Gustavo Rovelo
email: gustavo.roveloruiz@uhasselt.be

Donald Degraen
email: donald.degraen@uhasselt.be

Davy Vanacken
email: davy.vanacken@uhasselt.be

Kris Luyten
email: kris.luyten@uhasselt.be

Karin Coninx
email: karin.coninx@uhasselt.be

Gestu-Wan -An Intelligible Mid-Air Gesture Guidance System for Walk-up-and-Use Displays

Keywords: Gesture guide, mid-air gestures, walk-up-and-use

We present Gestu-Wan, an intelligible gesture guidance system designed to support mid-air gesture-based interaction for walk-up-and-use displays. Although gesture-based interfaces have become more prevalent, there is currently very little uniformity with regard to gesture sets and the way gestures can be executed. This leads to confusion, bad user experiences and users who rather avoid than engage in interaction using mid-air gesturing. Our approach improves the visibility of gesture-based interfaces and facilitates execution of mid-air gestures without prior training. We compare Gestu-Wan with a static gesture guide, which shows that it can help users with both performing complex gestures as well as understanding how the gesture recognizer works.

Introduction

Walk-up-and-use displays that provide mid-air gesture-based interfaces often struggle with informing their users of the possible gestures that can be executed and how to perform these gestures [START_REF] Walter | Strikeapose: Revealing mid-air gestures on public displays[END_REF]. In this paper, we propose Gestu-Wan, an intelligible midair gesture guidance system for walk-up-and-use displays. Figure 1 shows Gestu-Wan, visualizing a mid-air gesture set to control an omni-directional video 1 .

2D gesture-based interfaces have to cope with similar issues, and several solutions exist to improve the visibility of 2D gestures. For mid-air gestures, however, there are no comprehensive solutions that increase the visibility of the available gestures, provide guidance on how to execute those gestures and are tailored for usage without prior training.

In particular walk-up-and-use displays require users to be able to use the system without prior training [START_REF] Walter | Strikeapose: Revealing mid-air gestures on public displays[END_REF]. This implies that users should feel comfortable interacting with a system they never encountered before, and of which they cannot predict the behaviour. Ideally, potential users are informed of how to use such a display when they approach it, while also being enticed to use the system. This is a challenging task when interaction is accomplished exclusively by means of mid-air gestures. As a result, interaction designers usually limit both the number of gestures as well as their complexity, Fig. 1: Gestu-Wan is an intelligible gesture guidance system designed to support mid-air gesture-based interaction for walk-up-and-use displays.

in order to improve the so-called "guessability" of gestures [START_REF] Wobbrock | User-defined gestures for surface computing[END_REF]. The goal of this work is similar to StrikeAPose [START_REF] Walter | Strikeapose: Revealing mid-air gestures on public displays[END_REF]: revealing mid-air gestures for walk-up-and-use contexts, such as public displays, but where StrikeAPose focuses on discoverability of the initial gesture, we focus on guided exploration of the full gesture set and on informing the user about how the gesture recognizer works. In this paper, we focus on a single-user setting. Exploring multi-user scenarios is an important next step, as it requires an in-depth analysis of aspects such as floor control and mutual awareness.

Gestu-Wan shows users how to perform mid-air gestures of various degrees of complexity without prior explanation or training. It uses a minimalistic visualization of the upper body skeleton and a visual structure that shows users how to execute mid-air gestures before and during the actual gesture execution. The visualization has been created using an informed design method, surveying users on what elements are required to visualize a mid-air gesture. We incrementally added additional visual information until we reached a visual representation that offers just enough information to comfortably perform the gesture while minimizing visual clutter. We strived for a minimalist design for several reasons: [START_REF] Anderson | Learning and performance with gesture guides[END_REF] to be able to present a structured representation of how to perform various gestures on a limited screen size, [START_REF] Anderson | Youmove: Enhancing movement training with an augmented reality mirror[END_REF] to avoid demanding too much attention of the users or distracting users that have no need for the gesture guide, (3) to allow for integration with the content displayed and (4) to maximize the data-ink ratio. The latter reason is known to lead to clear and concise visualizations, and focuses on showing the core information that needs to be conveyed to the user [START_REF] Tufte | The Visual Display of Quantitative Information[END_REF].

Our solution provides an intelligible approach for revealing mid-air gestures. First, Gestu-Wan makes both the initial gesture as well as all available options visible to the user in exchange for a limited amount of screen estate. Secondly, Gestu-Wan provides feedforward similar to OctoPocus [START_REF] Bau | Octopocus: A dynamic guide for learning gesture-based command sets[END_REF], but instead of presenting the full path of the gesture, it only shows the possible next steps (or poses) that can be performed and all the possible outcomes, given the current posture. Thirdly, Gestu-Wan provides real-time feedback while the user is performing interactions and reveals how a gesture is executed in all three dimensions. Finally, Gestu-Wan improves the user's awareness of how the gesture recognizer works, so the user will be able to quickly get used to the required accuracy of the gestures. We believe this to be of great value, since the way mid-air gesture recognizers work is hard to unravel for an end-user (i.e. required granularity and speed to perform the gestures), which leads to mismatches between the gesture performed and the gesture recognized.

Related Work

Without proper guidance, gesture-based interfaces have a steep learning curve, as most of the time gestures are difficult to discover and learn [START_REF] Kurtenbach | Contextual animation of gestural commands[END_REF][START_REF] Norman | Gestural interfaces: A step backward in usability[END_REF][START_REF] Wobbrock | User-defined gestures for surface computing[END_REF]. Several solutions have been proposed to improve the visibility and usability of 2D and mid-air gesture-based interfaces. For 2D gesture-based interfaces, such as mouse-based and multi-touch gestures, dynamic and real-time guidance systems have been proposed to support gesture execution (e.g. [START_REF] Bau | Octopocus: A dynamic guide for learning gesture-based command sets[END_REF][START_REF] Bennett | Simpleflow: Enhancing gestural interaction with gesture prediction, abbreviation and autocompletion[END_REF][START_REF] Freeman | Shadowguides: Visualizations for in-situ learning of multi-touch and whole-hand gestures[END_REF][START_REF] Vanacken | Ghosts in the interface: Meta-user interface visualizations as guides for multi-touch interaction[END_REF]). For mid-air gestures, however, there are less guidance systems available, especially for walk-up-and-use interaction. Some notable examples that we will discuss in this section are LightGuide [START_REF] Sodhi | Lightguide: Projected visualizations for hand movement guidance[END_REF], YouMove [START_REF] Anderson | Youmove: Enhancing movement training with an augmented reality mirror[END_REF], and StrikeAPose [START_REF] Walter | Strikeapose: Revealing mid-air gestures on public displays[END_REF].

