Mohammad Obaid
email: mobaid@chalmers.se

Andreas Dünser

Elena Moltchanova

Danielle Cummings

Johannes Wagner

Christoph Bartneck

LEGO Pictorial Scales for Assessing Affective Responses

Keywords: LEGO Minifigures, Evaluation, Pictorial, Emotion, Scale 1

This article presents the design and evaluation of novel types of pictorial scales for assessing emotional response based on LEGO Minifigures. We describe the creation of two pictorial scales (LEGO Face Scale and Stylized LEGO Face Scale) through the use of a semi-automatic process. We report on the results of two evaluation studies conducted to assess the validity of the proposed pictorial scales. The first study evaluated the rating of emotions expressed by other humans; the second focused on rating one's own emotional state when looking at expressive stimuli. We investigate the validity of the two pictorial scales by comparing them to ratings given on a conventional Likert Scale. Results show that assessing expressive faces using the proposed pictorial scales can differ from using a Likert scale; however, when rating one's own emotional state there is no difference. Finally, we assembled a physical version of the LEGO Face scale and discuss future work.

Introduction

Emotional assessment plays an important role in the field of Human-Computer Interaction (HCI) as it provides an understanding of the user's state and emotional response when interacting with physical/digital content of a system. Capturing a person's emotion can be an important factor informing the design or re-design of a product. One of the main methods used to assess emotions is by having a person express them verbally or through a questionnaire. These questionnaires mostly ask the person to pick from a written set of emotions and rate the intensity, for example, on a Likert scale. This is supposed to capture the person's current emotional state, an experienced emotion, the emotion of somebody else or the emotional loading of an object. Textbased (non-pictorial) scales require a person to read through a list of possible answers and pick the most appropriate alternative. This can be accompanied by rating the intensity of their choice on a numeric scale. This approach has certain disadvantages such as being cognitively demanding and time consuming. Pictorial scales have been found to be less demanding with respect to time and effort. For example, Desmet et al. [START_REF] Desmet | Development and Application of a Pictorial Mood-Reporting Instrument[END_REF] found in their studies that "subjects are embarrassed when asked to express their emotional response to products. There are several explanations to this phenomenon. First, emotions are difficult to verbalize, especially the type of subtle, low intensity emotions by products". In addition, they explain it further stating that "asking users to describe their emotional responses will require cognitive involvement, which may influence the response itself". Moreover, Vastenburg et al. [START_REF] Vastenburg | Pmri: Development of a Pictorial Mood Reporting Instrument[END_REF] state, "Most instruments […] rely on verbal self-reports. Such questionnaires and scales tend to be rather demanding for the respondent."

Emotions can be difficult to verbalize, especially low intensity emotions [START_REF] Desmet | Designing Products with Added Emotional Value: Development and Application of an Approach for Research Through Design[END_REF]. Asking people to describe their emotions requires cognitive involvement, which could impact the response itself. Therefore, tools that let people express or rate emotions without the use of text can be preferable. The most efficient way of representing emotions is with facial features, because facial cues are used as a main means for judging emotional information even though other sources can provide more accurate information [START_REF] Ekman | The Repertoire of Nonverbal Behavior: Categories, Origins, Usage and Coding[END_REF][START_REF] Isomursu | Experimental Evaluation of Five Methods for Collecting Emotions in Field Settings with Mobile Applications[END_REF]. Thus, in this paper we propose a novel assessment tool based on LEGO Minifigures. The goal and main contribution of this paper is the design and evaluation of a new tool that allows us to rate emotions by having people choose a specific Minifigure which best represents their emotional state or reaction.

There are nearly 6000 different Minifigures that have been designed since 1975 [START_REF] Bartneck | The Unofficial LEGO Minifigure Catalog[END_REF]. Whereas most other pictorial scales are based on a small number of drawings created for a specific purpose (e.g. [START_REF] Desmet | Designing Products with Added Emotional Value: Development and Application of an Approach for Research Through Design[END_REF][START_REF] Bieri | The Faces Pain Scale for the Self-Assessment of the Severity of Pain Experienced by Children: Development, Initial Validation, and Preliminary Investigation for Ratio Scale Properties[END_REF][START_REF] Bradley | Measuring Emotion: The Self-Assessment Manikin and the Semantic Differential[END_REF][START_REF] Visser | Which Graphic Symbols Do 4-Year-Old Children Choose to Represent Each of the Four Basic Emotions?[END_REF]), our approach was to select suitable facial expressions representing specific emotions from a large pool of LEGO Minifigures. This overcomes a potential shortcoming of other pictorial scales for which the initial sample of available drawings for each scale point might have been too small.

In the following sections we discuss related work, the development of the pictorial LEGO Minifigure scales, report on two experimental studies that evaluate the validity of the scales, and discuss our findings and future work.

Related Work

Although the perception of emotions has been shown to be culturally independent [START_REF] Ekman | Constants Across Cultures in the Face and Emotion[END_REF], words can be sensitive to different interpretations across cultures or across individuals from the same culture. Scales relying on written language rather than pictorial content require users to understand the language in order to be able to interpret the words correctly. Many verbal scales are not suitable for use across different cultures.

In addition, verbal scales may pose problems for people who cannot properly read written text, including young children. Age related lower levels of verbal fluency or development related variations in understanding of test questions are important factors to take into account [START_REF] Bieri | The Faces Pain Scale for the Self-Assessment of the Severity of Pain Experienced by Children: Development, Initial Validation, and Preliminary Investigation for Ratio Scale Properties[END_REF]. Also, using numeric rating scales requires respondents to map their own concept of an emotion and its intensity onto another dimension. A judgment has to be made by translating the personal experience to numerical values, or a certain distance between end points represented by the scale.

