This research is supported by the Tsinghua University Initiative Scientific Research Program (Ergonomic design of curved keyboard on smart devices). The authors appreciate Tianyu Liu for his kind suggestions and coding assistance on figures.

The study was conducted in accordance with the ethical principle and was approved by the Ethics Committee of Tsinghua University. Figure 1 shows the process of evaluating the keyboard design of smartphones.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61796/61796fig01.jpg" /> 
Figure 1: General process of conducting a keyboard experiment and evaluating the keyboard design. <a href="https://www.jove.com/files/ftp_upload/61796/61796fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
1. Preparation
<ol>
	<li>Experiment design
	<ol>
		<li>Define the research issue and propose the hypothesis.</li>
		<li>Design the experiment according to the hypothesis and define the independent variables (e.g., keyboard layout, typing posture). Use the within-subject design in order to reduce confounding factors and variance caused by the difference among participants.</li>
	</ol>
	</li>
	<li>Dependent variables
	<ol>
		<li>Use physical data, including the hand length, the length of input finger, and the circumference of input finger, which were measured by a tape measure, as shown in Figure 2.</li>
		<li>Use physiological data, including galvanic skin response (measured by the portable wireless physiological detector), heart rate (measured by the portable wireless physiological detector), electromyographic activity (measured by surface electromyography), etc.</li>
		<li>Use input performance: word per minute, word error rate, and transition time between two keys.
		<ol>
			<li>Word per minute refers to the input speed of participants (i.e., the number of correct-inputted words per minute).</li>
			<li>Word error rate refers to the input accuracy of participants (i.e., the number of incorrect-inputted words divided by the total number of words under one condition). Corrected error rate, uncorrected error rate, and total error rate have also been used in previous studies36.</li>
			<li>Transition time between two keys refers to the reaction time of participants between two touchpoints of a correct-inputted word22 (i.e., the start time of the second touchpoint minus the departure time of the first character).</li>
		</ol>
		</li>
		<li>Use body-movement data such as hand gesture and body (finger) movement. They could be collected by the motion capture system35.</li>
		<li>Use subjective data such as perceived usability, intent to use, perceived accuracy and speed, perceived exertion and pain, and subjective workload, etc. Subjective data can be obtained through existing scales and questionnaires, which are highly reliable as well as valid to better evaluate the subjective feedback of participants about the keyboard design.
		<ol>
			<li>Use NASA-TLX, a 21-point scale that is used to measure subjective workload through mental, physical, time, performance, effort, and frustration dimensions. A high score indicates a high subjective workload26.</li>
			<li>Use the System Usability Scale, a 5-point questionnaire with 10 items, and the responses of one participant will be calculated as a single score from 0 to 100. A high score indicates a high perceived usability24.</li>
			<li>Use the Borg CR10 Scale, which is ranged from 0 to 10 to measure perceived pain and exertion. A high score indicates a high-level perceived pain and exertion25.</li>
			<li>Use the Intent to Use Scale: a 10-point questionnaire that is used to measure the likelihood that participants would use the technology or products. A high score indicates a high-level likelihood28.</li>
			<li>Perceived speed and perceived accuracy are all measured by 50-point scales, and a high score indicates a good perceived performance28.</li>
		</ol>
		</li>
		<li>Collect the coordinate data of each touchpoint and change it into the fitted ellipse (95% CI) of touchpoints on each button30,31. Adopt the area of each fitted ellipse and the offset from the center of the fitted ellipse to the target center of each button as dependent variables. 
		NOTE: The coordinate data can be precisely collected by the self-developed application on the smartphone. If it is hard to obtain the coordinate data, objective and subjective data are sufficient to roughly evaluate the keyboard design.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61796/61796fig2v4.jpg" /> 
Figure 2: The measurement of the hand. <a href="https://www.jove.com/files/ftp_upload/61796/61796fig2v4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="3">
	<li>Materials
	<ol>
		<li>Choose the experiment smartphone. Take weight, resolution, and screen size into consideration.</li>
		<li>Design and develop the experiment software on smartphones (optional step). 
		NOTE: Transition time between two keys can be recorded automatically by this software or motion capture sensors (i.e., the accelerometer sensor). It may be difficult to collect it manually (e.g., a clock or stopwatch).</li>
		<li>Select the input task from the following suggestions based on the hypothesis and revise it to match the research purpose.
		<ol>
			<li>For the character pair input task, randomly pair 26 English letters into 676 pairs and averagely divide them into several groups based on the experiment design.</li>
			<li>For the phrase (sentence) input task, use phrases that are moderate in length, easy to remember, and representative of the target language. If the target language is English, extract 15&#8211;20 (or based on research purpose) phrases or words from a 500 phrases set37.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Participant recruitment
	<ol>
		<li>Use the G*Power software to calculate the sample size.</li>
		<li>Post questionnaires to recruit potential participants.</li>
		<li>Filter potential participants with wanted characteristics, e.g., age, health, vision, handedness, and input experience. Ensure that the input experience of participants is balanced.</li>
	</ol>
	</li>
</ol>
2. Procedure
<ol>
	<li>Read out the informed consent form of the experiment to participants, including the experiment procedure, task, and whether they will encounter any mental or physical injuries. If participants agree to participate, they need to sign the informed consent form. If not, they can immediately withdraw. According to the informed consent form, participants can also withdraw at any stage of the experiment.</li>