Even in very early work on 2D gesture-based interfaces, extra clues such as cribnotes and contextual animations were used to expose the available gestures and how they can be performed [START_REF] Kurtenbach | Contextual animation of gestural commands[END_REF]. Marking menus [START_REF] Kurtenbach | Contextual animation of gestural commands[END_REF] expand on pie menus by offering the user the possibility to draw a stroke towards the desired item in order to activate the associated command, thus integrating 2D gestures with menu selection. The user learns how to perform the gesture by following the menu structure while performing the gesture, and can eventually activate menu items blindly by performing the associated gesture. Hierarchical marking menus [START_REF] Kurtenbach | The limits of expert performance using hierarchic marking menus[END_REF] increase the total number of menu options by presenting the user with several submenus. The subdivision of (complex) gestures is also a must for mid-air gestures, as it helps users to "find their way" through the different substeps to successfully execute a gesture.

Bau and Mackay focus on feedforward and feedback to facilitate learning and execution of complex gesture sets [START_REF] Bau | Octopocus: A dynamic guide for learning gesture-based command sets[END_REF]. Their gesture guide, OctoPocus, is a dynamic guide for single-stroke 2D gestures. Bau and Mackay are among the first to explicitly discuss feedforward as a separate concept for designing a successful gesture guide, an approach we also adopt in our work. Arpège [START_REF] Ghomi | Arpège: Learning multitouch chord gestures vocabularies[END_REF] provides finger by finger feedforward and feedback to help users learn and execute static multi-finger postures on multi-touch surfaces. TouchGhosts [START_REF] Vanacken | Ghosts in the interface: Meta-user interface visualizations as guides for multi-touch interaction[END_REF] is a gesture guide for 2D multi-touch gestures that demonstrates available gestures through virtual animated hands that interact with the actual user interface to show the effects of the gestures. ShadowGuides [START_REF] Freeman | Shadowguides: Visualizations for in-situ learning of multi-touch and whole-hand gestures[END_REF] visualize the user's current hand posture combined with the available postures and completion paths to finish a gesture.

Anderson and Bischof [START_REF] Anderson | Learning and performance with gesture guides[END_REF] compared the learnability and (motor) performance of different types of 2D gesture guides, and found that approaches with higher levels of guidance exhibit high performance benefits while the guide is being used. They also designed a gesture guide that supports the transition from novice to expert users, because most existing system insufficiently support that transition. This work provides additional grounding for a high-level dynamic gesture guide as a prerequisite to achieve a usable walk-up-and-use interface with mid-air gestures.

Nintendo Wii, Microsoft Xbox Kinect and PlayStation Move games make extensive use of mid-air gesturing to control games. These systems provide the user with written and graphical representations of the movements that are needed to play the games. Most games also incorporate a "training stage", an initial phase of the game that is specifically designed to teach users how to interact. For general gestures, Microsoft also provides a set of instruction movies2 . Mid-air gestures in games are, however, typically very simple and do not require accurate movements. In a walk-up-and-use scenario, similar instructions can be displayed on the screen when users approach it, but that is only practical in case of a small and simple gesture set.

In Augmented Reality (AR) applications, mid-air gestures are also very common and wide-ranging, as shown by the very extensive user-defined gesture set for AR of Piumsomboon et al. [START_REF] Piumsomboon | User-defined gestures for augmented reality[END_REF]. White et al. [START_REF] White | Visual hints for tangible gestures in augmented reality[END_REF] present graphical AR representations of potential actions and their consequences, which specifically target discovery, learning, and completion of tangible AR gestures.

Walter et al. [START_REF] Walter | Strikeapose: Revealing mid-air gestures on public displays[END_REF] investigated how to reveal an initial mid-air teapot gesture on public displays. Permanently displaying a textual instruction and a static contour of someone performing this gesture was found to be the most effective strategy, but the gesture is very straightforward, as users only need to touch their hip. LightGuide [START_REF] Sodhi | Lightguide: Projected visualizations for hand movement guidance[END_REF] incorporates feedback and feedforward by projecting graphical hints on the user's hand to guide mid-air movements. Users need to focus on their hand, however, which makes LightGuide suitable for exercise or physical therapy, but less appropriate when users are interacting with an application that requires visual attention. Furthermore, Sodhi et al. did not explore guidance of two hands simultaneously.

YouMove [START_REF] Anderson | Youmove: Enhancing movement training with an augmented reality mirror[END_REF] is a system for full-body movement and posture training. It presents the user with a skeleton representation that needs to be mimicked by the user, and uses an interactive mirror that shows the user in real-time how her current posture matches the target posture. YouMove is specifically built for training, however, and does not facilitate the discovery of the available gestures. Similar to LightGuide, users only have to focus on the guidance itself, since there is no underlying application that requires visual attention. This is an important difference, because it is challenging to clearly show users how to perform mid-air gestures without distracting them too much from the main purpose of the application.

In summary, Gestu-Wan is the first guidance system for walk-up-and-use scenarios that provides feedforward and feedback for mid-air gestures. We refer the reader to the work of Delamare et al. [START_REF] Delamare | Designing guiding systems for gesture-based interaction[END_REF] for a complete discussion of the design space for gesture guidance systems. Fig. 2: Gestures are subdivided in sequences of postures, which are structured hierarchically. A user follows a path in the gesture tree, performing the subsequent postures, until a leaf node is reached and the action associated with that gesture is executed. Note that this is only an illustrative visualization of the underlying gesture tree; it is not a depiction of Gestu-Wan.

An Intelligible Gesture Guide

In this section, we discuss how Gestu-Wan assists users in discovering and performing mid-air gestures, and its specific properties that make it suitable for walk-up-and-use situations.

Structure and Visual Appearance

Gestu-Wan uses a set of intermediate postures to generate the guidance visualization. We define this dataset in a pre-processing phase by manually subdividing each midair gesture in a number of steps, a sequence of postures that needs to be matched. By decomposing the gestures, they can be structured hierarchically in a tree, as shown in Figure 2. A user follows a path in the gesture tree, performing the subsequent steps that are shown in the nodes, until a leaf node is reached and the action associated with that gesture is executed.

