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&#228;t D&#252;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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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

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

The assessment of pain relies mostly on methods that require a person to communicate. However, for people with cognitive and verbal impairments, existing methods are not sufficient as they lack reliability and validity. To approach this problem, recent research focuses on an objective pain assessment facilitated by parameters of responses derived from physiology, and video and audio signals. To develop reliable automated pain recognition systems, efforts have been made in creating multimodal databases in order to analyze pain and detect valid pain patterns. While the results are promising, they only focus on discriminating pain or pain intensities versus no pain. In order to advance this, research should also consider the quality and duration of pain as they provide additional valuable information for more advanced pain management. To complement existing databases and the analysis of pain regarding quality and length, this paper proposes a psychophysiological experiment to elicit, measure, and collect valid pain reactions. Participants are subjected to painful stimuli that differ in intensity (low, medium, and high), duration (5 s / 1 min), and modality (heat / electric pain) while audio, video (e.g., facial expressions, body gestures, facial skin temperature), and physiological signals (e.g., electrocardiogram [ECG], skin conductance level [SCL], facial electromyography [EMG], and EMG of M. trapezius) are being recorded. The study consists of a calibration phase to determine a subject&#8217;s individual pain range (from low to intolerable pain) and a stimulation phase in which pain stimuli, depending on the calibrated range, are applied. The obtained data may allow refining, improving, and evaluating automated recognition systems in terms of an objective pain assessment. For further development of such systems and to investigate pain reactions in more detail, additional pain modalities such as pressure, chemical, or cold pain should be included in future studies. Recorded data of this study will be released as the &#8220;X-ITE Pain Database&#8221;.

This article focuses on the experimental elicitation of pain through heat (thermal) and electrical stimulation while recording physiological, visual, and paralinguistic responses. It aims at collecting valid multimodal data for analyzing pain based on its intensity, quality, and duration.&#160;

The assessment of pain relies mostly on methods that require a person to communicate. However, for people with cognitive and verbal impairments, existing ...

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common MCI tests are time-consuming such that indiscriminate massive screening would not be cost-effective. Here, we describe a protocol that uses machine learning techniques to rapidly select candidates for further screening via a question-based MCI test. This minimizes the number of resources required for screening because only patients who are potentially MCI positive are tested further.
This methodology was applied in an initial MCI research study that formed the starting point for the design of a selective screening decision tree. The initial study collected many demographic and lifestyle variables as well as details about patient medications. The Short Portable Mental Status Questionnaire (SPMSQ) and the Mini-Mental State Examination (MMSE) were used to detect possible cases of MCI. Finally, we used this method to design an efficient process for classifying individuals at risk of MCI. This work also provides insights into lifestyle-related factors associated with MCI that could be leveraged in the prevention and early detection of MCI among elderly populations.

This methodology produces decision trees that target population groups more prone to suffering from mild cognitive impairment and are useful for cost-effective selective screening of the disease.

Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common ...

Using Looming Visual Stimuli to Evaluate Mouse Vision

The visual system in the central nervous system processes diverse visual signals. Although the overall structure has been characterized from the retina through the lateral geniculate nucleus to the visual cortex, the system is complex. Cellular and molecular studies have been conducted to elucidate the mechanisms underpinning visual processing and, by extension, disease mechanisms. These studies may contribute to the development of artificial visual systems. To validate the results of these studies, behavioral vision testing is necessary. Here, we show that the looming stimulation experiment is a reliable mouse vision test that requires a relatively simple setup. The looming experiment was conducted in a large enclosure with a shelter in one corner and a computer monitor located on the ceiling. A CCD camera positioned next to the computer monitor served to observe mouse behavior. A mouse was placed in the enclosure for 10 minutes and allowed to acclimate to and explore the surroundings. Then, the monitor projected a program-derived looming stimulus 10 times. The mouse responded to the stimuli either by freezing or by fleeing to the hiding place. The mouse&#39;s behavior before and after the looming stimuli was recorded, and the video was analyzed using motion tracking software. The velocity of the mouse movement significantly changed after the looming stimuli. In contrast, no reaction was observed in blind mice. Our results demonstrate that the simple looming experiment is a reliable test of mouse vision.

To examine mouse vision, we conducted a looming test. Mice were placed in a large square arena with a monitor on its ceiling. The looming visual stimulus consistently evoked freezing or flight reactions in the mice.

The visual system in the central nervous system processes diverse visual signals. Although the overall structure has been characterized from the retina ...