Visser et al. [START_REF] Visser | Which Graphic Symbols Do 4-Year-Old Children Choose to Represent Each of the Four Basic Emotions?[END_REF] studied which graphic symbols are perceived by four-year olds as best representation of the four emotions: happy, sad, afraid, and angry. For each emotion they selected four graphic depictions of faces taken from three different sources (Picture Communication Symbols, PICSYMS, and Makaton; see [START_REF] Visser | Which Graphic Symbols Do 4-Year-Old Children Choose to Represent Each of the Four Basic Emotions?[END_REF]). They found that happy, the only positive or pleasurable emotion was a relatively easy emotion to represent in a graphic symbol. Angry was more difficult and sad and afraid were found to produce the least consensus in terms of expected responses to these emotions. This suggests that recognizing some emotions (e.g. angry) may be more susceptible to individual influences, as there is a variety of ways that different people in different contexts can express them.

The Self Assessment Manikin (SAM) is a pictorial scale to measure people's ratings of three independent affective dimensions: pleasure, arousal, and dominance. It is used to measure a person's affective reaction to situations, stimuli or events [START_REF] Bradley | Measuring Emotion: The Self-Assessment Manikin and the Semantic Differential[END_REF]. The SAM was developed to overcome issues with verbal scales such as the required time, effort and the need to be able to interpret written language. The scale uses graphical depictions of a Manikin to be rated by people. Each of the three dimensions has five figures representing different intensity levels. The pleasure dimension ranges from a happy to an unhappy figure. The arousal dimension ranges from an excited figure to a relaxed figure, whereas the dominance dimension changes the size of the manikin. Bradley et al. [START_REF] Bradley | Measuring Emotion: The Self-Assessment Manikin and the Semantic Differential[END_REF] compared people's responses to a set of 21 pictures (International Affective Picture system) using SAM (3 ratings) or a Semantic Differential (requiring 18 ratings). They found that correlations between the two rating methods were high for pleasure and arousal. Differences in the dominance dimension suggest that SAM may better track a person's response to an affective stimulus. They argue that SAM may lead to more consistent judgments because the figure is human-like.

Emocards [START_REF] Desmet | Designing Products with Added Emotional Value: Development and Application of an Approach for Research Through Design[END_REF] is a method that uses 16 cartoon faces showing eight different emotional expressions (one each for each gender). The expressions are combinations of two dimensions pleasantness and arousal. A pleasant emotion with high levels of arousal for example is euphoric, whereas an unpleasant emotion with high arousal is annoyed. The emotions depicted by the faces in Emocards are the ones most frequently elicited by products. Desmet et al. [START_REF] Desmet | Designing Products with Added Emotional Value: Development and Application of an Approach for Research Through Design[END_REF] argues that the method can be used as an aid to objectify emotional responses to a product and as an aid for starting conversation between participants and researchers or designers.

Isomursu et al. [START_REF] Isomursu | Experimental Evaluation of Five Methods for Collecting Emotions in Field Settings with Mobile Applications[END_REF] presented a comparison of five self-report methods for measuring emotional responses to mobile applications. The comparison included SAM, Emocards, and three self-created tools (Experience Clip, Expressing Experiences and Emotions, and Mobile Feedback Application). They developed a comparison framework and showed that all of these methods can be successfully used for collecting emotional responses, all with certain advantages and disadvantages. SAM results, for example, correlated well with results obtained from other methods, however, they found that the SAM scales were not easy to interpret for participants. For some participants it was not clear whether to evaluate their own state or that of the application. Likewise for dominance, it is not clear whether the participant or application had to be judged. With Emocards some participants complained that they could not find a picture to represent their emotional state.

Pollak et al. [START_REF] Pollak | A Photographic Affect Meter For Frequent, In Situ Measurement of Affect[END_REF] presented the Photographic Affect Meter (PAM), which is an affect tool that measures affect on the valence and arousal dimensions. It allows the user to select their mood from a set of pre-defined pictures that are obtained from the Flickr API. Based on several tests Pollak et al. identified the most common userselected photos, and defined the arrangement of the tool in a 4x4 grid layout. Their results confirmed the validity of the proposed PAM tool.

Scales based on real faces often use actors for expressing the respective emotion. But actors might portray emotions differently and more intensively than nonprofessionals [START_REF] Grimm | Evaluation of Natural Emotions Using Self Assessment Manikins[END_REF]. However, recent studies by Aviezer et al. [START_REF] Aviezer | Body Cues, Not Facial Expressions, Discriminate between Intense Positive and Negative Emotions[END_REF] suggest that, in real world situations, people cannot distinguish between intense positive and intense negative emotions expressed through facial expressions alone but only in conjunction with body cues. Thus, for scales showing faces only, depicting exaggerated emotions might ensure better discriminability for low as well as highly intense emotions.

Another limitation of scales using photographs or drawings of faces that express emotions is that they can potentially introduce gender bias [START_REF] Desmet | Designing Products with Added Emotional Value: Development and Application of an Approach for Research Through Design[END_REF]. Authors can address this by generating both male and female faces, or by using gender natural faces.

We propose a measurement scale based on LEGO Minifigures that can be used by a range of different people; the nature and the wide use of LEGO allow it to be popular amongst both adults and children [START_REF]A Short Presentation[END_REF]. With pictorial scales, respondents still need the cognitive ability to match pictures of facial expressions with a specific emotion, but they do not need to be able to count or use numbers in a categorical way [START_REF] Hicks | The Faces Pain Scale -Revised: Toward a Common Metric in Pediatric Pain Measurement[END_REF] or be able to express actual emotions and intensities in abstract entities (i.e. numbers). The ability to distinguish between facial patterns expressing basic emotions is developed by the age of 4 to 5 years [START_REF] Bieri | The Faces Pain Scale for the Self-Assessment of the Severity of Pain Experienced by Children: Development, Initial Validation, and Preliminary Investigation for Ratio Scale Properties[END_REF]. Pictorial scales using faces are particularly suitable for children and preferred by children because they are concrete measures and easier to use [START_REF] Hicks | The Faces Pain Scale -Revised: Toward a Common Metric in Pediatric Pain Measurement[END_REF]. In addition, the value of the proposed scale is to communicate the user's response on emotional stimuli and it is not limited to one context. Potential uses in HCI related contexts maybe evaluating emotional responses to new designs of physical artefacts and hardware interface as well as software interfaces.

