with information of their mental or physical states, will pave the way for new added-value applications and services to improve academic performance. For example, the new information captured with the most suitable wearable device according the protocol discussed, could be applied to the early detection of situations affecting performance, such as fatigue, lack of attention, or stress. And to guide students to overcome those situations.

evaluation-commercial-off-shelf-wrist-wearables-to-estimate-stress-on

Research

JoVE Journal

Engineering

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 ...

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 ...

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

This protocol presents a method for manufacturing, control, and evaluation of the performance of a soft robot that can climb inclined flat surfaces with slopes of up to 84&#176;. The manufacturing method is valid for the fast pneunet bending actuators in general and might, therefore, be interesting for newcomers to the field of actuator manufacturing. The control of the robot is achieved by means of a pneumatic control box that can provide arbitrary pressures and can be built by only using purchased components, a laser cutter, and a soldering iron. For the walking performance of the robot, the pressure-angle calibration plays a crucial role. Therefore, a semi-automated method for the pressure-angle calibration is presented. At high inclines (&#62; 70&#176;), the robot can no longer reliably fix itself to the walking plane. Therefore, the gait pattern is modified to ensure that the feet can be fixed on the walking plane.

This protocol provides a detailed list of steps to be performed for the manufacturing, control and evaluation of the climbing performance of a gecko-inspired soft robot.

This protocol presents a method for manufacturing, control, and evaluation of the performance of a soft robot that can climb inclined flat surfaces with ...

Evaluation of the Curing of Adhesive Systems by Rheological and Thermal Testing

The analysis of thermal processes associated to the curing of adhesives and the study of mechanical behavior once cured, provide key information to choose the best option for any specific application. The proposed methodology for the curing characterization, based on thermal analysis and rheology, is described through the comparison of three commercial adhesives. The experimental techniques used here are Thermogravimetric Analysis (TGA), Differential Scanning Calorimetry (DSC) and Rheology. TGA provides information about the thermal stability and filler content, DSC allows the evaluation of some thermal events associated to the cure reaction and to thermal changes of the cured material when subjected to temperature changes. Rheology complements the information of the thermal transformations from a mechanical point of view. Thus, the curing reaction can be tracked through the elastic modulus (mainly the storage modulus), the phase angle and the gap. In addition, it is also shown that although DSC is of no use to study the curing of moisture curable adhesives, it is a very convenient method to evaluate the low temperature glass transition of amorphous systems.

An experimental methodology based on thermal and rheological measurements is proposed to characterize the curing process of adhesives with to obtain useful information for industrial adhesive selection.

The analysis of thermal processes associated to the curing of adhesives and the study of mechanical behavior once cured, provide key information to choose ...

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

In this study, methods for the mechanoluminescent (ML) visualization of crack propagation and mechanical behavior to evaluate adhesive joints are demonstrated and explained. The first step involved sample preparation; an air spray was used to apply ML paint to the surface of the adhesive joint specimens. The performance of the ML sensor was described to examine the measurement conditions. The results of ML sensing during a double cantilever beam (DCB) test and a lap-shear (LS) test are demonstrated as these are the most frequently and widely used methods for evaluating adhesives. Originally, it was difficult to directly quantify the crack tip and strain/stress distribution and concentration because the crack tip was too small, and the effects of the strain could not be observed. The mechanoluminescence, crack propagation, and mechanical behavior during mechanical testing can be visualized via the ML pattern during the adhesive evaluation. This allows for the recognition of the precise position of the crack tips and other mechanical behaviors related to structural failure.

In this study, a protocol is presented that describes the use of mechanoluminescent (ML) visualization for monitoring crack propagation and mechanical behavior during adhesive joint evaluation testing.