Segmenting gestures can be cumbersome, as not every type of gesture can be decomposed in a straightforward manner. Consider, for example, a continuous circular movement, or varying the zoom level according to the distance between the hands of the user. Splitting up such a continuous gesture or representing the parametric input causes an exponential growth of the tree or requires adding an extra level of detail (e.g. arrows, labels) to the representation. Although Gestu-Wan is able to support both approaches, we did not further investigate this feature in the context of the presented Fig. 3: Gestu-Wan showing a set of possible mid-air gestures. Each gesture is subdivided in a number of steps, thus a sequence of postures that need to be matched. The user can see the previous step (the green skeleton at the top), the next possible steps (the black skeletons), and her current posture (the blue overlay skeleton). The numbers at the side represent the steps: the user already executed two steps and is now in the third step.

work. The automatic segmentation of gestures into series of reproducible postures is also outside the scope of this paper. Our focus is on the representation and structuring of mid-air gestures, and on showing what effect the execution of a gesture has.

Instead of showing the entire gesture tree to the user, Gestu-Wan only shows the previous posture that the user correctly matched and the next possible postures, as seen in Figure 3. Each of the next possible postures is also labelled with text that describes which action(s) can be reached through that posture. The current posture of the user is displayed in blue and is continuously updated, while the possible target postures are shown in black. Once the current posture of the user matches with one of the target postures, either the next possible steps of the gesture are shown, or, in case of a leaf node, the action corresponding to the gesture is executed. Note that we only add an open or closed hand to the skeleton when that particular hand posture is required; when the hand is not visualized, the recognizer does not take into account the hand posture.

If the current posture of the user no longer matches the requirements from the previous steps, the color changes from blue to red and the user has a few moments to correct her posture. If not corrected in time, the gesture tree resets to the first step.

To avoid unintentional execution of gestures when a new user approaches the system, Gestu-Wan first shows an initial gesture that the user has to perform in order for the recognizer to start interpreting the postures in the gesture tree. The importance of learning about such an initial gesture for walk-up-and-use displays has been shown by Walter et al. (the teapot gesture) [START_REF] Walter | Strikeapose: Revealing mid-air gestures on public displays[END_REF].

Animations

Gestu-Wan provides two types of animations that contribute to the recognizability of how to proceed to finish a gesture: the overlay skeleton and hierarchy traversal animations. The overlay skeleton, depicted in blue in Figure 4, shows the current posture of the user on top of the target posture, shown in black. The current posture is tracked in real-time, so any movements of the user are immediately reflected in the overlay skeleton. This dynamic aspect is important for users to identify themselves with the overlay skeleton and to quickly identify what posture to take. Performing the next step of a gesture is thus reduced to trying to match the overlay posture with the target posture.

The traversal through the gesture hierarchy, also depicted in Figure 4, is animated: each time a target posture is matched, the gesture tree shifts upwards. The top shows the users where they came from (i.e. which posture they matched in the previous step) and the bottom shows what the next possible steps are. This strengthens the idea that the gesture set can be navigated as a hierarchy, which makes it easier for users to situate their current posture within the whole gesture tree.

Feedback and Feedforward

The core aspect to create an intelligible gesture guide is providing both feedback on the user's actions and the system's behaviour, as well as feedforward to show what effect the execution of a gesture has. While a vast amount of work exists on this in the area of 2D gestures (e.g. [START_REF] Anderson | Learning and performance with gesture guides[END_REF][START_REF] Bau | Octopocus: A dynamic guide for learning gesture-based command sets[END_REF][START_REF] Kurtenbach | The limits of expert performance using hierarchic marking menus[END_REF][START_REF] Long | Implications for a gesture design tool[END_REF]), only little can be found on using a combination of feedforward and feedback for mid-air gestures.

Similar to the feedforward provided by OctoPocus for 2D gestures [START_REF] Bau | Octopocus: A dynamic guide for learning gesture-based command sets[END_REF], Gestu-Wan presents continuous and sequential feedforward while executing a gesture. It shows what the possible next steps are when reaching a particular posture, thus what postures can be performed next (sequential) while performing the posture (continuous). Besides the continuous and sequential feedforward, Gestu-Wan also includes functional affordances. This is simply done by using labels to indicate the possible actions, thereby revealing the effect one can expect (e.g. "zoom in" or "pan left"). For a full description of feedforward, we refer to Bau and Mackay [START_REF] Bau | Octopocus: A dynamic guide for learning gesture-based command sets[END_REF] and Vermeulen et al. [START_REF] Vermeulen | Crossing the bridge over norman's gulf of execution: Revealing feedforward's true identity[END_REF].

While feedforward tells the user what to do and what she can expect when she does this, feedback tells her more about how the system tracks her while performing gestures. The most obvious feedback is the overlay skeleton that tracks the user continuously and shows the tracked posture in real-time. Once a posture is matched, it turns green and Gestu-Wan shifts the hierarchy up by means of an animation. When the user's current posture no longer matches the requirements from the previous steps, the overlay skeleton turns red until the user corrects her posture or the gesture tree resets.

Design Rationale

Since we target walk-up-and-use settings, the system should provide guidance in such a way that occasional and first time users can easily perform mid-air gestures while immediately engaging with the system. It is, however, very challenging to clearly show users how to perform mid-air gestures without overwhelming them with details or distracting them too much from the main purpose of the application. In an early design phase, in which five users tried a first version of Gestu-Wan, we identified three categories of challenges: (1) providing appropriate depth cues (occlusions, shadows, perspective, etc.), [START_REF] Anderson | Youmove: Enhancing movement training with an augmented reality mirror[END_REF] showing which body parts contribute to a gesture, and (3) visualizing gestures that include hand postures.

Depth cues are needed to show 3D movements and postures. When a rich and wideranging set of mid-air gestures is required, the gestures inevitably become more complex and require more movements in all three dimensions, which makes depth cues indispensable. However, a broad range of depth cues exist, and we want to find the minimum amount of details required to clearly convey depth. Showing which body parts are involved in a gesture is important to avoid that users try to match the entire body, and not just the parts that matter. For instance, a gesture might simply require the user to raise and close her left hand. However, if the gesture guide does not explicitly visualize the right hand as "inactive", the user might also try to precisely match the position and posture of her right hand, which makes the gesture unnecessarily hard and uncomfortable to perform. With these challenges in mind, we performed an online survey to guide the design of the guidance visualization. In this survey, 50 respondents rated different skeleton representations to find out the best combination of depth cues, body representation, level of detail for the hand postures. Participants were asked to rate each of these designs on a scale according to clarity and visual attractiveness. Figure 5 shows an example of three design alternatives.