We base our design on the well-established theory of Ekman [START_REF] Ekman | Unmasking the Face[END_REF] whose work identified six basic emotions. Other tools use a (slightly) different approach (e.g. SAM is based on pleasure, arousal and dominance; PAM measures affect on the valence and arousal dimensions; Emocards is based on pleasantness and arousal), providing arguably different angles on assessment. Thus, the main contribution of this work is the introduction of an orthogonal emotional evaluation scale using LEGO Minifigures that is based on the six expressions of Ekman and five intensities for each emotion. Our intention is to expand the toolbox for researchers interested in emotion assessment with a novel tool. In addition, the value of using LEGO Minifigures is that they are physically available and can introduce another dimension to an evaluation tool. Allowing people to pick a physical figuring that corresponds to an emotion is a unique and novel approach, which we intend to explore this in our ongoing work. In the following sections we describe how we have developed and evaluated two new pictorial scales for assessing emotions based on LEGO Minifigures.

Scale Development

The goal of this research is to develop a new measurement tool for emotions that will allow subjects to easily rate the emotional value of stimuli as well as their own emotional state. To accomplish this, we developed two scales based on the design of LEGO Minifigures. Our scales are based on a sample of 722 facial expressions available through the LEGO Minifigures. These expressions were rated in a previous study that investigated the change of emotional expression of 722 LEGO Minifigures released between 1975 and 2010 [START_REF] Bartneck | Agents with Faces -What can we Learn from LEGO Minfigures[END_REF]. This study discovered that the variety of facial expressions increased considerably since 1989. Not surprisingly the two most frequent expressions were happiness and anger, however, the proportion of happy faces was decreasing over time. To collect this data we used Amazon's Mechanical Turk (MT) 1 for recruiting participants and administering the questionnaire.

The participants were shown one of the 722 LEGO Minifigure pictures at a time and the task was to rate the type and intensity of the expressed emotion from 1-5 (weak-intense). The emotional types to choose from were anger, disgust, fear, happiness, sadness, and surprise [START_REF] Ekman | An Argument for Basic Emotions[END_REF][START_REF] Ekman | Unmasking the Face[END_REF]. For the scale development, 264 adult participants rated on average 82.05 faces each.

In a follow-up study [START_REF] Bartneck | Press) Comparing the Similarity of Responses Received from Studies in Amazon's Mechanical Turk to Studies Conducted Online and with Direct Recruitment[END_REF], we evaluated the validity of gathered data via MT compared to gathered data via regular questionnaires. A subset of 96 faces was chosen and ratings received through MT, face-to-face questionnaire and online questionnaire respectively were compared. Our findings showed no substantial differences in the responses, suggesting that MT is a viable option for conducting experiments. These results served as a basis for choosing MT as a tool for rating emotion. We note, however, that although these ratings are based on subjective judgments from many people, this does not establish a "ground truth". This means that we cannot state that a particular face actually depicts a certain emotion, but that it was rated as showing a particular emotion with a certain frequency. However, this is similar to other scales that have been created based on people's subjective judgments.

Our results are in line with other research that has shown MT to be a viable option for conducting experiments of this type and that the quality of the results obtained were similar to conventional methods at much lower costs [START_REF] Buhrmester | Amazon's Mechanical Turk: A New Source of Inexpensive, yet High-Quality Data?[END_REF][START_REF] Goodman | Data Collection in a Flat World: Accelerating Consumer Behavior Research by using Mechanical Turk[END_REF][START_REF] Paolacci | Running Experiments on Amazon Mechanical Turk[END_REF].

Based on our previous research we have developed a scale covering the emotions of `Anger', `Disgust', `Fear', `Happiness', `Sadness', and `Surprise' with 5 intensity levels each.

Item Selection

We used a semi automatic algorithm to find the most representative faces for each emotion type and intensity combination. For example, a participant can rate a LEGO Minifigure as `anger' with intensity 5. At the same time the algorithm tries to maximize discriminability between categories. For example, a face that was frequently rated as "intense anger" but also often as "intense happy" would be a less suitable candidate than a face with similarly high number of "intense anger" ratings but less clear ratings on one or more particular other emotions.

For each LEGO Minifigure, two statistics were determined: the emotion-intensity category (for example, "intense anger") that is most frequently associated with the face, and the difference between the frequency of that association and the next most frequent association. For example, for the distribution shown in Table 1, the highest frequency is 9/30, for happiness-3, and the next highest frequency is 5/30 for happiness-1 and happiness-2, so the two statistics of interest are 9/30 and 9/30-5/30=4/30.

The two statistics are naturally correlated; the more often a face is associated with any particular emotion, the less often is it associated with any other, making the distance to the next most frequently selected emotion greater. A face which is consistently associated with a single emotion-intensity category will have values 30/30=1 and 30/30-0=1 respectively (the largest theoretically possible), whereas a face which has been associated equally with all the 30 emotion-intensity categories will have values 1/30 and 0 respectively (the smallest theoretically possible). For each emotionintensity category, five candidate Minifigure faces were chosen to represent this category and no other. After that, the best option was selected manually from these candidates. Our choice was guided by two requirements: a face can only appear once in the whole scale and the faces should not be alien (i.e. animal or sci-fi like). Some faces were among the best five options for multiple scale points, in particular for low intensity emotions. A certain face might, for example, be equally suitable for sadness at the intensity level two or three. We selected the most suitable unique face for every scale point. From a design perspective, it is reasonable to give the entire scale a consistent look. Thus, we do not include faces of aliens or that have artefacts such as sunglasses.

