Applications of affordance and cognitive ergonomics in virtual design: A digital camera as an illustrative case

Affordance

The term affordance was coined by (Gibson, 1977), who argued that an artifact contains clues and that users simply perceive how to use it: that is, what it affords the user for good or for ill. He emphasized that humans or “animal” perceive these physical properties directly, reducing the cognitive gap between the products and its users. This concept was quickly adopted in Engineering Design, Human-Computer Interaction (HCI), and Industrial Design (ID). In Engineering Design, Fadel and his students are among the pioneers in the application of the concept into this field, classifying affordance into three types based on the entities that are interacting: Artifact-User Affordances (AUAs), Artifact-Artifact Affordances (AAAs) (Maier and Fadel, 2009a), and Artifact-Environment Affordances (AEAs) (Hu & Fadel, 2012). In the fields of HCI and ID, affordance has become one of the design principles, along with others such as discoverability, feedback, conceptual model, signifiers, mappings, and constraints (Norman, 2013).

According to Norman, there is abundant symbolic information in physical reality, that is, affordances. For example, a genuine hammer or a mug provides many physical clues about its purpose and how it should be used, information that users can directly and easily process. However, for electronic devices, consumers have to learn and remember the complicated operation process when they operate a product for the first time. The lack of physical characteristics to help users identify affordances is the primary difference between traditional mechanical products and consumer electronics. This, in turn, is the main cause of the increasing perception gap between users and products, somewhat contradicting Norman’s contention (Norman & Donald, 2002) that the perception of a product’s affordances is an important factor in its success. But the development of affordance is also accompanied by the challenge of how to apply this concept to design.

According to Gibson, people could understand how to operate a product through its visual characteristics or properties without any need for symbols or text. For contemporary products, Gibson’s concept appears idealistic as, for example, it is especially difficult to describe the interaction between humans and aspects of a graphical user interface (GUI). Based on Gibson’s concept, Djajadiningrat designed a video deck, eliminating the common symbols and color signs in his model (Djajadiningrat, 1998). He found that affordances provide a variety of ways for interaction, but cannot enhance a products’ usability (ease of use). Norman was one of the pioneers in applying affordances to design artifacts, increasing the applicability and practicability by dividing the concept into real and perceived affordances. El Amri proposed that if at least one of a product’s perceived affordances is good, users need less help in understanding the usability of this product, and its adoption would be enhanced (Dhouha, 2019). Thus, perceived affordance is important to designers. As Norman writes in his paper: “The designer cares more about what actions the user perceives to be possible than what is true. Moreover, affordances, both real and perceived, play very different roles in physical products than they do in the world of screen-based products. In the latter case, affordances play a relatively minor role: cultural conventions are much more important.” (Norman, 1999). He further illustrates this idea about a screen-based product, explaining that a circle drawn by a designer on a screen to indicate where a person should touch is not an affordance but rather a signifier as defined in the new edition of The Design of Everyday Things (DOET) (Norman, 2013).

In general, many electronic products are similar in shape: a closed and seemingly meaningless box. While the components inside the box may be fast and powerful, users do not interact with them directly. They interact through a GUI, relying on their knowledge and experience, interactions that involve a significant amount of cognitive knowledge. Researchers have addressed this situation, for example, Hartson defines and uses four complementary types of affordances in the context of interaction design and evaluation: cognitive affordance, physical affordance, sensory affordance, and functional affordance (Rex, 2003). Besides, Nagy and Neff proposed “imagined affordance” to describe three relationships, such as users’ perception, attitude and expectation, the materiality and functionality of technologies, and designers’ intentions and perceptions (Peter and Gina, 2015). In order to build and validate a measurement scale of perceived design affordance in high-tech products, El Amri and Akrout proposed “symbolic affordance” in their Perception Design Affordance Scale (PDAS) (Dhouha & Houcine, 2020). However, there remains a lack of agreement on terminology, for example, between cognitive affordances, conventional affordances and signifiers. This paper focuses on the complementary relationship between affordances and cognitive ergonomics using Norman’s concept of perceived affordance as exemplified by a chair that affords support both for sitting and carrying.

Cognitive ergonomics

Cognitive ergonomics is a branch of ergonomics based on cognitive psychology and other related sciences. Therefore, it shares a common purpose with ergonomics, which aims to facilitate human performance through the adaptation of tools or devices to human characteristics and preferences. Further, it focuses on the human mental processing that is involved in processing information, such as rehearsing and recalling. While cognitive psychology establishes general theories about mental behavior, cognitive ergonomics aims to apply this knowledge to make the human–product interaction compatible with human cognitive abilities and limitations (Haan, 2010), meaning it focuses on the application of cognition to a product.