The respondents preferred a complete trapezoid representation of the upper body, including both arms, the torso and the head, using a different shade of color to represent an inactive arm. Respondents argued that using a more complete representation of the body increases the "guessability" of the gestures, and that the torso and its rotation could be easily perceived. Drawing only a stick figure was confusing, as participants expressed that the position of the arms with respect to the body was less clear. The respondents also highlighted the importance of occlusions to perceive depth (i.e. the torso being partially hidden by the representation of the arms, hands and joints). A perspective projection of the skeleton was clearer than using an orthogonal projection, and using shadows (i.e. a projection of the torso, arms and joints) combined with a rectangular grid drawn with a perspective view was the clearest way to provide depth cues.

In addition to the results from the online survey, we considered the relevant guidelines from the set suggested by Anderson et al. for YouMove [START_REF] Anderson | Youmove: Enhancing movement training with an augmented reality mirror[END_REF]. Gestu-Wan leverages domain knowledge on the behaviour of the gesture recognizer. The hierarchical structure shows how gestures are composed of subsequent postures, and the animations show how the recognizer progresses to the next steps while performing series of postures. This not only improves the visibility of the recognizer's behaviour, but also makes the interaction more intelligible [START_REF] Bellotti | Intelligibility and accountability: Human considerations in context-aware systems[END_REF], since the system shows the user how it perceives and processes the input. The user will notice early why some movements and postures work or do not work, and are thus able to efficiently interact with the system much faster.

The overlay skeleton immediately shows the user that she is being tracked as soon as she enters the field of view, which improves the discoverability of the interactive nature of the system. As a result, users quickly connect with the system, thereby motivating the user to engage in further interactions. Gestu-Wan conveys the necessary information to smoothly start interacting without prior training, while the visualization minimizes visual clutter, keeping the presentation simple. There is no need for any prior experience with mid-air gesturing, and gestures do not need to be remembered, requiring a low cognitive load.

User Study

The main goal of the user study is to evaluate to what extent the properties of Gestu-Wan enable walk-up-and-use interaction. In particular, we want to evaluate if the visual structure and the continuous feedforward and feedback increase discoverability and learnability of mid-air gestures, and, at the same time, increase awareness of the recognizer's behaviour.

We performed a controlled lab experiment, in which we asked volunteers to perform a set of tasks in an Omni-Directional Video (ODV) player. The content of the ODV is a first-person video walk-through of university premises (some streets, buildings and parking lots), providing an experience similar to Google Street View, but using video instead of a set of still images. The ODV player supports the following actions: play, pause, restart, pan left and right, zoom in and zoom out.

The gesture set used in our study is based on the user-defined gesture set of Rovelo et al. [START_REF] Ruiz | Multi-viewer gesturebased interaction for omni-directional video[END_REF], which specifically targets ODV interaction. According to their findings, the gestures are reasonably easy to learn and in some cases also easily discoverable. Since all the gestures in that set are fairly simple (i.e. consisting of three "steps"), we replaced the pause and restart gestures with two more complex gestures of five steps. This allows us to test Gestu-Wan with a broader range of gestures and helps us to evaluate the depth dimension of the hierarchical representation more thoroughly. The added complexity of those gestures also prevents accidental activation of the "disruptive" action of restarting the ODV.

Baseline Considerations

Gestu-Wan is, to our knowledge, the first walk-up-and-use mid-air gesture guidance system, thus comparing it with another similar system is not possible. A few systems that support mid-air gestures, such as YouMove [START_REF] Anderson | Youmove: Enhancing movement training with an augmented reality mirror[END_REF] and StrikeAPose [START_REF] Walter | Strikeapose: Revealing mid-air gestures on public displays[END_REF], are available in literature and are discussed earlier in this paper. Although we highlighted some significant differences with these systems, the findings that are presented in those papers proved to be very useful as a basis for informing the design of Gestu-Wan. To evaluate Gestu-Wan, we decided to compare it against a static printed graphical gesture guide (see Figure 6A). The paper version gives participants a complete overview of all gestures and allows them to quickly skim what they need to do without the time constraints that would be imposed by using, for example, video guides. We chose a static representation because it is a very low-threshold and informative way of representing gestures (e.g. always visible, easy to skim and read, no side effects of animations).

Alternatively, Gestu-Wan could be compared against a textual explanation of the gestures or a set of videos or animations. Both alternatives would require a lot of time to be processed by the participants and would not represent the whole gesture set in a visible, structured way. The printed graphical guide and Gestu-Wan do provide such an overview: the former shows the structure of the whole set at once, while the latter reveals it incrementally, while gestures are being performed. Furthermore, textual explanations and especially videos and animations do not offer accessible feedforward while performing gestures. The graphical printed guide does offer this in a sense, because the user can easily scan ahead in the graphical representation to see what is next and what it will lead to.

These considerations led us to choose the printed graphical gesture guide as a basis for comparison. For walk-up-and-use situations, this representation offers the most advantages over the other alternatives.

Methodology

We used a between-subjects design to compare the performance of participants who used the dynamic guide, Gestu-Wan, with participants who used static gesture representations printed on paper. The printed representations are similar to the drawings that are used by Nintendo Wii or Microsoft Kinect games, and clearly show the different steps that a user needs to perform to complete each gesture, as shown in Figure 6.

Every participant started the study with an exploration phase, in which the participant was free to try and explore the system. After the participant performed the initial gesture, she could control the ODV player by performing any of the gestures that the (dynamic or static) guide presents. During this initial phase, we explained neither the gesture guide, nor the ODV player, in order to evaluate the effectiveness of the gesture guidance for walk-up-and-use interaction. We interrupted this initial phase after the participant performed each gesture at least once. If a participant kept repeating certain gestures or if she did not explore every available gesture, she would be reminded that the next phase of the experiment would commence as soon as each gesture was executed.

Next, one of the observers explained the purpose of the study and the tasks they needed to perform. We reset the ODV to its original starting point and asked participants to perform each action three more times. The first two times, participants were asked to execute every action one by one, e.g. when asked to zoom out, the participant had to find the necessary steps to perform that particular action. The third time, participants were asked to accomplish a more complex task: "walk until building three is visible, then zoom in on its address sign and read it to us". This task required several actions, such as starting the video, pausing it when the building is found, panning to focus on the area that contains the address sign and zooming in on the sign. By giving participants a task that requires them to engage with the ODV content, we created a situation that resembles using the system in a real-life setting, which helped us to investigate the user experience in the post-usage questionnaire.

After performing all the tasks, participants answered a short questionnaire regarding the gesture guide and their experience with the interaction with the ODV. They also exchanged their thoughts with one of the observers during an informal interview, for instance on the gestures that caused them problems.

The system logged the time every participant spent on each step of the gestures and the number of times that participants activated the wrong action. Two observers were present during the study and took notes. Afterwards, the observers analyzed video recordings of the sessions to annotate participants' questions, remarks and frustrating moments during their interaction with the system. The observers also used this analysis phase to validate the annotations made during the study.