Stylization

Many LEGO Minifigure faces have certain features that might not be necessary for the communication of emotions, for example, slight shadows on the sides or slight variations in the color of the skin. Moreover, in certain situations it might not be advisable to present colorful faces. Reproducing color consistently across different devices such as screens, printers and projectors remains a difficult task requiring a full color synchronization process and color calibration for each device. To avoid these problems, we developed a stylized version of the LEGO faces that exclude unnecessary details and use gray scale colors. Moreover, transforming the LEGO faces to vector graphics enables users to scale the faces to any desired size without any loss of visual quality. A professional designer used the original LEGO faces as templates to draw stylized faces using a maximum of four gray scale colors: white, light gray, dark gray and black. The stylized faces were created using vectors rather than a bitmap and are therefore easily scalable. The vector graphics can be transformed to bitmap graphics at any desired size. Bitmap graphics are more commonly used in the World Wide Web, such as in the JPEG, PNG or GIF file format.

First Evaluation Study

We

4.1

Stimuli

The 213 pictures of the JAFFE Database [START_REF] Lyons | Coding Facial Expressions with Gabor Wavelets[END_REF] were used as the stimuli for our experiment. All the faces were used for each of the three conditions, resulting in (213x50x3) 31.950 responses. In the LFS condition we used the images of the LEGO faces and in the SFLS condition we used the stylized LEGO faces. In the third condition, we used a five point Likert scale. Fig. 1 shows the three scales, where the emotions presented in the scale's rows of Fig. 1(A) and Fig. 1(B) are (from the top) anger, disgust, fear, happiness, sadness, and surprise.

Method

A between-subject design was used to perform the experiment in which we used three different measurement tools as our three experimental scales: LFS, SFLS and Likert. We used MT to recruit participants and to execute the study. The participants were asked to rate what emotion each stimulus expressed using the measurement tools presented in Fig. 1. The experimenter posted the questionnaires as tasks within the MT environment, where the Mechanical Turk Users (MTU) could then answer as many questions as they desired. Fifty different participants rated each stimulus, where they could stop at any time. This means that it would have not been the exact same 50 participants rating each and every stimulus. This plays an important part in the selection of an appropriate statistical test, as described in the results section.

To protect the privacy of MTUs, we were not allowed access to demographic data; hence it is not available for this study. However, we can be relatively certain that all participants are adults since minors are not allowed as MTUs. Previous surveys on the population of MTUs reveal that MTUs from the US tend to be well educated, young, have moderately high incomes, and are roughly equally male and female [START_REF] Ipeirotis | Demographics of Mechanical Turk[END_REF][START_REF] Ross | Who are The Crowdworkers? Shifting Demographics in Mechanical Turk[END_REF].

Second Evaluation Study

In this study we present standardized stimuli that are intended to induce physiological states that manifest themselves in physical reaction. Participants were asked to rate their reactions using the three measurement tools (LFS, SLFS, and Likert). For this study we directly recruited participants instead of using Amazon's MT. Section 5.1 describes how the stimuli images were selected and Section 5.2 outlines the method used to evaluate the proposed measurements tools.

Stimuli

In order to induce physical responses we used images from the standardized International Affective Picture System (IAPS) as stimuli [START_REF] Lang | International Affective Picture System (IAPS): Technical Manual and Affective Ratings[END_REF]. The corpus of 1182 IAPS images comes with a table of ratings collected from a group of 100 college students (50 male, 50 female). These students were asked to rate the images based on the Self-Assessment Manikin (SAM); however, the images do not have an associated label that relates to one of the six basic emotions identified by Paul Ekman: happiness, surprise, fear, sadness, anger, and disgust combined with contempt [START_REF] Ekman | An Argument for Basic Emotions[END_REF][START_REF] Ekman | Unmasking the Face[END_REF]. We reviewed the set of 1182 images and selected 50 that we believed to be the best candidates for eliciting the six emotions. We selected stimuli images with the highest arousal ratings (positive and negative) within their category and further edited the selection based on the content. We chose to limit our subset to 50 images based on the suggested limit in [START_REF] Cummings | Multimodal Interaction for Enhancing Team Coordination on the Battlefield[END_REF], in which 50 images was the approximate average number that participants were willing to evaluate in one sitting.

To verify the use of the 50 stimuli images and to ensure that we would obtain a sufficient variety of responses, we decided to perform a labeling pre-study to evaluate the reaction to the 50 images. We wanted to determine the overall feelings associated with our images as well as the level of intensity related to those feelings. The labeling pre-study involved 24 participants: 15 men and 8 women of ages 22 -44 years, from eight countries. The labeling pre-study allowed participants to view each of the 50 images for a fixed time and then immediately presented them with an evaluation screen that allowed them to select one of six emotions and a level of intensity related to the emotion selected. The data obtained from this study verified that the 50 selected images consisted of a near equivalent number of positive and negative images with varying levels of impact as shown in Table 2.

In a follow-up interview, we asked participants about their overall emotional response to the images. Most stated that they were not overly disturbed or offended by the images shown. No one felt the need to end the study due to discomfort.

Label

Method

We developed a system to collect participants' responses on the set of stimuli images using the three different scales shown in Fig. 1. The system displays an image for five seconds; followed by a screen where the participant can identify one of the six emotions that they feel best describes their response to the image. A blank five-second countdown screen was used after each rating screen to prepare participants for the next stimulus image. Fig. 2 shows the sequence of events when doing the study.

The study involved 14 participants: nine male and five female of ages from 23 -35. For each participant, the set of 50 images was randomly divided into three subsets (A, B, and C) as shown in Fig. 3; the number of images in the subsets were 16, 17, and 17 respectively. Each subset was then assigned to one of the three scale types of the study. We counter balanced the scale type-to-subset assignment for participants.