	<li>Collect physical as well as demographic data. Use a tape measure to measure the hand of every single participant (Figure 2) in order to eliminate the effect of the hand size difference and also provide repeatable data for future research. Collect demographic data such as age, gender, precise input experience, and occupation.</li>
	<li>Disinfect all devices and clean the body parts of the participant that will touch the devices.
	<ol>
		<li>Ask participants to wash their hands and clean the screen of smartphones so that sensors of smartphones can be more sensitive.</li>
		<li>Ask participants to wear portable wireless physiological detectors or a motion capture system. Ask participants to wear the portable wireless physiological detection wristband on the non-dominant hand to record galvanic skin response and heart rate with the noise interference avoided.
		<ol>
			<li>Place passive markers of the motion capture system on the fingernails, the proximal phalanx of the finger, cervical vertebrae (C3&#8211;C5), and arm, to collect the precise body and finger movement. Stick wireless electrodes to the skin of two arms and two forearms to detect the electromyographic activity (optional step).</li>
		</ol>
		</li>
		<li>Calibrate all the devices used in the experiment.</li>
	</ol>
	</li>
	<li>Practice part
	<ol>
		<li>Let participants complete the training task. The training task is used to improve participants&#39; familiarity with input tasks and keyboards to reduce the effect of practice or the unfamiliarity on the experiment result. It is composed of 50 pairs or 20 words randomly selected from the 676 English pairs set or 500 phrases set. Only when their input accuracy reaches 80% or more in 150 seconds can they enter the formal trials. The exemplar research adopted inputting 50 pairs as the training task.</li>
	</ol>
	</li>
	<li>Main task
	<ol>
		<li>Let participants complete formal trials under all experimental conditions. They need to ensure their accuracy as quickly as possible during the time of the input task. Formal trials are real input tasks that will be evaluated and analyzed in the research. Each pair, word, or sentence represents a trial, and different experimental designs produce different experimental conditions.</li>
		<li>Have participants complete the input task in random order or a balanced order. Methods of the division of input materials are as follows. First, 676 pairs can be randomly divided into each experimental condition (i.e., participants have entered all pairs when they complete all experimental conditions). Second, under each experimental condition, 676 pairs can be divided into several blocks randomly, and participants need to complete these blocks randomly. Third, for inputting words, participants need to complete around 20 trials under each condition. Fourth, for inputting sentences, participants need to complete about 10&#8211;15 trials under each condition. Researchers should ensure no significant difference between the number of characters and the number of words entered by the participant under each condition. The exemplar research adopted the first method and had four experimental conditions.</li>
		<li>After each condition, ask participants to complete all the questionnaires (scales assessing their subjective experience) at random and give them 1 min or more to rest.</li>
	</ol>
	</li>
	<li>At the end of the experiment, let each participant finish the comprehensive questionnaire (Q &#38; A) to obtain subjective feedback.</li>
	<li>Express appreciation to participants with monetary or material rewards.</li>
</ol>
3. Data analysis
<ol>
	<li>Hypothesis testing by appropriate parametric or non-parametric tests
	<ol>
		<li>Analyze the physical, physiological, and body-movement data to test whether the difference between participants would significantly influence the results and inexpressive input experience of users (optional step).</li>
		<li>Analyze the input performance of participants to test the input efficiency on the keyboard.</li>
		<li>Analyze subjective data to test the perceived usability and subjective feedback of the keyboard.</li>
		<li>Figure out whether the practice effect and fatigue effect significantly influence the result. For each condition, trials are divided into two parts according to the timestamp (i.e., the first half part and the second half part). Specifically, under each condition, examine the difference of input performance between the first half part and the second half part to test whether the practice effect or fatigue effect exist.</li>
		<li>Analyze the area of the fitted ellipse of touchpoints on each button as well as the offset from its center to the target center of each button (optional step).
		<ol>
			<li>Collect all the touchpoints of each button with the software, and they roughly accord with the bivariate Gaussian distribution. The 95% confidence interval of each button in both x- and y-directions is derived through the coordinate data of each touchpoint in pixel, and the 95% confidence ellipses over a 1:1 outline of the button for each keyboard is fitted through Python scripts on pixel coordinate (see Coding File 2).</li>
			<li>Use fitted ellipses (95% CI) and their areas to demonstrate the dispersion of touchpoints on each button. In each button, the offset of fitted ellipse calculated by Python scripts is defined as the center point of the fitted ellipse to the target point of the button, and it could be represented from x- and y-directions (i.e., in X-axis and Y-axis, see Coding File 3).</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Modeling and simulation
	<ol>
		<li>Use the data-driven model as a function of keyboard location and orientation to predict the finger movement by Python scripts. All movements of fingers are divided into eight directions38 (the top to the bottom, the bottom to the top, the left to the right, the right to the left, the left-top to the right-bottom, the right-bottom to the left-top, the left-bottom to the right-top, the right-top to the left-bottom). For each direction, the average transition time between two keys is calculated to represent the effectiveness of finger movement, which is used to evaluate the keyboard design (optional step).</li>
		<li>Use linear regression analysis to build an enhanced Fitts&#39; Law (or its extended version, FFitts&#8217; Law) model to predict the transition time between two keys using an integrated cognitive architecture39 by Python scripts. The enhanced Fitts&#39; Law model could provide a better prediction and evaluation on keyboard design based on its analyses on the location and effective width of keys, as well as the distance of two keys (optional step).</li>
	</ol>
	</li>
</ol>