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Reaching and grasping are highly-coupled movements, and their underlying neural dynamics have been widely studied in the last decade. To distinguish reaching and grasping encodings, it is essential to present different object identities independent of their positions. Presented here is the design of an automatic apparatus that is assembled with a turning table and three-dimensional (3D) translational device to achieve this goal. The turning table switches different objects corresponding to different grip types while the 3D translational device transports the turning table in 3D space. Both are driven independently by motors so that the target position and object are combined arbitrarily. Meanwhile, wrist trajectory and grip types are recorded via the motion capture system and touch sensors, respectively. Furthermore, representative results that demonstrate successfully trained monkey using this system are described. It is expected that this apparatus will facilitate researchers to study kinematics, neural principles, and brain-machine interfaces related to upper limb function.

Presented here is a protocol to build an automatic apparatus that guides a monkey to perform the flexible reach-to-grasp task. The apparatus combines a 3D translational device and turning table to present multiple objects in an arbitrary position in 3D space.

Reaching and grasping are highly-coupled movements, and their underlying neural dynamics have been widely studied in the last decade. To distinguish ...

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

As many scholars and practitioners study personalization and relationship marketing, it is important to provide personalization such as mass customization through marketing technology. The purpose of this study is to examine how to conduct consumer research using an online survey and analysis of data. This study examines consumers&#39;&#160;perceived benefits while customizing a product as well as emotional product attachment, attitudes toward a customization program, and loyalty intentions in the context of online retailing. In addition, this study investigates how consumer responses are different based on individual characteristics such as fashion innovativeness. An online survey company in South Korea recruited 290 female apparel shoppers who purchased apparel online. To enhance external validity, this study used an existing retail website with a well-established mass customization program. After completing the customization program, participants complete the online questionnaire. Structural equation modeling (SEM) and latent mean analyses (LMAs) are then performed for analyses. This study stresses the importance of testing measurement invariance for mean comparisons. Before the SEM and LMA, this study follows the hierarchy of invariance tests (configural invariance test, metric invariance test, and scalar invariance test), which are not considered by traditional approaches such as ANOVA. These statistical analyses provide applicability of the invariance test procedures and LMA to consumer behaviors. The conclusions of mean differences have integrity and validity because they are guided by a sophisticated statistical procedure to ensure measurement invariance.

Presented here is a protocol to examine consumer responses toward mass customization in the context of online retailing. The protocol details the online survey procedure and how to analyze data using structural equation modeling and group differences using latent mean analyses.

As many scholars and practitioners study personalization and relationship marketing, it is important to provide personalization such as mass customization ...

Assessment of Sexual Behavior of Male Mice

Sexual behavior is highly species-specific. Although rodents have slightly different sexual behaviors, mice and rats have a similar sexual behavioral pattern. The purpose of this article is to describe the hormone-induced estrus ovariectomized female model and the experimental procedure for the assessment of sexual behavior of male mice. The most important sexual behavioral elements are demonstrated in the video and illustrations. The critical steps, advantages, and limitations of the sexual behavior test are explained as well. Finally, the behavior parameters are presented, and mounting, intromission, and ejaculation processes in mating are distinguished. Behavioral parameters are assessed in terms of the occurred duration and counts during the test period.

This article describes how to perform sexual behavior tests in male mice.

Sexual behavior is highly species-specific. Although rodents have slightly different sexual behaviors, mice and rats have a similar sexual behavioral ...

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Current athlete monitoring practice in team sports is mainly based on positional data measured by global positioning or local positioning systems. The disadvantage of these measurement systems is that they do not register lower extremity kinematics, which could be a useful measure for identifying injury-risk factors. Rapid development in sensor technology may overcome the limitations of the current measurement systems. With inertial measurement units (IMUs) securely fixed to body segments, sensor fusion algorithms and a biomechanical model, joint kinematics could be estimated. The main purpose of this article is to demonstrate a sensor setup for estimating hip and knee joint kinematics of team sport athletes in the field. Five male subjects (age 22.5 &#177; 2.1 years; body mass 77.0 &#177; 3.8 kg; height 184.3 &#177; 5.2 cm; training experience 15.3 &#177; 4.8 years) performed a maximal 30-meter linear sprint. Hip and knee joint angles and angular velocities were obtained by five IMUs placed on the pelvis, both thighs and both shanks. Hip angles ranged from 195&#176; (&#177; 8&#176;) extension to 100.5&#176; (&#177; 8&#176;) flexion and knee angles ranged from 168.6&#176; (&#177; 12&#176;) minimal flexion and 62.8&#176; (&#177; 12&#176;) maximal flexion. Furthermore, hip angular velocity ranged between 802.6 &#176;&#183;s-1 (&#177; 192 &#176;&#183;s-1) and -674.9 &#176;&#183;s-1 (&#177; 130 &#176;&#183;s-1). Knee angular velocity ranged between 1155.9 &#176;&#183;s-1 (&#177; 200 &#176;&#183;s-1) and -1208.2 &#176;&#183;s-1 (&#177; 264 &#176;&#183;s-1). The sensor setup has been validated and could provide additional information with regard to athlete monitoring in the field.&#160;This may help professionals in a daily sports setting to evaluate their training programs, aiming to reduce injury and optimize performance.