Statistical Methods

The emotion, selected by a participant to be associated with a given image, can be modeled as a categorical variable: Anger, Disgust, Fear, Happiness, Sadness and Surprise. Multinomial distribution is the common way to model such outcomes; however, it is difficult to summarize since it is, in effect, a multivariate distribution. In order to assess the consistency of scaling, the entropy statistic was calculated:

𝑒𝑛𝑡𝑟𝑜𝑝𝑦 = - 𝑝 ! log (𝑝 ! ) ! !!!
where 𝑝 𝑖 is the proportion of respondents who have categorized the given image to emotion 𝑖. In our case, entropy equal to 1.79 would correspond to 𝑝 𝑖 = 1/6∀ 𝑖 , i.e., a distribution obtained by random chance, and 0 corresponds to the case where all the 50 respondents have selected the same emotion. In order to explore pairwise agree-ment between scales, a sum of squared differences in proportions was calculated for each image, where 𝑝 !" is the proportion of respondents who have classified the image to emotion 𝑖 on the scale 𝑘. This was selected rather than, for example, 𝑥2 -𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐, which is inapplicable if zero-counts occur in any of the categories.

Mixed-Effects Multinomial Logistic Regression

To test for the effect of the scale on the frequency distribution of the emotions assigned by respondents to each image, a mixed-effects multinomial logistic regression model was fitted. This is a standard choice for nominal response data (e.g. [START_REF] Hedeker | A Mixed-Effects Multinomial Logistic Regression Model[END_REF]). We have chosen to implement the model within Bayesian framework, using the Win-BUGS software [START_REF] Lunn | WinBUGS -A Bayesian Modeling Framework: Concepts, Structure, and Extensibility[END_REF]. The discussion of Bayesian inference is beyond the scope of this article and an interested reader may refer to, for example [START_REF] Gelman | Bayesian Data Analysis[END_REF] for more information; it is, however, becoming more and more widely used due to its flexibility with regard to model formulation and assumptions.

To test if scale matters, the above model was fitted with/without the scale taken into account. The two models were then compared using the Deviance Information Criterion (DIC) [START_REF] Spiegelhalter | [END_REF]. DIC can be viewed as a Bayesian version of the Information Criterion (AIC), which is widely used in classical statistics to compare goodness-offit of non-nested models [START_REF] Akaike | Information Theory and an Extension of the Maximum Likelihood Principle[END_REF]. The smaller the value of DIC, the better the model, and, according to the authors, differences between 5 and 10 are substantial, while differences of more than 10 definitely rule out the model with the higher DIC 2 . In addition, for the second evaluation study, a Bayesian mixed-effects multinomial logistic regression model was again implemented in WinBUGS (5000 burn-in and 5000 monitored iterations). The model structure accounted for the fact that each participant rated each of the fifty images, as well as for the order in which the images were presented. Win-BUGSs was used to implement both models and. convergence was assessed visually.

Results

The distribution of observed entropy by scale and dominant emotion (defined here as the emotion most frequently selected by the respondents for a given image) is shown in Fig. 4 On average, respondents using the Likert scale tended to agree more than those using the other two. However, a large spread should be noted. For some images, the response distribution was not far from that obtained by responding at random. The plot of squared differences in estimated proportions in Fig. 5 shows that the distribution of responses obtained from using LFS and SLFS generally agreed across the emotion range, but both disagreed with the response distribution on the Likert scale. The estimated multinomial probability distributions of assigning a random image to one of the six emotions under each scale in the evaluation studies one and two are shown in Fig. 6 and Fig. 7 respectively. These are adjusted for random and a pooled distribution is shown in each case as well. One can see a significant difference in the 4. DIC comparisons between the models where order is taken into account and where it is not for the second evaluation data. Smaller DIC indicate a better model. There is therefore some evidence that order does have an effect on the assessment. Also, the pooled model has significantly smaller DIC values whether the ordering is taken into account or not, demonstrating lack of statistical evidence for the scale effect.

Scales

proportion of the images marked as `angry' between the three scales, increasing from LFS to SLFS to Likert, with the opposite trend in `disgust'.

For the second study, about 35% of the images appear to have been assigned to the `happy'-category with the rest of the probability mass being almost equally distributed between the other five emotions. The ΔDIC values expressing the possible scalespecific effects in evaluation studies one and two are shown in Table 3. In each case the model with scale-specific probabilities of emotion selection was compared to the model where scales were assumed to have identical probability distributions. A negative ΔDIC means that the pooled model was better than the scale-specific one and thus that no statistical evidence for the effect of scale has been found. The results for evaluation study one show that while for LFS and SLFS the distribution of responses, adjusted for random image-specific effects, is the same, there are significant pairwise differences between LFS and Likert as well as between SLFS and Likert scales. These differences are large enough to justify the scale-specific rather than pooled model overall (ΔDIC=1315.00). For the second study, however, the pooled model was always found to be statistically better since the DIC was found to be always smaller for the pooled model (see Table 3). For the second evaluation study, the possible effect of the order in which the images were presented was also tested and the results are presented in Table 4. The smallest DIC is returned for the pooled model adjusted for order (DIC=1763.52). The difference with the DIC of the pooled model not adjusted for order, however, is small (ΔDIC=2.23) indicating only slight possible effect.

Discussion

We have conducted two studies to evaluate the proposed pictorial scales. First we compared the two pictorial scales to a Likert scale when judging emotions of others. The task was to judge images with facial expressions of different emotions with the three scales. This study was conducted using the Amazon Mechanical Turk service, which allowed us to gather a large amount of data for our evaluation purposes.

As a measure of reliability we calculated entropy, where the smaller the entropy, the better the respondents agree in their ratings of an emotion that a specific image depicts. This indicates that the respondents on average agree more when using the Likert scale than when using the pictorial scales. Happiness shows most agreement, however, the variation of the entropy measure for the different pictures that were rated as happy is rather large from almost perfect agreement to almost random ratings.