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T., Federici, 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+data-driven+design+evaluation+tool+for+handheld+device+soft+keyboards.">A data-driven design evaluation tool for handheld device soft keyboards.</a> Plos One. 9 (9), 107070 (2014).</li><li>Cao, S., Ho, A., He, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+and+predicting+mobile+phone+touchscreen+transcription+typing+using+an+integrated+cognitive+architecture.">Modeling and predicting mobile phone touchscreen transcription typing using an integrated cognitive architecture.</a> International Journal of Human-Computer Interaction. 34 (4-6), 544-556 (2018).</li></ol>

The authors declared no financial disclosure or conflicts of interest.

In this study, based on the development of screen technology, we presented a summarized and general protocol of keyboard design evaluation to assess the keyboard design systematically and precisely. Existing indicators and methods from previous studies, pairs matched by English characters, and transition time between two keys are integrated and modified to generate an effective protocol.
Several critical points need to be noticed in this protocol. The selection of variables and indicators is e...

An erratum was issued for:&#160;<a href="https://www.jove.com/t/61796">An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones</a>. The Authors section was&#160;updated.
Yincheng Wang1 
Ke Wang1 
Yuqi Huang1 
Di Wu2 
Jian Wu3 
Jibo He4,1 
1Department of Psychology, School of Social Sciences, Tsinghua University 
2Department of Computer Science, Beijing Normal University 
3Haier Innovation Design Center, Haier Company 
4Key Laboratory of Emotion and Mental Health in Chongqing, User Experience and Human-computer Interaction Technology Institute, Chongqing University of Arts and Sciences
to:
Yincheng Wang1 
Ke Wang1 
Yuqi Huang1 
Di Wu2 
Jian Wu3 
Jibo He1 
1Department of Psychology, School of Social Sciences, Tsinghua University 
2Department of Computer Science, Beijing Normal University 
3Haier Innovation Design Center, Haier Company

An erratum was issued for:&#160;<a href="https://www.jove.com/t/61796">An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones</a>. The Authors section was&#160;updated.