Participants

We invited 26 participants (15 men and 11 women). We balanced both conditions with respect to gender and professional background: 15 participants had a background in computer science, six in bio-science, three in economy and administration, one in statistics and one in graphical design.

All participants were familiar with 2D gesture-based interfaces, as they all own an smartphone. Experience with mid-air gestures was limited, as only two participants of the baseline group reported that they play Microsoft Xbox Kinect games (one plays less than one hour a week, the other between one and five hours). Six participants reported they play video games using the Nintendo Wii: one of the Gestu-Wan group and two of the baseline group play less than one hour, while two of the former group and one of the latter group play between one and five hours a week.

Given our focus on walk-up-and-use displays, we opted for a wide diversity of participants and did not perform a visual-spatial awareness test beforehand. The high number of participants with a background in computer science is because of the environment in which the study took place. There were, however, also no participants who regularly use mid-air gestures among the computer scientists.

Apparatus

We used a 43" display to show the ODV, mounted on a pedestal to place its center more or less orthogonal to the participants line of sight, as shown in Figure 6. Gestu-Wan was shown on a separate 21.5" screen, placed at the lower right corner of the ODV screen. The printed guide was on a A0 sheet, located bellow the ODV screen. The size of the representation of each gesture was the same in both conditions (approximately 12 by 7 cm).

Participants were standing at a fixed position, two meters from the display. To track the participants' movements, we used a second-generation Microsoft Kinect. We also recorded all the sessions with two video cameras. One camera was used to record the gestures and the participants' comments while they were interacting. The other camera recorded the ODV and gesture guide.

Results and Discussion

Phase 1: Free Exploration Phase. The free exploration phase offers us the possibility to compare how both types of guidance help participants to discover the interaction possibilities without any prior knowledge of the system. We extracted the time to activate the system from the system's logs, by calculating the elapsed time between the moment when the participant was instructed to start and the moment when she successfully performed the initial gesture. This gives us some insights into how long it takes users to get acquainted with the system. Fig. 6: On the left, the setup that was used for the study with the printed gesture guide (A). On the right, the setup with Gestu-Wan (B).

Figure 7(a) shows the distribution of elapsed time for both conditions. Welch's t-test shows no significant effect of the guidance on the time to activate the system (t(24) = -1.102, p = 0.141). With the dynamic guide, the average time was 81.37±63.65 seconds, while it was 116.34 ± 95.08 seconds with the static guide. Note that this elapsed time not only encompasses the time that was required to execute the initial gesture, but rather the whole initial phase to discover and get acquainted with the system, since this was the first time that participants saw the setup. Furthermore, there was no sense of urgency to start interacting with the ODV. Participants who used Gestu-Wan typically took their time to first explore the behaviour of the skeleton (e.g. moving, leaning forward, waving). This type of "playful" behaviour was also reported by Tomitsch et al. [START_REF] Tomitsch | Who cares about the content? an analysis of playful behaviour at a public display[END_REF]. We did not observe this playful behaviour in participants who used the static guide. Some of these participants were confused about the status of the system (e.g. asking "Is the system working already?") or asked the observers for feedback and instructions on what to do.

A number of participants could not activate the system without assistance from one of the observers: two participants in case of the dynamic condition and six in case of the static condition (Figure 7(b)). We provided extra feedback to them about the cause (e.g. not lifting the arm sufficiently high), so they could continue with the study.

If we look at the gestures performed after the initial gesture, one participant of each group was not able to perform the "play" gesture, and one participant who used the static guide could not perform the "restart" gesture. The most difficult gesture during exploration seemed to be "pause": while only one participant using the dynamic guide failed to perform it, 11 of the 13 participants who used the static guide did not succeed. Fisher's exact test reveals that the number of participants who completed the "pause" gesture differs significantly between conditions (p < 0.001, 95% CI [5.60, 5743.07], odd ratio = 82.68). Since we redefined the "pause" gesture from the gesture set of Rovelo et al. [START_REF] Ruiz | Multi-viewer gesturebased interaction for omni-directional video[END_REF] and made it much more complex to execute, this effect is in line with our expectations.

(a) Distribution of the time that was required to activate the system during the free exploration phase of the user study. Phase 2: Individual Gesture Execution. In this phase, participants were asked to execute every action one by one. We analyzed the video recordings and logs to count the number of failed attempts before they correctly performed a gesture. We marked an attempt as failed when a participant triggered the wrong action, or performed the sequence of steps, but one of the steps was not recognized by the system and the action was thus not triggered.

The analysis reveals a trend that indicates that the dynamic guide helps participants to be more accurate when performing gestures. Figure 8 shows an overview of the medium number of failed attempts per condition for each gesture. The order in the figure is also the order in which the participants had to execute the gestures. Only one participant could not perform the "pause" gesture with the dynamic guide. With the static guide, on the other hand, one participant could not perform "play", 10 participants could not perform "pause", and one participant could not perform "restart". The considerably higher average number of failed attempts for the "zoom out" gesture in the dynamic condition is caused by one of the participants who failed to perform the gesture over 10 times in a row. This participant executed all other gestures fine, and we have no explanation for the sudden change. This distorts the results for the "zoom out" gesture, although when not including this outlier, the static condition still outperforms the dynamic condition slightly (mean number of attempts dynamic: 0.4, static: 0.3).

There is a statistically significant difference in the number of failed attempts for the "pause" gesture (t(17.17) = -3.767, p = 0.001, 95% CI[-3.84, -1.08]). This confirms our expectation that for more complex gestures (e.g. composed of more than three steps and requiring movements in the depth plane), the dynamic guide outperforms the static guide. For simple gestures, however, the performance of both guides does not differ statistically. Fisher's exact test reveals that the number of participants who completed the "pause" gesture differs significantly between conditions (p < 0.001, 95% CI [3.34, 2534.63], odd ratio = 41.35).

The analysis did not reveal any overall statistically significant difference regarding the time that participants required to perform every action. This might be due to the fact Fig. 8: Distribution of failed attempts to perform the seven gestures during the second phase of the user study. Complex gestures benefit from the dynamic condition, while there is no significant benefit for simple gestures.

that following the guide in the dynamic condition costs time, but also because of the simplicity of most gestures.

Phase 3: Navigate and Search. Participants were asked to perform a navigation and search task, giving them the opportunity to evaluate the experience during a realistic task. They were asked to look for a specific location in the ODV, and then focus and zoom in on a specific point of interest. Participants of both groups rated the intrusiveness of the guides as neutral. However, our observations and the comments of four participants during the informal interviews show that participants were almost constantly observing either the dynamic or static guide while executing a gesture. Given the position of both the screen (dynamic) and paper (static), as shown in Figure 6, this is not surprising. The out-of-context visualization interferes with watching the ODV content, since it requires shifting focus. We envision the dynamic gesture guide being integrated in the actual content, as depicted in Figure 1, which should make it less disruptive.