When comparing ratings of facial expressions between the three rating scales, our findings indicate that the judgments of facial expressions tend to agree between the two pictorial scales. On the other hand, the Likert scale ratings are likely to differ; this means that emotion might be rated differently with the Likert scale. Moreover, it indicates a lack of construct validity of the pictorial scales as measured against Likertscale ratings. It should be noted that the Likert scale, although it is frequently used as a rating scale, it is not a baseline for rating emotions in a strict sense. Therefore, the finding that the ratings differ does not mean that the LFS and the SLFS scales are less suitable for rating emotional expressions. At this stage we cannot judge which of the three approaches generated more 'correct' emotional ratings.

One concern could be that, when rating the emotional expression of faces with pictorial scales, users might simply match the facial expression in the stimulus with an image in the scale that visually provides the best match. In principle, this visual matching could be done without actually judging the emotional expression. One then might argue that it is not necessary to actually judge the shown emotion. But this does not necessarily rule out the usefulness of such a pictorial scale. It could still give meaningful results when seen from the perspective that it allows users to just pick a face without having to go through an active cognitive interpretation process which for example requires verbalization of the otherwise just visually processed emotion. Brasley et al. [START_REF] Bradley | Measuring Emotion: The Self-Assessment Manikin and the Semantic Differential[END_REF] argue that the scale SAM may lead to more consistent judgments than a verbal scale (Semantic differential) because the stimulus material is human-like.

The translation of personal experience to numerical values, such as in Likert scales, can be somewhat problematic. While some might assume that the numbers represent equidistant categories for judging emotional intensity (e.g. the difference between level 1 and 2 is the same as between 2 and 3), others might interpret the same scale as ordinal (where the difference between levels is not necessarily the same). This must be taken into account in analyzing data from such scales. Equal intervals in a scale can ensure a higher level of discriminability in a sense that the categories are more or less distinct and do not measure the same quantity [START_REF] Bieri | The Faces Pain Scale for the Self-Assessment of the Severity of Pain Experienced by Children: Development, Initial Validation, and Preliminary Investigation for Ratio Scale Properties[END_REF]. When designing our pictorial scales we developed a selection algorithm that was designed to find the best representative for each emotion and intensity level, while still aiming to achieve a high amount of discriminability. However, we cannot assume equidistance between the intensity levels. This should be taken into account by analyzing and interpreting data obtained with these scales.

In the second study, we took a slightly different approach compared to the first one. Instead of using pictures and having users judging these facial expressions, we used a set of images that were supposed to elicit an emotional reaction. The task was then to use one of the three methods to rate this emotional reaction. Therefore, instead of rating emotional facial expressions, the users' emotional state is of interest here. Furthermore, the above mentioned facial image matching could not take place because the stimulus material was more generic; not necessarily showing people's faces. Results of this experiment show that ratings made with all scales agree. This means that the two pictorial scales and the Likert scale produce very similar ratings for judging a user's emotional state elicited through pictorial stimulus material.

It would be desirable to first establish a ground truth to compare the ratings against. Taking our findings into account one might even argue that Likert scale based ratings, the most commonly used approach, might be less suitable for certain types of emotional rating as they require more cognitive effort. Therefore, trying to validate new methods with comparing them to Likert scale based ratings might not be the best approach. Thus, further studies are needed to investigate the scales' validity.

Our findings thus indicate that when judging emotion of faces, judgments based on the two pictorial scales that we have developed can in some instances be different from Likert scale based ratings. However, when rating one's own emotional state or reaction to a scene, there is no difference between these measures. Further investigations are needed to study these findings in more detail and to see, why the pictorial scales differ in the one instance but not in the other. One possible explanation for this could be found in the notion of pictorial matching. We could assume that having to interpret someone else's facial expression and having to verbalize this (i.e. finding the verbal expression that corresponds to the facial expression) requires more cognitive demand [START_REF] Desmet | Development and Application of a Pictorial Mood-Reporting Instrument[END_REF] and thus might introduce some bias in the emotion rating process. Using pictorial scales could be easier and quicker for completing the same task. When this easy matching process cannot be done (for example, when rating one's emotional response to a scene) there always has to be some interpretative step, which could explain why the three methods achieved more similar results in the second experiment.

Finally, the LEGO Minifigures are not only useful as a measuring tool, but there are other valuable uses. Similar to Desmet et al. [START_REF] Desmet | Designing Products with Added Emotional Value: Development and Application of an Approach for Research Through Design[END_REF] the Minifigures can be used as an aid to objectify emotional states and responses, which could be difficult for many people to express otherwise. Also, the Minifigures can be used to start a conversation on these responses between participant and researcher or designer.

Conclusion and Future Work

We describe the design and the evaluation a new measurement tool for rating emotions based on LEGO Minifigures. The measuring tool use Minifigures selected by responders to represent a respective emotion and intensity. As one step towards this tool, we chose to create two pictorial scales based on Minifigure images (LEGO Face scale and Stylized LEGO Face scale). The scales are based on images from a pool that consists of more than 700 Minifiguring faces. This allowed us to choose suitable facial expressions for the respective emotions and their intensities from a large amount of possible faces. This approach could overcome problems found by other researchers with tools based on a smaller selection of stimuli or designed stimuli. Moreover, based on the results of our study we assembled a physical version of our facial scale (see Fig. 8). This version is now ready for further testing and we refer to it as the Emotion Rating Figurines (ERF). The ERF may enable people to indicate emotions by selecting an emotional figurine from our set of calibrated figurines. ERF may enable children to easily express their emotions, since they typically identify themselves with dolls during play already. Also other users who struggle with processing abstract categories of emotions could greatly benefit from this new measurement tool.

The ERF has a large potential for mental and other health assessments. An increasing number of children suffer from communication disorders, attention deficit hyperactivity disorder, Aspergers syndrome and autism. The ERF could enable them to communicate emotions even if their specific disorder impairs them from using language-based tools. Due to the potential toy-like design of the ERF, children may naturally be attracted to it. The great advantage of ERF over existing questionnaire based emotion measurement tools is the physical embodiment of the figurines.