Application of cognitive ergonomics in user interface design

User interface design, while not new, is an essential part of product design (Mariia & Angela, 2019). Today, increasing numbers of screens and graphical user interfaces are embedded in products. Therefore, determining what information should be displayed on a screen or which layout matches the users’ mental model is certainly worth exploring. The purpose of cognitive ergonomics is to develop and adapt cognitive devices and their uses to improve human information processing to increase efficiency, reduce errors and accidents, and increase well-being (Haan, 2010).

Cognitive ergonomics may help to improve the performance of a user interface by addressing, for instance, the layout of icons. A careful layout of icons plays a critical role in clarifying the functions of electronic products, and this is helpful for the users to understand and interact with the products. Vicente developed design principles for the layout of complex interfaces based on skills and knowledge, an approach referred to as Ecological Interface Design (EID) (Vicente Kim, 2002). In further research, some scholars proposed guidelines on how to design large screen displays, focusing on a digital interface based on a Process Control System (PCS) (Braseth & Ritsland, 2013). In addition, Nielsen found that based on eye tracking, users read web pages in an F-shaped pattern, with the upper part of the content area being noticed first and attracting the most attention (Nilsen, 2017). This F-layout, seen in Figure 1(a), is the natural behavior of most web surfers. Other patterns have also been found, including the Gutenberg diagram, the Z-pattern layout, the Zig-zag pattern layout (see Figure 1(b)), and the Golden-triangle layout.

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Figure 1.

Traditional layouts: linear layouts (a) F-shaped pattern (b) Zig-zag pattern.

It can be found that most researchers focus on the layout of complex, large interfaces and web pages. However, with the increased need to provide visual information for all kinds of devices such as power drills, handheld medical devices, and a variety of consumer electronics, increasingly smaller screens are being designed. Compared with large, complex screen-based interfaces, there is just a few published research papers guiding the designer on how to design these handheld digital devices’ (smaller) interfaces or how to standardize them, an area where research is much needed because these products are designed for users who neither have enough time to operate them nor want to be trained on how to interact with them. Cognitive friction is apparent when users interact with these products. To address this specific concern, this research focuses on the following issues:

• What type of display layout is suitable for handheld digital device’s screen?

• How can a handheld device interface be designed based on cognitive ergonomics?

Introduction to ABIGA

According to Maier and Fadel, one of the properties of affordance is form dependence (Maier et al., 2007, 2009b), meaning specific affordances may vary with changes in the design parameters. ABIGA is also created based on the form dependence property of affordances. In addition, ABIGA considers the product and users as an entire system. This tool is a design assistant system specifically for the early stages of the design process as it attempts to progressively improve a product’s form based on the perceived affordance qualities observed by users. The search mechanism used is AMGA2, the second-generation Archive Based Micro Genetic Algorithm (Santosh et al., 2011), which provides an efficient way to evolve design solutions using a very small population by taking advantage of an archive (Santosh et al., 2011). In this research, ABIGA was used with the aim of reducing the cognitive friction between the users and the product, based on their feedback.

Preparing for the implementation of ABIGA

Affordances and design parameters chosen

In designing a compact digital camera, the affordances of interest to the designer and the explanations provided to the users are the following:

• Portability: Interaction between the users and the camera when they carry it or store it in a bag/pocket.

• Hold-ability: Holding interaction between the user and the camera using either one hand or two.

• Exposure-ability: The unit of measurement of the total amount of light permitted to reach the electronic sensor while taking a photograph.

• Screen view-ability: Visual interaction between the users and the camera that allows them to conveniently see photos on the screen.

The six design parameters used to determine the body’s basic features and their ranges are shown in Figure 3. More detailed information about the affordances and design parameters to be chosen and how to choose them was provided by Chen et al. (2019).

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Figure 3.

Design parameters.