Post-study questionnaire. Our post-study questionnaire, of which an overview of the results is shown in Figure 9, revealed some interesting and unexpected results. In the dynamic condition, users were more aware of the gestures that were executed. When they performed a gesture by accident, they were able to identify what happened because the gesture guide's feedback. A Mann-Whitney U-test shows a significant positive effect of the dynamic guide on awareness of gestures being accidentally performed (U = 132, p = 0.013). In both conditions, participants triggered actions by accident at about the same rate, but the median rating with regard to the question on triggering actions by accident is four for the participants who used the dynamic guide ("agree" on the fivepoint Likert scale), and two for the participants who used the static guide ("disagree" on the Likert scale).

The post-study questionnaire also shows that the dynamic guide provided adequate insight in the behaviour of the system, although it was rated somewhat lower than the Fig. 9: Summary of the post-experiment questionnaire. static guide. This seems odd, since in the dynamic condition, the user is guided through the gestures step-by-step. However, the static guide shows an ordered overview of all postures required to perform a gesture, informing the user about the whole gesture at once. Moreover, since we used a between-subject design, we are unsure how users would rate the two conditions with respect to each other when being exposed to both guides. Being able to see all the steps at once also resulted in a higher subjective appreciation in case of the static condition. This indicates that a dynamic gesture guide might need to incorporate more extensive feedforward to provide a full overview of the gestures.

The static guide, however, takes up a lot more space than the dynamic guide, since all the steps of each gesture need to be presented at once. Even with the limited number of gestures used in our study, integration of the static guide with the display itself (e.g. overlayed on the content) is not possible. The gesture set that was used in the study only contained eight gestures, but the AR gesture set presented by Piumsomboon et al. [START_REF] Piumsomboon | User-defined gestures for augmented reality[END_REF] is, for instance, much more extensive. Gestu-Wan can handle such an extensive set more easily than a static guide, on the condition that we structure the gestures hier-archically. Furthermore, a larger gesture set typically requires users to perform gestures more accurately in order for the recognizer to distinguish potentially similar gestures. Gestu-Wan helps users to deal with a recognizer that requires accuracy.

Conclusion

We presented Gestu-Wan, a dynamic mid-air gesture guide specifically designed for walk-up-and-use displays. The contributions are that Gestu-Wan (1) makes the available gestures visible to users, (2) facilitates the execution of mid-air gestures, and (3) turns the gesture recognizer into an intelligible system. The intelligibility results in users gaining a better understanding while performing gestures about what to do next and about how the gesture recognizer works. Given the walk-up-and-use context, we assume that there is no upfront training and that users want to access the system immediately.

Throughout the design and evaluation of Gestu-Wan, we perceived the difficulties of supporting mid-air gestures. Nowadays, typical gestures are fairly simple and can easily be performed with a static guide. Such a static guide, however, requires a lot of space. We also noticed that a dynamic gesture guide not necessarily leads to a significantly improved performance. Difficulties in perceiving depth and the amount of attention that a dynamic guide requires might be as demanding as trying to reproduce gestures presented on a static medium. The guidance provided by Gestu-Wan did, however, outperform the static guide for complex gestures.

We believe that the further progression of mid-air gesture-based interfaces entails challenges to present information to the user about which gestures are available and how to execute them, in a limited though easily accessible space. Gestu-Wan is the first system that provides a functional solution for these challenges.

Fig. 4 :

 4 Fig. 4: Step (1): (A) shows the previously matched posture, while (B) is the target posture. Step (2): When the target posture is matched, it moves up and the tree expands to show the next steps. Step (3): All the next possible postures are shown and the user can match one of them to continue with the gesture.

Fig. 5 :

 5 Fig. 5: Several of the designs that respondents of the online survey had to evaluate according to clarity and visual attractiveness.

  (b) Number of participants who required assistance from one of the observers to activate the system during the free exploration phase of the user study.

Fig. 7 :

 7 Fig. 7: Results for the exploration phase.

A 360°video recording, e.g. http://www.youtube.com/watch?v=ClAuhgFQpLo

http://support.xbox.com/en-US/xbox-360/kinect/body-controller

Acknowledgments

The iMinds ICON AIVIE project, with project support from IWT, is co-funded by iMinds, a research institute founded by the Flemish Government. We thank the participants of our study.


Vicente Nacher
email: vnacher@dsic.upv.es

Javier Jaen
email: fjaen@upv.es

Evaluating the Accuracy of Pre-Kindergarten Children Multi-touch Interaction

Keywords: M ulti-touch interaction, gestures, usability evaluation, prekindergarten, accuracy 1

The direct manipulation interaction style of multi-touch technolgy makes it ideal for pre-kindergarten children. Recent studies have shown that these challenging users are able to perform a set of basic multi-touch gestures. However, little is known about the accuracy that they can achieve. This paper evaluates the performance of prekindergarten children when accuracy is required in the termination phase of these gestures and points out that a mechanism for dynamically adapting the accuracy level could help children in their motor skills development.

Introduction

Nowadays children between zero and eight years old are frequent users of digital media [START_REF] Sense | Zero to Eight: Childrens M edia Use in America[END_REF]. In fact, as touch allows a more intuitive and natural way of interaction [START_REF] Smith | Developing, evaluating and deploying multi-touch systems[END_REF], they are often exposed to multi-touch technology even before they learn higher oral communication skills.

This has been confirmed by recent works such as [START_REF] Nacher | M ulti-touch gestures for pre-kindergarten children[END_REF] which reveals that even children between the ages of two and four are able to perform a basic set of touch gestures and [START_REF] Vatavu | Touch interaction for children aged 3 to 6 years : Experimental findings and relationship to motor skills[END_REF] which concludes that, overall, children aged 3 to 6 years are able to perform the tap, double tap, drag & drop and double drag & drop gestures. However, these works have also pointed out that very young children have precision problems in both the acquisition and termination phases of these interactions because of limitations in cognitive and motor skills .