In general, users could be more easily able to project themselves into the figurines compared to identifying themselves with a drawing on paper. The ERF might become useful for HCI researchers, psychologist, therapists, social workers and others who want to better communicate with their clients about emotions.

  performed an experiment to evaluate the two new measurement instruments: LEGO Face Scale (LFS) and Stylized LEGO Face Scale (SLFS); examples are shown in Fig. 1(A) and Fig. 1(B) respectively. We investigated the validity of the two new instruments by comparing them to ratings on a 5-point Likert scale (Fig, 1(C)). In this first study we asked participants to rate the emotional expression of face portrait photographs selected from the Japanese Female Facial Expression (JAFFE) Database [25]. The following sections describe the methods and setup of the study. (A) LEGO Face Scale (LFS) (B) Stylized LEGO Face Scale (SLFS) (C) Likert Scale

Fig. 1 .

 1 Fig. 1. The three measurement scales (A) LEGO Face Scale, (B) Stylized LEGO Face Scale, and (C) A five point Likert scale.

Fig. 2 .

 2 Fig.2. Illustration of the trial sequence: five seconds break (black screen) followed by an image from the pool of 50 IAPS stimuli followed by one of the three scales (Likert, LFS, SLFS).

Fig. 3 .

 3 Fig. 3. Illustration of the setup for allocating each of the 50 IAPS into one of three sets and presenting the sets for evaluations using a counterbalanced order for each scale.

Fig. 4 .

 4 Fig. 4. Entropy by Scale and Mode Emotion.

Fig. 5 .

 5 Fig. 5. Disagreement by Scale and Mode Emotion.

Fig. 6 .

 6 Fig. 6. For the first evaluation, the graphs represent the estimated posterior mean probabilities and the associated 95% CIs (credible intervals) of categorizing a random photograph into each of the six emotions by scale, and pooled.

Fig. 7 .

 7 Fig. 7. For the second evaluation, the graph represents the estimated posterior mean probabilities and the associated 95% CIs of categorizing a random image into each of the six emotions by scale, and pooled. Model adjusted for individual-and order-random effects.

Fig. 8 .

 8 Fig. 8. A physical LEGO Face Scale named Emotion Rating Figurines (ERF).

Table 1 .

 1 An example distribution of responses for a LEGO Minifigure image.

Table 2 .

 2 Results from the image labeling pre-study.

Table 3 .

 3 DIC comparisons between the scale-specific and pooled models in studies 1 and 2. Large negative values indicate that the pooled model is better, i.e. no effect of scale on probabilities of assigning a photograph to an emotion-intensity categories. 'All' corresponds to testing the hypothesis of at least one of the scales being different from the rest.

			Study 1: ΔDIC Study 2: ΔDIC
	LFS and SLFS	-580.28	-5.42
	LFS and Likert	1628.03	-92.29
	SLFS and Likert 1102.98	-49.11
	All		1315.00	-79.85
		DIC, with ord. DIC, without ord. ΔDIC
	Style specific 1843.74	1849.09	-5.35
	Pooled	1763.52	1765.75	-2.23

Table

MT is a crowd-sourcing web service that enables distributing small tasks to a large anonymous workforce who receive micro-payments for completed tasks.

http://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-dic/#q9


Marco Romano

Andrea Bellucci
email: abellucc@inf.uc3m.es

Ignacio Aedo
email: aedo@ia.uc3m.es

Understanding Touch and Motion Gestures for Blind People on Mobile Devices

Keywords: User-defined, gestures, blind, accessibility, touch screens. 1

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Mainstream mobile devices, like smartphones, are widely used by blind people [START_REF] Kane | Freedom to Roam : A Study of Mobile Device Adoption and Accessibility for People with Visual and Motor Disabilities[END_REF]: they offer opportunities to increase their level of independence by making it possible to keep in touch with family and caregivers [START_REF] Sánchez | Mobile messenger for the blind[END_REF], orientate themselves in the street [START_REF] Di Chiara | The Framy user interface for visually-impaired users[END_REF] and have portable access to assistive technologies like screen-readers [START_REF] Manduchi | Portable and Mobile Systems in Assistive Technology[END_REF]. Apart from screen-reading software that are now included in major mobile platforms, the interaction with mobile devices presents important accessibility barriers, since touch-enabled displays are designed for sighted interactions -users need to locate objects on the screen-and do not provide tactile feedback, conversely from hardware buttons.

Likewise previous research [START_REF] Kane | Slide rule: making mobile touch screens accessible to blind people using multi-touch interaction techniques[END_REF][8], we are interested in understanding how to improve the accessibility of modern mobile interaction. Off-the-shelf mobile phones allow to interact both by touch and motion gestures to perform specific computing tasks: our goal is to explore the design space and provide insights to understand how best to design gestural interaction with mobile devices for a blind person.

Here we present results of a preliminary elicitation study [START_REF] Schuler | Participatory design: Principles and practices[END_REF] in which 8 blind users are asked to provide touch or motion gestures for common tasks on a mobile device. Our insights provide "prima facie" implications for the design of accessible mobile gestures sets. Compared with the study of Kane et al. [START_REF] Kane | Usable gestures for blind people: understanding preference and performance[END_REF], in which researchers used a 10.1-inches tablet pc to elicit touch gestures from 10 blind people, our results suggest that users have different gestures preferences for a mobile form factor. Moreover, we found that blind people are not particularly keen to motion gestures.

BACKGROUND

Our research is informed by previous work on touch and motion gestures, accessible mobile touch interface for blinds and user-defined gestures for surface computing.

Touch and motion gestures.