Erratum: An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

Keyboard input is the mainstream method of the human-smartphone interaction1,2, and with the penetration of smartphones, keyboard input gets billions of users. In 2019, the global smartphone penetration rate had reached 41.5%3, while the United States, with the highest penetration, had come up to 79.1%4. Up to the first quarter of 2020, the Sogou mobile keyboard had about 480 million daily active users5. Up to May 6, 2020, the Google Gboard had been downloaded more than 1 billion times6.
Unsatisfactory keyboard input experience increases with the enlargement of the phone screen. Although the enlarged screen aimed to improve the viewing experience, it has changed the gravity, size, and weight of smartphones, causing users to change holding posture repeatedly to reach remote areas (e.g., button A and Q for right-handed users), thus leading to input inefficiency. The stretch of muscle may cause users to suffer from musculoskeletal disorders, hand pains, and different types of disease (e.g., carpal tunnel syndrome, thumb osteoarthritis, and thumb tenosynovitis7,8,9,10). Users who prefer one-handed usage are under worse conditions11,12.
Therefore, the evaluation and optimization of keyboard design have become hot topics of psychological, technical, and ergonomic research. Variable keyboard designs and concepts have constantly been proposed by input method editor (IME) companies and researchers to optimize input experience and efficiency, including layout-changed and character-reordered keyboards: Microsoft WordFlow Keyboard13, Functional Button Area in Glory of Kings14, IJQWERTY15, and Quasi-QWERTY16.
Existing evaluation methods of keyboard design vary from researcher to researcher except for several highly accepted indicators, and more accurate indicators are proposed. However, with a variety of indicators, there is not a summarized and systematic protocol provided to demonstrate the process of evaluating and analyzing the keyboard design. Fitts&#8217; Law17 and its extended version FFitts&#160;Law18, which described human-computer interaction, were widely adopted to evaluate keyboard performance19,20,21,22. Moreover, the functional area of the thumb was proposed to improve keyboard design, and it described a curved motion area for the thumb to comfortably complete the input task23. Based on these theories, indicators including word per minute, word error rate, and subjective feedback (perceived usability, perceived performance, perceived speed, subjective workload, perceived exertion and pain, and intent to use, etc.), which were highly adopted, were partially used in previous studies24,25,26,27,28,29 except for modeling and simulation methods. In addition, the fitted ellipse of touchpoints on each button and its offset30,31 were used in recent years to investigate the accurate performance of inputting events. Also, the galvanic skin response, heart rate, electromyographic activity, hand gesture, and body movement32,33,34,35 were adopted to directly or indirectly evaluate muscle fatigue, comfort, and satisfaction of the users. However, these various methods lack reflection on the appropriateness of the indicators used, and a novice researcher may be confused to select the appropriate indicators for his or her research.
The research about keyboard design is also easy to be conducted, operated, and analyzed. With the boom of screen technology, more behavioral data could be easily collected to evaluate the keyboard design in-depth (e.g., the transition time between two keys and the coordinate data of each touchpoint). Based on the mentioned data, researchers could precisely explore the details of keyboard design and analyze its disadvantages and advantages. When compared with other human-computer interaction research, the research of keyboard design on portable smartphones also has high application value for its vast user base with no expensive equipment, complicated materials, or huge laboratory space needed. The questionnaires, scales, and Python script about the research are open-source and easy to access.
The purpose of this research is to summarize the previous methods to demonstrate a systematic, precise, and general protocol to evaluate and analyze the keyboard design on smartphones. The exemplar experiment and results aim to show whether the curved QWERTY keyboard with size-adjustable buttons could optimize the input experience of one-handed input on a 5-inch smartphone when compared with traditional QWERTY keyboard and share the visualization method and Python script of data analysis.