Regarding this matter, there are no studies in the literature addressing the topic of accurate performance of multi-touch gestures by pre-kindergarten children. In this paper we explore whether children aged two to three years are able to perform a set of touch gestures when high levels of precision are required and evaluate whether factors such as age and gender have an impact on the performance of these interactions by pre-kindergarten users. This paper contributes to a growing body of literature in the area of children-computer interaction by providing findings from a controlled experiment with four touch gestures in which the termination phase must be performed with high-levels of precision. The findings will confirm that, at this early age-range, there are significant differences among subjects with respect to precision and, therefore, designers of future touch based applications for these specific users should devise adaptive mechanisms to cope with different levels of accuracy that allow very young children to exercise and incrementally develop fine-grained touch interaction skills.

2

Related works

Several studies have analyzed the use of touch devices by pre-kindergarten children.

The works of Abdul Aziz et al. [START_REF] Abdul | Selection of Touch Gestures for Children's Applications: Repeated Experiment to Increase Reliability[END_REF][START_REF] Abdul | Selection of touch gestures for children's applications[END_REF] evaluated the tap, drag, rotate, drag and drop, pinch, spread and flick gestures with children aged 2 to 4 years. Their results showed that 4 years old children were able to perform all gestures, the 3 years old ones only had some issues with the spread task and the youngest users (2 years old) were able to perform the tap and drag gestures properly but had some issues with the more complex ones.

On the other hand, the study of Nacher et al. [START_REF] Nacher | M ulti-touch gestures for pre-kindergarten children[END_REF] evaluated a basic set of multitouch gestures with pre-kindergarten children (2 to 3 years old) and concluded that they are able to perform gestures such as tap, drag, scale (up & down) and one finger rotation. Moreover, the authors of this work point out that, when some proposed assisted strategies are used, pre-kindergarten children are able to perform more problematic gestures such as double tap and long press [START_REF] Nacher | Improving Pre-Kindergarten Touch Performance[END_REF] with high success rates.

Vatavu et al [START_REF] Vatavu | Touch interaction for children aged 3 to 6 years : Experimental findings and relationship to motor skills[END_REF] evaluated touch gestures (tap, double tap, drag & drop, multiple drag & drop) with children aged between 3 and 6 years on tablets and smartphones. Their results showed that although all children had high success rates, there was a significant performance increase with age in terms of success rate and time spent performing the gesture which is an expected behavior.

These works seem to conclude that pre-kindergarten children have the necessary skills to make use of multi-touch technology. However, they assume that children may not accurately perform the multi-touch gestures under consideration and always implement assistive techniques to deal with precision issues during the initiation and termination phases of each gesture. This results in interaction styles in which prekindergarteners do not have the control over the termination of the gestures despite some of them have the proper cognitive abilities to perform the gestures with higher levels of precision. As a result, existing applications designed under these as sumptions do not benefit from the use of multi-touch technology to help children to develop their precision-related cognitive and motor skills. Hence, in this work we evaluate the drag, scale up, scale down and rotation gestures when accurate termination of the gestures is required. The goal is to gain additional knowledge about precision issues when pre-kindergarten users are considered in the design of future touch -based applications. The results of this work would allow the design of applications that provide assistive strategies to deal with precision issues in an adaptive way only for less skilled children and not in an exhaustive way for every child as current systems do.

Experimental study

The overall goal of our experimental study is to identify whether pre-kindergarten children are able to get high success rates when performing gestures with high accuracy levels. More specifically, the research questions of the study are formulated as follows: When high accuracy touch gestures are considered… RQ1: …is the degree of success independent of age group? RQ2: …is the degree of success independent of gender? RQ3: …is the completion time independent of age group? RQ4: …is the completion time independent of gender? RQ5: …is the average error independent of age group? RQ6: …is the average error independent of gender?

Participants

Forty children aged between 25 and 38 months took part in the experiment (Mean (M) = 31.60, Standard Deviation (SD) = 4.32). Children were balanced in gender and in age group, i.e., two age groups 24 to 30 months and 31 to 38 months, with 10 males and 10 females per group were configured. Participants from two Spanish nursery schools were involved in order to explore whether children could perform non static touch gestures, i.e. requiring the movement of contacts across the surface. Parental consent was obtained before carrying out the study.

Apparatus

The interaction framework for the experiment was implemented in Java using JMo n-keyEngine SDK v.3.0beta. The devices used for the experiment were a Motorola MZ601 and a Samsung Galaxy Note 10.1 tablet with Android 3.2 both with capacitive multi-touch screens.

Tasks

Drag.

A static image of an animal appears in a random position on the screen and the same (reference) image appears in a white profile in another random position, always at a distance of 588 pixels so as to be able to compare execution times among the different subjects (Fig. 1-a). The random position of the reference image is subject to some geometric restrictions, to make sure that it is completely visible on the surface. Participants are requested to drag the target to the reference image with one finger. The task is successful when the target image reaches the location of the reference image with a precision of less than 15 pixels on both X and Y axis when the subject lifts his/her hand (like a drag and drop gesture).

Rotation.

A static image of an animal appears in the center of the screen in front of a blank profile of the same image in a different orientation. Rotation is always clockwise to a fixed position so as to be able to compare interaction execution times among subjects (see Fig. 1-b). Participants are requested to rotate the target image to the position of the reference image by dragging one finger around the center of the target image. Pressure can be applied on the target image itself or anywhere around it. The task is successful when the target image reaches the orientation of the reference image with a precision of less than 5 degrees when the subject lifts his/her hands.

Scale up.

A static image of an animal with a size of 5 centimeters appears in the center of the screen within a similar but 1.5 times larger reference shape (see Fig. 1-c). Participants are requested to scale up the target image to the size of the reference shape. This can be done by expanding the distance between two fingers of either one hand or two hands. The fingers do not have to be in contact with the reference image and the sca ling factor applied is the incremental value returned by the JMonkeyEngine runtime for this gesture. If more than two contacts are made on the surface, JMonkeyEngine considers only the two most recent ones for communicating scaling events. The task is successful when the size difference between the manipulated and the reference images is less than 5% when the subject lifts his/her hands. A static image of an animal with a size of 15 centimeters appears in the center of the screen superimposed on a similar reference shape half its size (see Fig. 1-d). Participants are requested to scale down the target image by making the target object shrink until it reaches the size of the reference image using two fingers of either one or two hands. The task is successful when the size difference between the manipulated and the reference images is less than 5% when the subject lifts his/her hands.

Procedure

For each task, the children participated in a 2-minute learning session with an instructor. Then, the experimental platform asked them to perform the task with no external adult intervention. They had to perform five repetitions of each gesture as described in the Tasks section. When the gesture was successfully completed, the platform gave a positive audiovisual feedback. If the instructor observed that the participant did not carry out the task in a given time, it was marked as unsuccessful and the child went on to the next one. For each interaction, the system recorded the start time (seconds needed to go into action after the test began), completion time (milliseconds until the gesture was completed), success (performed correctly or incorrectly), and t he number of times that users lift their hands while performing the gesture. Additional notes were taken by an external observer for posterior analysis.