User interface and participants

Detailed information about the software ABIGA, including the development of the code, its use in various case studies, and its limitations can be found in Mata and Chen (Chen et al., 2019; Ivan et al., 2018). The web evaluation page for the camera based on ABIGA is divided into four sections as seen in Figure 4. At the top of the page, a short paragraph introduces the experiment and explains how to use the software. The camera is shown on the left side, the representation consisting of three views: front, top, and back. Size reference for the camera is shown using a hand holding an AA battery. This hand is a 50th percentile representation obtained by averaging the size of the typical male and female hand. The breadth and length of this hand are 88.4 mm and 174.9 mm, respectively (DTI, 2002). The evaluation survey rates the quality of the affordance on a 7-point Likert scale using quality values ranging from −3 to +3. Negative values indicate that the affordance exhibits poor usability; positive, good; and zero indicates a neutral rating. Users select each affordance quality value on a slider.

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Figure 4.

Camera user interface for evaluation.

Participants rated the quality of the affordances of this camera presented to them, with most respondents being mechanical engineering students, although the application is on the web and can be accessed by anyone. Based on the data obtained after the completion of the experiment, five users with ages ranging between 24 and 29 completed all the evaluations.

Evolution of designing camera form

The evolution of designing a camera

Although ABIGA as a multiple-objective Genetic Algorithm (GA) does not combine objectives, to show the overall evolution of the camera, the sums of the affordance over each generation were plotted to display the solution’s overall quality evolution (Ivan et al., 2018). The interactive aspect is related to the capture of the users’ feedback as to the value of each affordance. According to Banerjee et al., 15–20 generations/iterations can achieve satisfactory results using IGAs (Amit et al., 2008). Therefore, this camera study was run for 20 generations. As shown in Figure 5, the concepts tend to improve as the generations increase based on the evolution of the average fitness of the products’ affordances, meaning that the GA is creating solutions of better perceived quality overall (Table 1).

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Figure 5.

Camera: Average fitness of population versus generation.

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Table 1.

The range of values.

Design parameter	Minimum	Maximum
Width	325 pixel (86 mm)	430 pixel (113 mm)
Height	150 pixel (40 mm)	300 pixel (79 mm)
Depth	50 pixel (13 mm)	160 pixel (42 mm)
Fillet radius	15 pixel (4 mm)	120 pixel (32 mm)
Screen size	Sw = 163 pixel (43 mm)	Sw = 300pixel (80 mm)
	Sh = 75 pixel (20 mm)	Sh = 210pixel (56 mm)
Lens size	75 pixel (19 mm)	280 pixel (74 mm)

Relationship between affordance and design parameters

Workbench was used to extract the data from the database (Ivan et al., 2018). For the camera case reported here, logistic regression was used to check for the relationship between its design parameters and the affordances of interest. For this scenario, the design parameters that define the product form are continuous. The affordance quality response was categorized as described below using a binary logistic regression. The way the response was modified is shown in Table 2. Since designers are interested in what people perceive as good quality, zero-valued responses were not considered in the analysis.

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Table 2.

Binary categorization of user response.

7-point Likert scale	−3	−2	−1	1	2	3
Binary response	Not good (0)			Good (1)

Minitab 17 was used to perform the binary regression analyses; the results of all one-to-one comparisons are shown in Table 3. The p-value is shown for each pair of the affordance/design parameters, with values less than 0.05 being considered significant (Martz, 2015), suggesting that the affordance/design-parameter pair is related. These significant values are highlighted in the table.

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Table 3.

Binary logistic regression p-value results.

DP.Aff	Width	Height	Depth	Fillet radius	Screen size	Lens size
Hold-ability	0.453	0.000	0.764	0.017	0.169	0.527
Exposure-ability	0.591	0.000	0.079	0.299	0.793	0.000
Port ability	0.107	0.577	0.000	0.751	0.268	0.546
Screen view-ability	0.000	0.000	0.954	0.007	0.000	0.055

DP = design parameters; Aff = affordances.

The relationships identified in Table 3 provide the designers with an indication of the impact of the parameter(s) on the product’s perceived usability. For instance, if the designers want to improve hold-ability, the width of the camera seems irrelevant to the users, but its height and possibly its fillet radius are two parameters to modify. However, at times, there is no apparent direct relationship between a parameter and the affordances being considered. For example, screen size view-ability depends on screen size, which indirectly depends on the camera’s width, height, and fillet radius since these parameters constrain it. Two of these relationships in the camera study reported here are analyzed and discussed more fully in the following sections.