<table><tbody><tr><td>Changxiang 6S smartphone</td><td>Huawei</td><td></td><td>Smartphone used in the examplar study</td></tr><tr><td>Curved QWERTY keyboard software</td><td>Tsinghua University</td><td></td><td>Developed by authors</td></tr><tr><td>SPSS software</td><td>IBM</td><td></td><td>Data analysis software</td></tr><tr><td>G*Power software</td><td>Heinrich-Heine-Universit&amp;auml;t D&amp;uuml;sseldorf</td><td></td><td>Sample size calculation</td></tr><tr><td>E4 portable wireless wristband</td><td>Empatica</td><td></td><td>Recording galvanic skin response and heart rate</td></tr><tr><td>Arqus</td><td>Qualysis</td><td></td><td>Motion capture camera platform</td></tr><tr><td>Passive marker</td><td>Qualysis</td><td></td><td>Appropriate sizes: 2.5 mm, 4 mm, and 6.5 mm</td></tr><tr><td>Trigno sEMG</td><td>Delsys</td><td></td><td>Recording electromyographic activity</td></tr><tr><td>Visual Studio Code</td><td>Microsoft</td><td></td><td>Python editor</td></tr></tbody></table>

an assessment method and toolkit to evaluate keyboard design on smartphones

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

The representative study is mainly following the mentioned protocol. The study adopts a 2 (Keyboard layout: Curved QWERTY vs. Traditional QWERTY) &#215; 2 (Button size: large, 6.3 mm &#215; 9 mm vs. small, 4.9 mm &#215; 7 mm) within-subject design to evaluate whether the curved QWERTY could improve the input efficiency and comfort when compared with the traditional QWERTY in different sizes of buttons by the character pair input task through our self-developed software (Figure 3). This study...

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An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

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

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

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Research

JoVE Journal

Behavior

3.1K Views.  Tsinghua University. The presented protocol integrates various evaluation methods and demonstrates a method to evaluate the keyboard design on smartphones. Pairs matched by English characters are proposed as the input material, and the transition time between two keys is used as the dependent variable.

Video: An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

Practical Methodology of Cognitive Tasks Within a Navigational Assessment

This paper describes an approach for measuring navigation accuracy relative to cognitive skills. The methodology behind the assessment will thus be clearly outlined in a step-by-step manner. Navigational skills are important when trying to find symbols within a speech-generating device (SGD) that has a dynamic screen and taxonomical organization. The following skills have been found to impact children&#8217;s ability to find symbols when navigating within the levels of an SGD: sustained attention, categorization, cognitive flexibility, and fluid reasoning1,2. According to past studies, working memory was not correlated with navigation1,2.
The materials needed for this method include a computerized tablet, an augmentative and alternative communication application, a booklet of symbols, and the Leiter International Performance Scale-Revised (Leiter-R)3. This method has been used in two previous studies. Robillard, Mayer-Crittenden, Roy-Charland, Minor-Corriveau and B&#233;langer1 assessed typically developing children, while Rondeau, Robillard and Roy-Charland2 assessed children and adolescents with a diagnosis of Autism Spectrum Disorder. The direct observation of this method will facilitate the replication of this study for researchers. It will also help clinicians that work with children who have complex communication needs to determine the children&#8217;s ability to navigate an SGD with taxonomical categorization.

Children who have complex communication needs can benefit from the use of a speech-generating device (SGD). Cognitive skills were identified as having an impact on the ability to navigate between levels of SGDs. This protocol describes the steps needed for Speech-language Pathologists to assess cognitive skills and navigational abilities.

This paper describes an approach for measuring navigation accuracy relative to cognitive skills. The methodology behind the assessment will thus be ...

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

Progressive and irreversible muscle atrophy characterizes Spinal Muscular Atrophy (SMA) and other similar muscle disorder diseases. Objective assessment of muscle functions is an essential and important, although challenging, prerequisite for successful clinical trials. Current clinical rating scales restrain the movement abnormalities to certain predefined coarse-grained individual items. The Kinect 3-D sensor has emerged as a low-cost and portable motion sensing technology used to capture and track people&#39;s movement in many medical and research fields. A novel approach using this 3-D sensor was developed and a game-like test was designed to objectively measure the upper limb function of patients with SMA. The prototype test targeted joint movement capability. While sitting in a virtual scene, the patient was instructed to extend, flex, and lift the whole arm in order to reach and place some objects. Both kinematic and spatiotemporal characteristics of upper limb movement were extracted and analyzed, e.g., elbow extension and flexion angles, hand velocity, and acceleration. The first study included a small cohort of 18 ambulant SMA patients and 19 age- and gender-matched healthy controls. A comprehensive analysis of arm movement was achieved; however, no significant difference between the groups were found due to the mismatch of patient&#39;s capability and the test difficulty. Based on this experience, a second version of the test consisting of a modified version of the first game with increased difficulties and a second game targeting muscle endurance were designed and implemented. The new test has not been conducted in any patient groups yet. Our work has demonstrated the potential capability of the 3-D sensor in assessing such muscle function and suggested an objective approach to complement the clinical rating scales.