Monitoring athletes is essential for improving performance and reducing injury risk in team sports. Current methods to monitor athletes do not include the lower extremities. Attaching multiple inertial measurement units to the lower extremities could improve monitoring athletes in the field.

Current athlete monitoring practice in team sports is mainly based on positional data measured by global positioning or local positioning systems. The ...

Flypub To Study Ethanol Induced Behavioral Disinhibition and Sensitization

Alcohol use disorder (AUD) remains a serious problem in our society. To develop effective interventions for addiction, it is important to understand the underlying neurobiological mechanisms, for which diverse experimental approaches and model systems are needed. The main ingredient of alcoholic beverages is ethanol, which causes adaptive changes in the central nervous system and behavior upon chronic intake. Behavioral sensitization (i.e., escalated responses) in particular represents a key adaptive change underlying addiction. Most ethanol-induced behavioral sensitization studies in animal models have been conducted on the locomotor activating effect of ethanol. A prominent effect of ethanol is behavioral disinhibition. Behavioral sensitization to the disinhibition effect of ethanol, however, is underrepresented. To address this issue, we developed the Flypub assay that allows measuring the escalated increase in disinhibited courtship activities upon recurring ethanol exposure in Drosophila melanogaster.&#160;Here, we report the step-by-step Flypub assay including assembly of ethanol exposure chambers, setup of the assay station, criteria for fly care and collection, ethanol delivery, quantification of disinhibited courtship activities, data processing and statistical analysis. Also provided are how to troubleshoot critical steps, overcome limitations and expand its utility to assess additional ethanol-induced behaviors. The Flypub assay in combination with powerful genetic tools in Drosophila melanogaster will facilitate the task of discovering the mechanism underlying ethanol-induced behavioral sensitization.

The Flypub assay measures the behaviors that the fruit fly Drosophila melanogaster displays under the influence of ethanol. The assay can be readily mastered by experimenters at all levels and applied to various vaporized stimuli, facilitating substance abuse and addiction studies.

Alcohol use disorder (AUD) remains a serious problem in our society. To develop effective interventions for addiction, it is important to understand the ...

Assessment of Stress Effects on Cognitive Flexibility using an Operant Strategy Shifting Paradigm

Stress affects cognitive function. Whether stress enhances or impairs cognitive function depends on several factors, including the 1) type, intensity, and duration of the stressor; 2) type of cognitive function under study; and 3) timing of the stressor in relation to learning or executing the cognitive task. Furthermore, sex differences among the effects of stress on cognitive function have been widely documented. Described here is an adaptation of an automated operant strategy shifting paradigm to assess how variations in stress affect cognitive flexibility in male and female Sprague Dawley rats. Specifically, restraint stress is used before or after training in this operant-based task to examine how stress affects cognitive performance in both sexes. Particular brain areas associated with each task in this automated paradigm have been well-established (i.e., the medial prefrontal cortex and orbitofrontal cortex). This allows for targeted manipulations during the experiment or the assessment of particular genes and proteins in these regions upon completion of the paradigm. This paradigm also allows for the detection of different types of performance errors that occur after stress, each of which has defined neural substrates. Also identified are distinct sex differences in perseverative errors after a repeated restraint stress paradigm. The use of these techniques in a preclinical model may reveal how stress affects the brain and impairs cognition in psychiatric disorders, such as post-traumatic stress disorder (PTSD) and major depressive disorder (MDD), which display marked sex differences in prevalence.

Stressful life events impair cognitive function, increasing the risk of psychiatric disorders. This protocol illustrates how stress affects cognitive flexibility using an automated operant strategy shifting paradigm in male and female Sprague Dawley rats. Specific brain areas underlying particular behaviors are discussed, and translational relevance of results are explored.

Stress affects cognitive function. Whether stress enhances or impairs cognitive function depends on several factors, including the 1) type, intensity, and ...

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 ...