As listed in Table 3, the exposure-ability strongly relates to the camera height and the lens size. The diameter of the camera’s lens is constrained by the height of the camera because the lens diameter should be smaller than the camera’s height which is smaller than its width. In order to have high exposure-ability, larger lens size is expected, which can explain why larger lenses are continually being developed, one such example is the SLR camera (Single Lens Reflex camera). Figure 6 demonstrates the fitted curve from the binary logistic regression analysis indicating the relationship between the lens size and exposure-ability of the camera. It can be found that the design parameter lens size is significantly associated with the ability of the camera to capture light for an effective photograph, that is, its exposure-ability.

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Figure 6.

Logistic regression: Exposure-ability versus lens size.

In Figure 6, the data points represent the users’ binary categorized responses, with the curve representing the increased probability of a positive assessment as the lens size increases. For example, for a lens size of 0.6 (∼38 mm), the probability that a user would perceive the exposure-ability of the camera as positive is 0%, while lens sizes bigger than 0.9 (57 mm) have a positive assessment from almost 100% of the users.

Logistic Regression also identified the relationships between hold-ability and both height and fillet radius. The results show that the hold-ability has a positive effect on height, but a negative correlation with fillet radius. The designer needs to attempt to understand the reason for these relationships.

According to the Occupational Safety & Health Administration (OSHA), a hand in the shape of a “C” suggests holding or griping (Figure 7(a)). The suggested size for these hand postures is between 44.45 mm and 76.2 mm (Figure 7), within the constraints for the height of the camera in this study of between 40 mm and 79 mm. In the experiment, the user input obtained indicated that the participants prefer holding the camera as indicated in Figure 7 (a) or (b) when they perceive that the height is small. When the height is perceived to become larger, we assume that the posture of their hand changes, and they hold the camera as indicated in Figure 7c. This latter position does not require the exertion of excessive force by the users, explaining why users indicated that a bigger height size is easy to hold.

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Figure 7.

Three postures for holding.

This study also found that the fillet radius has a negative effect on hold-ability. Some users may think that a large fillet radius makes the camera easier to hold since there are no sharp corners to hurt them. But this larger fillet radius also has the potential of increasing slip-ability.

Subset of best solutions for the product form

Next, a set of optimized solutions were generated by the ABIGA. Because this tool focuses only on the quality of a product’s affordances rather than on its overall evaluation, the optimal models should also consider the designers’ experiences and not be dictated solely by the algorithm’s results. Two of the sets of design parameters suggested to the designer can be seen in Table 4:

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Table 4.

Two design solutions used in this research (unit: mm).

ID	Width	Height	Depth	Fillet radius	Screen body size	Lens diameter
1	90	54	12	4	Sw = 57	56
					Sh = 43
2	105	63	19	6	Sw = 82	50
					Sh = 48

Once the physical dimensions of the camera are identified, the information on the screen, which is the primary interface for the user, has to be properly designed. This research considered this aspect of the design using cognitive ergonomics.

Introduction to the Cog Tool

This research used the Cog Tool, which constructs valid Keystroke-Level Models (KLMs) (Card Stuart et al., 1980), to estimate the time of completion of certain tasks rather than another tool for three reasons. First, the Cog Tool evaluates UI design ideas quantitatively before investing resources in programming those ideas (John, 2010). This predictive human performance modeling has been proven to reliably and quantitatively predict various aspects of usability (Brinck et al., 2002; John Bonnie et al., 2012). In addition, it can also provide design recommendations by displaying timelines showing how long a user would look, think, and act while accomplishing a task. Designers can then identify differences between UIs by comparing their respective timelines (John Bonnie et al., 2012). Finally, this tool can save time and money as it does not require real end-user involvement. Thus, it is an appropriate initial measurement tool for evaluating the performance of the two UIs designed in this research.

UI design

According to McKay, the scan path for large displays is usually an arc beginning in the upper left and ending in the lower right, meaning the layout is understandable, simple, efficient, and orderly (Mckay, 2013). However, this is not the case for handheld digital devices (small) displays such as the ones being considered here. For smaller interfaces, the screen center is also the visual center, meaning that this area draws the most attention from the users and is the first one they focus on. Therefore, a new pattern layout is proposed here, as seen in Figure 8. The number one locates the center square, which will display the most important information or information that needs to be confirmed.

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Figure 8.

Nontraditional radiant layout.

For this reason, an elliptical design is drawn, which is more centric than the ones used for large screens, thus fitting the rectangular shape of a small screen. The purpose of this layout is to guide the users’ scan path from the center of the screen outward in all directions as seen in Figure 8 and Figure 9. The effectiveness of this elliptical layout will be compared with the traditional layout in the Evaluation Section. This research, thus, developed two layouts, a traditional linear layout, which simulated the actual interface of the Cannon SD1300 and was used as reference (Figure 10), and an elliptical one developed based on the concept that the center attracts more user attention.