An objective measure of muscle functions is challenging especially in children. Based on a commercially available digital 3-D sensor, a child-friendly gaming test was developed to assess upper limb function for clinical trials.

Progressive and irreversible muscle atrophy characterizes Spinal Muscular Atrophy (SMA) and other similar muscle disorder diseases. Objective assessment ...

Measuring the Switch Cost of Smartphone Use While Walking

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

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

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

The Multiple Sclerosis Performance Test (MSPT): An iPad-Based Disability Assessment Tool

Precise measurement of neurological and neuropsychological impairment and disability in multiple sclerosis is challenging. We report a new test, the Multiple Sclerosis Performance Test (MSPT), which represents a new approach to quantifying MS related disability. The MSPT takes advantage of advances in computer technology, information technology, biomechanics, and clinical measurement science. The resulting MSPT represents a computer-based platform for precise, valid measurement of MS severity. Based on, but extending the Multiple Sclerosis Functional Composite (MSFC), the MSPT provides precise, quantitative data on walking speed, balance, manual dexterity, visual function, and cognitive processing speed. The MSPT was tested by 51 MS patients and 49 healthy controls (HC). MSPT scores were highly reproducible, correlated strongly with technician-administered test scores, discriminated MS from HC and severe from mild MS, and correlated with patient reported outcomes. Measures of reliability, sensitivity, and clinical meaning for MSPT scores were favorable compared with technician-based testing. The MSPT is a potentially transformative approach for collecting MS disability outcome data for patient care and research. Because the testing is computer-based, test performance can be analyzed in traditional or novel ways and data can be directly entered into research or clinical databases. The MSPT could be widely disseminated to clinicians in practice settings who are not connected to clinical trial performance sites or who are practicing in rural settings, drastically improving access to clinical trials for clinicians and patients. The MSPT could be adapted to out of clinic settings, like the patient&#8217;s home, thereby providing more meaningful real world data. The MSPT represents a new paradigm for neuroperformance testing. This method could have the same transformative effect on clinical care and research in MS as standardized computer-adapted testing has had in the education field, with clear potential to accelerate progress in clinical care and research.

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool

Precise measurement of neurological and neuropsychological impairment and disability in multiple sclerosis is challenging. We report methodologic details on a new test, the Multiple Sclerosis Performance Test (MSPT). This new approach to the objective of quantification of MS related disability provides a computer-based platform for precise, valid measurement of MS severity.

Precise measurement of neurological and neuropsychological impairment and disability in multiple sclerosis is challenging. We report a new test, the ...

Medicine

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Collision warning system plays a key role in the prevention of driving distractions and drowsy driving. Previous studies have proven the advantages of tactile warnings in reducing driver&#8217;s brake response time. At the same time, tactile warnings have been proved effective in take-over request (TOR) for partially autonomous vehicles.
How the performance of tactile warnings can be optimized is an ongoing hot research topic in this field. Thus, the presented low-cost driving simulation software and methods are introduced to attract more researchers to take part in the investigation. The presented protocol has been divided into five sections: 1) participants, 2) driving simulation software configuration, 3) driving simulator preparation, 4) vibrating toolkit configuration and preparation, and 5) conducting the experiment.
In the exemplar study, participants wore the tactile vibrating toolkit and performed an established car-following task using the customized driving simulation software. The front vehicle braked intermittently, and vibrating warnings were delivered whenever the front vehicle was braking. Participants were instructed to respond as quickly as possible to the sudden brakes of the front vehicle. Driving dynamics, such as the brake response time and brake response rate, were recorded by the simulation software for data analysis.
The presented protocol offers insight into the exploration of the effectiveness of tactile warnings on different body locations. In addition to the car-following task that is demonstrated in the exemplar experiment, this protocol also provides options&#160;to apply other paradigms to the driving simulation studies by making simple software configuration without any code development. However, it is important to note that due to its affordable price, the driving simulation software and hardware introduced here may not be able to fully compete with other high-fidelity commercial driving simulators. Nevertheless, this protocol can act as an affordable and user-friendly alternative to the general high-fidelity commercial driving simulators.