Results

Success

In order to aggregate the success variable over the five repetitions, the variable was expressed as a percentage according to the number of repetitions performed succes sfully (Table 1 The results of a two-way between-subjects ANOVA on the success revealed a significantly main effect of the age group factor in the scale up (F(1,40) = 8.052, p = .007), scale down (F(1,40) = 10.913, p = .002) and rotation (F(1,40) = 5.930, p = .020) tasks but not in the drag task (F(1,40) = .951, p = .336). The statistical analysis of the gender factor only revealed a significant effect in the rotation task (F(1,40) = 10.104, p = .003).

Completion time

The average of each subject's successful tasks is used to obtain the completion time aggregated by users (see The conducted two-way between-subjects ANOVA on the completion time revealed significant main effects of the age group factor for the drag task (F(1,36) = 7.844, p = .009) where the older group performed significantly faster. No other effects were found significant for any gesture and factor.

Accuracy

In order to evaluate pre-kindergarten performance when accurate termination of the gestures is required, in this section the error values for each gesture by age group and gender are showed. For each task the error is calculated as the discrepancy between the reference and the manipulated elements. In the case of the drag gesture the error is measured as a distance in pixels between them, for the scale-up and down gestures the error is a percentage measuring the discrepancy of size and for the rotation gesture the error is measured in degrees as the difference between their rotation values. On the one hand, the results show that both age groups have similar levels of accuracy when terminating the drag gesture, i.e., similar average error (F(1,39) = .179, p = .675) with avg_error(drag , young)=10.75px and avg_error(drag , old)=8.86px).

On the other hand, the results show that in the scale (up & down) tasks the old er group had almost a 50% greater precision than the younger one (F(1,72) = 10.885, p = .002) with avg_error(scales , young)=20.84% and avg_error(scales , old)=10.74%. This is also the case for the rotation task in which older children have a 50% greater precision (F(1,37) = 6.497, p = .016) with (avg_error(rot,young)=29.7º and avg_error(rot,young) =12.06º). Finally, with respect to the gender factor no significant differences were found for any task.

Discussion and future work

There are several interesting conclusions that are obtained when considering multitouch gestures with high-levels of termination precision. Firstly, the results reveal that age is the main factor affecting the degree of success variable (RQ1) for all tasks except for the drag gesture. In this specific gesture all children, no matter what age, achieve similar high success rates ranging from 73% to 84%. However, for more complex tasks such as rotations and two-finger scaling, the additional requirement of precise termination of the gesture has a high impact on less than 30 months children. Success rates decrease for this age group (27%-47%) and are significantly better for children aged 31 months and over (57%-73%). This result confirms that children start to develop their fine motor skills at this age [START_REF] Nacher | M ulti-touch gestures for pre-kindergarten children[END_REF] and, therefore, applications requiring higher levels of precision could be designed to stretch and challenge children in the second age group. This would be in concordance with the principles of differentiated instruction [START_REF] Subban | Differentiated instruction: A research basis[END_REF]. Secondly, it was also observed that girls are on average more succes sful than boys when precision is an issue (RQ2). These results are consistent with previous work which shows superior fine motor skills in girls [START_REF] Reikerås | M otor-life-skills of toddlersa comparative study of Norwegian and British boys and girls applying the Early Years M ovement Skills Checklist[END_REF]. However, our study only revealed significant differences for the rotation task. This is because the add itional difficulty associated to the coordination of two finger contacts makes the scaleup/down tasks specially challenging for both boys and girls when precision is required (see Table 1).

In addition, if completion time is considered (RQ3 & RQ4), no significant differences were found in terms of age nor gender for all tasks except for the drag gesture. This means that relatively challenging actions in terms of cognitive and motor skills such as the scale-up/down and rotation gestures are performed by all children at similar speeds. It was observed that the additional precision requirement in these gestures forced all children, no matter their age or gender, to perform the final phase of the interaction (contact release phase) repeatedly until the final successful completion of the gesture was achieved. This was not the case for the drag gesture (see Fig. 2), specially perceived by older children (aged 31 months and over) as an easy to perform action they were able to complete with a lower number of attempts and, thus, resulting in significantly lower completion times.

Finally, when accuracy is considered (RQ5), the analysis of the average errors reveals that the most challenging gestures (scale up-down and rotation) are performed with significantly higher levels of accuracy (lower average error) by older children (see section 4.3). This observation brings up the matter of us ing assistive strategies to deal with precision issues in an adaptive way according to the actual motor skills of each child and not, as most existing touch-based applications for pre-kindergarten children currently implement, in a comprehensive way assuming all children have the same levels of accuracy.

The size of the objects involved in the experiments may have an impact on the effectiveness; hence, future research should be done to evaluate different sizes.

To sum up, the previous results point out that pre-kindergarten children (2 to 3 years old) are in the process of developing their motor skills and have different levels of accuracy. Particularly, older children are able to perform complex gestures with significantly higher levels of accuracy and, therefore, future multi-touch application for this age range should consider assistive strategies that adapt their behavior to the actual levels of motor and cognitive development of each child. Not doing so, would prevent the more skilled children from exercising and further enhancing their precision related skills at an early phase of their development.

Fig. 1 .

 1 Fig. 1. Example tests: (a) drag (b) rotation (c) scale up (c) scale down

Fig. 2 .

 2 Fig. 2. Pre-kindergarten child performing the drag task.

Table 1 .

 1 Success rate of each task by group.

	).
	Task	Gender		Age Group	Overall
	Drag	F 82	M 75	<=30 73	>30 84	78.50
	Scale up	F 46	M 38	<=30 27	>30 57	42
	Scale down	F 48	M 43	<=30 27	>30 64	45.50
	Rotation	F 77	M 43	<=30 47	>30 73	60

Table 2 )

 2 . The unsuccessful tests were not included in the completion time analysis.

	Task	Gender	Age Group	Overall
	Drag	F 13721.63	M 13793.40	<=30 16922.11	>30 10588.94	13755.53
	Scale up	F 9517.72	M 10402.34	<=30 8151.86	>30 11586.09	9991.62
	Scale down	F 10109.92	M 9591.46	<=30 7732.45	>30 11361.15	9859.62
	Rotation	F 16972.38	M 16239.20	<=30 17751.49	>30 15681.13	16651.61

Table 2 .

 2 Completion time in miliseconds of each task by group.

Acknowledgments

Work supported by the MINECO (grants TIN2010-20488 and TIN2014-60077-R).