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Figure 9.

Elliptic layout of the camera’s interface.

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Figure 10.

Linear layout of the camera’s interface (as reference).

The UI elements seen in applications include both interactive ones such as buttons and informative elements such as images, and color. In this research, the icons and buttons are the same for the two layouts for consistency, thus eliminating the effect of these factors on the results.

Icon design aims to facilitate communication and understanding with the users, ensuring the interaction with the UI requires minimum cognitive resources. Two design approaches, metaphoric and realistic, both from cognitive psychology, are used to provide hints to the users about how to interact with the texts and icons. For example, the image playback icon uses a circle to imply that users can browse through their photos. The circle icon and the behavior of photo browsing share the same characteristics, that is, users have the freedom to choose any photos that they want to check, but these photos are organized in the order they were taken. Other icons, such as for printing, deleting photos, and switching the settings, are designed in the same way. They could imply their operation or destination. Users will understand the meaning of these icons based on their experiences in the real world. In addition, based on cognitive ergonomics, other aids can be used to reduce the cognitive friction between users and the UI, including shading, underlined texts, arrows, and highlighted texts.

In this research, color is used not only to reflect the UI’s aesthetics, but also to indicate icon states through. For example, to draw users’ attention, being clicked icons or buttons are displayed in highlighted colors, with orange and blue used as target or foreground colors in these instances. The icons or buttons that are not clicked are displayed in light gray as seen in Figure 9 and Figure 10. In addition, a camera is usually used outdoors, so high contrasting colors are needed to ensure visual clarity. Based on research from cognitive ergonomics, white/ black and yellow/ black have been found to be the most effective combination (Shieh and Lai, 2008). Further, Pastoor found that dark colors function better as background and light ones as the target color (Pastoor, 1990). To enhance the visibility of the UI developed here, black and dark gray was selected as the background colors and blue and orange as the target colors as seen in Figures 9 and 10.

Evaluation

The camera Canon SD1300 was employed in this research as a reference to compare with our designed UI menu. Three tasks including how to adjust the camera’s parameters under the mode of taking a photo are set in order to evaluate the operation efficiency in using those UIs. In each task, five steps are set for further evaluation. For example, in task 1, step 1 to step 5 correspond to displaying information, blink detection, flash setting, AF-point zoom, and turn off grid lines, respectively. Table 5 lists the detailed functions needed to complete in each step for the three tasks.

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Table 5.

The tasks set-up in the shooting mode.

Tasks steps	Task 1	Task 2	Task 3
Step 1	Displaying information	Image playback	Set language as simplified Chinese
Step 2	Blink detection	Set as slideshow	Create folder
Step 3	Flash setting	Set play time at 5s	Set LCD brightness at the third level
Step 4	AF-point zoom	Erase photos	Power saving
Step 5	Turn off grid lines	Print photo	Set photo time

Once the tasks are set, we can use the Cog Tool (John Bonnie et al., 2012) to make quantitative prediction of human performance. Project, task, design and frame are mainly included in Cog Tool. The more detailed information about how to use the Cog Tool was provided by http://cogtool.hcii.cs.cmu.edu/

Figure 11 presents the predicted time results for human performance. It can be found that skilled users will spend longer operation time on the Canon linear UI than that on the elliptic one in completing all of the three tasks. The simulation result demonstrates that the elliptic UI is able to increase user efficiency and reduce the cognitive friction.

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Figure 11.

Results from the two layouts for the same tasks.

To analyze the differences between the two interfaces and user interaction in more depth, this study not only focused on task completion time but also emphasized the cognitive process by comparing the timelines obtained from the Cog Tool as seen in Figure 12. For designers, measuring only the completion time of a task does not completely identify UI problems or provide recommendations for optimization or redesigning. The timeline visualizations from the Cog Tool show what users see, think, and do.

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Figure 12.

Comparison of the visualization results from the two interfaces.

Figure 13 shows the series of procedures for the interactive behavior needed to be completed for Task 1. The procedure involves hand and eye movements, some of which are parallel, for example, the movement of the cursor to the target point and eye movement to the target simultaneously happened. The visualization results found that the time difference between the two UI’s layouts is primarily influenced by three movements: moving the cursor to the target point, the first eye movement to the target point, and the second eye movement to the target point.