This protocol describes a driving simulation platform and a tactile vibrating toolkit for the investigation of driving-related research. An exemplar experiment exploring the effectiveness of tactile warnings is also presented.&#160;

Collision warning system plays a key role in the prevention of driving distractions and drowsy driving. Previous studies have proven the advantages of ...

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.

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

A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing

Kindergarten writing involves acquiring fundamental skills, such as letter formation, phonemic awareness, and the gradual use of written language to express ideas. In this context, tablet-based curriculum assessments present new opportunities for teaching and evaluating these early writing abilities. To the best of our knowledge, there is currently no tool with these features available for Spanish-speaking children. Therefore, the primary objective of this study was to present a tablet-based protocol for screening at-risk young writers. This tablet-based protocol is specifically designed for kindergarten-level education and serves as a curriculum-based assessment tool that focuses on evaluating early writing skills in young learners. This user-friendly application incorporates interactive tasks and exercises, including assessments of phonological awareness, name writing, alphabet letter copying fluency, and oral narrative skills. These comprehensive evaluations cover various aspects of writing. This application is meticulously aligned with kindergarten curriculum objectives, ensuring that assessments adhere to educational standards. By providing educators with a digital platform to assess and enhance students' writing skills, this tool empowers them to make data-driven decisions for effective instruction during the early stages of writing development. Moreover, it functions as a curriculum-based measurement (CBM), offers valuable support for identifying potential writing challenges in young learners and continuously monitoring their progress. This feature enables early intervention and tailored instruction to optimize writing skill development.

This protocol presents the step-by-step method for using a writing-based, valid, and reliable application designed for kindergarten-level education. It operates as a curriculum-based assessment tool, specifically focusing on evaluating early writing skills in young learners.

Kindergarten writing involves acquiring fundamental skills, such as letter formation, phonemic awareness, and the gradual use of written language to ...

Design to Implementation Study for Development and Patient Validation of Paper-Based Toehold Switch Diagnostics

Access to low-burden molecular diagnostics that can be deployed into the community for testing is increasingly important and has meaningful wider implications for the well-being of societies and economic stability. Recent years have seen several new isothermal diagnostic modalities emerge to meet the need for rapid, low-cost molecular diagnostics. We have contributed to this effort through the development and patient validation of toehold switch-based diagnostics, including diagnostics for the mosquito-borne Zika and chikungunya viruses, which provided performance comparable to gold-standard reverse transcription-quantitative polymerase chain reaction (RT-qPCR) based assays. These diagnostics are inexpensive to develop and manufacture, and they have the potential to provide diagnostic capacity to low-resource environments. Here the protocol provides all the steps necessary for the development of a switch-based assay for Zika virus detection. The article takes readers through the stepwise diagnostic development process. First, genomic sequences of Zika virus serve as inputs for the computational design of candidate switches using open-source software. Next, the assembly of the sensors for empirical screening with synthetic RNA sequences and optimization of diagnostic sensitivity is shown. Once complete, validation is performed with patient samples in parallel with RT-qPCR, and a purpose-built optical reader, PLUM. This work provides a technical roadmap to researchers for the development of low-cost toehold switch-based sensors for applications in human health, agriculture, and environmental monitoring.

Access to decentralized, low-cost, and high-capacity diagnostics that can be deployed into the community for decentralized testing is critical for combating global health crises. This manuscript describes how to build paper-based diagnostics for viral RNA sequences that can be detected with a portable optical reader.

Access to low-burden molecular diagnostics that can be deployed into the community for testing is increasingly important and has meaningful wider ...

Bioengineering

Smartphone-Integrated Dengue NS1 Paper-Based Diagnostic Device

Portable Paper-Based Immunoassay Combined with Smartphone Application for Colorimetric and Quantitative Detection of Dengue NS1 Antigen

Dengue virus (DENV) infection, which is transmitted by Aedes mosquitoes, is a major public health concern in tropical and subtropical countries. With an annual incidence of approximately 10 million cases and 20,000-25,000 deaths, particularly among children, there is an urgent need for practical diagnostic tools. The presence of dengue non-structural protein 1 (NS1) during early infection has been linked to cytokine release, vascular leakage, and endothelial dysfunction, making it a potential marker for severe dengue. 
Paper-based immunoassays such as lateral flow assays (LFAs) and microfluidic paper-based analytical devices (PADs) have gained popularity as diagnostic tests due to their simplicity, rapidity, inexpensiveness, specificity, and ease of interpretation. However, conventional paper-based immunoassays for dengue NS1 detection typically rely on visual inspection, yielding only qualitative results. To address this limitation and enhance sensitivity, we proposed a highly portable NS1 dengue detection assay on a Paper-based Analytical Device (PAD), namely, DEN-NS1-PAD, that integrates a smartphone application as a colorimetric and quantitative reader. The development system enables direct quantification of NS1 concentrations in clinical samples. 
Serum and blood samples obtained from patients were utilized to demonstrate the system prototype performance. The results were obtained immediately and can be employed for clinical assessment, both in well-equipped healthcare facilities and resource-limited settings. This innovative combination of a paper-based immunoassay with a smartphone application offers a promising approach for enhanced detection and quantification of dengue NS1 antigen. By augmenting sensitivity beyond the capabilities of the naked eye, this system holds great potential for improving clinical decision-making in dengue management, particularly in remote or underserved areas.

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection

Addressing urgent dengue diagnostic needs, here we introduce a smartphone app-integrated Dengue NS1 Paper-based Analytical Device (DEN-NS1-PAD) for quantifying Dengue NS1 antigen concentration in clinical serum/blood samples. This innovation enhances dengue management by aiding clinical decision-making in various healthcare settings, even resource-limited ones.

Dengue virus (DENV) infection, which is transmitted by Aedes mosquitoes, is a major public health concern in tropical and subtropical countries. With an ...

Immunology and Infection

Quantitative Assessment of Hand Use in Unilateral Nerve Injury

Block Building Task Identifies Distinct Groups of Left/Right-hand Choice Patterns After Unilateral Peripheral Nerve Injury

Numerous methods exist to assess hand and arm function after upper extremity peripheral nerve injury, but peripheral injuries are often unilateral, and few existing methods are designed to capture the unique consequences of unilateral injury. Unilateral impairment of an upper extremity can lead to increased or decreased use of the dominant hand, and either change may be adaptive or maladaptive depending on the individual patient&#8217;s needs. To identify atypical hand usage (left/right choices), researchers and clinicians need to measure it. However, hand usage is traditionally assessed with self-report surveys, which do not necessarily reflect actual left/right-hand choices. Here, this gap in knowledge is addressed with the Block Building Task (BBT), which provides a rapid, quantitative, inexpensive assessment of left/right-hand choices in an unconstrained environment. In the BBT, participants build abstract shapes with interlocking plastic bricks, with no instructions about hand usage. The primary outcome is the fraction of reaches (i.e., for the initial pickup of each brick) made with each hand. After unilateral peripheral nerve injury, patients fell into three clusters: approximately typical hand use (44%), always use the dominant hand (44%), or never use the dominant hand (13%). Even among patients with an injured dominant hand, atypically elevated use of the dominant hand occurred regularly (36%). Notably, hand usage was not predicted by clinical characteristics, so the BBT provides an objective measurement of left/right-hand choices that are not otherwise predictable from the clinical characteristics of patients with peripheral nerve injury. The BBT protocol will be of interest to researchers or clinicians interested in the assessment of conditions with asymmetric effects on the upper limb.

The block-building task provides a rapid, objective, quantitative measurement of how often individuals choose to use their left versus right hand for reach-to-grasp action. After unilateral peripheral nerve injury, patients often shift to near-total usage of one hand, the direction of which is not predictable from other clinical factors.

Numerous methods exist to assess hand and arm function after upper extremity peripheral nerve injury, but peripheral injuries are often unilateral, and ...