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Figure 13.

Results from the different layouts for the same task (a) linear layout (b) elliptical layout.

To determine the influence of the distance between the button and the target on the predicted time cost by eye movement and cursor movement, the target point was simplified as point 3 in the visualization modeling as seen in Figure 14. Points 1 and 2 stand for the button position. The results show that the cost time for the first eye movement has a positive relationship with the distance between the starting point and the target point for both linear and elliptical UI layouts. However, there is no relationship between the cost time of the cursor movement and the distance. Linear regression was used to verify this relationship between the first eye movement and the distance as seen in Figure 15. A similar conclusion was found in Wang et al.’s study (Wang et al., 2015). Based on these results, it was concluded that the first eye movement helps to find the target points.

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Figure 14.

The coordinates for two different layouts.

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Figure 15.

Linear relationship between time and distance.

These results for the elliptical layout proposed here appear to contradict with the visual workflow from left to right and from top to bottom in the traditional layout that requires fewer cognitive resources for mastering interface information. Previous research focused on large-screen interfaces while this study focused on small screens where the interface is inside the range of sight. For this reason, the crossover visual workflow of the elliptical layout (Figure 14 (b)) does not affect the information processing of the user.

Besides analyzing the time cost for the first eye movement to the target point, the similar method was also used to identify and analyze the spent time for the second eye movement to the target point. The analysis results indicate no direct relationship between the distance and the time spent on the second eye movement to the target point. Additional factors affecting this relationship will be investigated in future work.

Through the above analysis, it was concluded that users will think, observe, and look for the desired target when they interact with interfaces. In this process, the first eye movement tries to find the target point. And the second eye movement plays the role of confirmation. From this perspective, different layouts should result in different time consumption. Using the situation investigated here as an example, the elliptical layout is considered advantageous since the distance between the starting point and the target is small, facilitating the user’s search for the target.

Design plan A

The design parameters for the camera’s physical shape were selected from the ABIGA optimization as seen in Table 4. Compared to Canon SD 1300, the new camera is similar in width and height. But the new camera is thinner. From Table 3, there is one to one mapping relationship between depth and portability, which can be inferred that the portability is enhanced. As design plan A is aimed at young people, the lens and screen occupy a large proportion of the shape to optimize the exposure-ability and screen view-ability, highlighting the camera’s unique design features. The shell is covered with high saturation blue color varnish to please the target users. The arrangement of the operation buttons follows the principles of favoring right-handed users. A single button for both reviewing and deleting is convenient for young people to adjust, capture, or remove photos easily. In addition, to enhance the camera’s hold-ability, a small piece of skid-resistant material is added to the upper right corner. These design features enhance the affordances of this camera. Finally, because of this age group’s desire for unique new features, the elliptical UI is used. The final design is seen Table 6.

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Table 6.

The modified camera compared with its original version (unit: mm).

Original	Modified-design plan A	Modified-design plan B
Body size: 91*56*22	Body size: 90*54*12	Body size: 105*63*19
Screen size: 55*35	Screen size: 57*43	Screen size: 82*48

Design plan B

The design parameters for this camera’s physical body were also obtained from the ABIGA optimization as seen in Table 4. The width and height of the new camera are larger than the reference, but the depth is similar to the original. The screen size of the newly designed camera is 82 mm*48 mm. In other words, its screen is more rectangular. The newly designed camera is for business people, a population with more professional needs than the younger group. As we all know, this group focuses on product quality. Based on this knowledge, the new screen with large size can enhance the user experience of screen-viewing. According to this, we infer that the present screen size is wider with an aspect ratio of 21:9 instead of 16:9. Since they frequently carry briefcases, a wider camera still features portability for this group. As for hold-ability, these users prefer using it with two hands rather than one, meaning the height also can be increased. Because of this increase in size, the operational buttons are arranged on both sides of the screen, different from design A as seen in Table 6. This arrangement allows both hands to operate the camera, meaning this general design not only takes the right hand into consideration but also considers a left-handed operation. The lens and screen of this camera occupy a large proportion of the shape to optimize the exposure-ability and screen view-ability. To improve the users’ perception of hold-ability, a skid-resistant material such as abrasive plastic and a large chamfer on both sides is adopted. Finally, since this group is older and, thus, prefers a familiar user interface, the traditional linear UI is used as seen in Table 6.