Touch gestures (or surface gestures) are hand movements performed on a twodimensional surface, like the touch-screen of a mobile phone [START_REF] Morris | Understanding users' preferences for surface gestures[END_REF]. Over the last two decades, surface gestures have been studied under different perspectives, from enabling hardware [START_REF] Rekimoto | Smartskin: an infrastructure for freehand manipulation on interactive surfaces[END_REF], to software frameworks and architectures [START_REF] Echtler | A multitouch software architecture[END_REF], to design principles and taxonomies for touch interaction and evaluations of gestures vocabularies [START_REF] Morris | Understanding users' preferences for surface gestures[END_REF]. Modern mobile devices allow users to perform also motion gestures that are gestures performed in three dimensions by translating and rotating the device thanks to embedded sensors [START_REF] Ruiz | User-defined motion gestures for mobile interaction[END_REF]. One of the first examples of a motion gesture was provided by Rekimoto [START_REF] Rekimoto | Tilting Operations for Small Screen Interfaces[END_REF], who exploited tilting data to interact with virtual objects. Recently, motion gestures have been explored for a variety of input tasks, such as text entry [START_REF] Jones | GesText: accelerometer-based gestural text-entry systems[END_REF] and maps navigation [START_REF] Bellucci | t touch me: multi-user annotations on a map in large display environments[END_REF]. One of the limitations of gestural interaction is the fact that, without any user participation, the gestures set is defined by designers. Although such gestures can be legitimate to test technical aspects of gesture systems -since they are easy to recognize-they do not consider users preferences and performances.

User-defined gestures for surface computing.

Previous research exhibits that users preference patterns match better with gestures created by groups of potential users rather than expert designers [START_REF] Landau | Merging Tactile Sensory Input and Audio Data by Means of The Talking Tactile Tablet[END_REF]. To this end, researchers have begun to focus on participatory design techniques [START_REF] Schuler | Participatory design: Principles and practices[END_REF] to elicit touch gestures from users, aiming at generating better interactions that are directly informed by users behaviour. In their influential study, Wobbrock et al. [START_REF] Wobbrock | User-defined gestures for surface computing[END_REF] created a userdefined touch gestures set for surfaces by showing the outcome of an action to participants and asking them to provide a gesture that would produce that action. In the recent past a plethora of other studies have exploited the same method to elicit user preferences for a variety of conditions, e.g.: motion gestures for mobile devices [START_REF] Rekimoto | Smartskin: an infrastructure for freehand manipulation on interactive surfaces[END_REF], touch gestures for coupling mobile devices with interactive surfaces and multi display environments [START_REF] Kristensson | Designing and evaluating text entry methods[END_REF] or for discerning cultural differences and similarities in touch behaviours [START_REF] Mauney | Cultural Similarities and Differences in User-Defined Gestures for Touchscreen User Interfaces[END_REF]. Significant to our research is the work of Kane et al. [START_REF] Kane | Usable gestures for blind people: understanding preference and performance[END_REF]: they applied the method of Wobbrock et al. with the goal of devising what kind of gestures are the most intuitive and easy to perform for blind people. In their study they asked participants to provide touch gestures for standard tasks on a touch-enabled 10.1-inches tablet. Insights from the study provide implications for the design of accessible touch interfaces for blind user, such as avoiding symbols used in print writing, use physical edges and corners as landmarks and reduce the need of spatial accuracy.

Our study follows the method proposed by Wobbrock et al. [START_REF] Wobbrock | User-defined gestures for surface computing[END_REF] and differs from the study of Kane et al. [8] in the following aspects: [START_REF] Abascal | Mobile communication for people with disabilities and older people: New opportunities for autonomous life[END_REF] we explore gesture preferences for a different form factor -a 3.5-inches smartphone-since the size of a device can be an important factor for the definition of a gestures set, and (2) we include motion gestures, which are a relevant input modality supported by modern smartphones that, nonetheless, is underutilized [START_REF] Rekimoto | Tilting Operations for Small Screen Interfaces[END_REF]. Moreover, there is a lack of knowledge if or not motion gestures can be a valid input method for blind users.

Mobile touch interfaces for blind people.

The widespread diffusion of mobile devices requires to assess accessibility issues of touch interfaces in order to provide efficient interaction techniques for visual impaired users [START_REF] Abascal | Mobile communication for people with disabilities and older people: New opportunities for autonomous life[END_REF]. Speech, audio and haptic has been widely used as output channels. For instance, screen-readers have been developed for different platforms, such as VoiceOver 1 for Apple OS or Eyes-Free Shell2 for Android. The Talking Tactile Tablet [START_REF] Landau | Merging Tactile Sensory Input and Audio Data by Means of The Talking Tactile Tablet[END_REF] uses a special hardware with an overlay that enables tactile feedback. With respect to the input, various touch techniques have been explored for eyes-free interactions. Slide Rule [START_REF] Kane | Slide rule: making mobile touch screens accessible to blind people using multi-touch interaction techniques[END_REF], for example, offers adapted gestural interaction for navigating lists of items. Lastly, the role of touch location has been studied in order to provide different triggers for gestures that depend on the screen region [START_REF] Ruiz | User-defined motion gestures for mobile interaction[END_REF].

Research on touch interaction techniques for blind people shows that we have not yet developed a clear understanding of users preferences, which can be achieved by involving the end users in the design process [START_REF] Schuler | Participatory design: Principles and practices[END_REF]. The results are promising, since it has been shown that blind people have significant spatial and tactile abilities and therefore are capable of using touch-and motion-based interfaces [START_REF] Kane | Usable gestures for blind people: understanding preference and performance[END_REF].

USER STUDY

We are interested in understanding what kind of gestures blind users consider intuitive for mobile devices and whether or not motion gestures can be a valid input modality together with touch gestures. We conducted a preliminary study with a group of 8 blind people in which participants were asked to perform touch or motion gestures for common tasks on a mobile phone. We recruited 8 users, 4 male and 4 female with average age 61.1 (SD=11.29). Participants were selected considering their level of blindness and their familiarity with touch technologies. Participants have congenital blindness (2 of 8) or are early blind (6 of 8), a condition that requires the use of a screen reader to access digital information. As one of the participants stated "blind people are not an homogeneous population" and there are differences, for instance, with respect to spatial perception between early and late blind [START_REF] Postma | Differences between earlyblind, late-blind, and blindfolded-sighted people in haptic spatial-configuration learning and resulting memory traces[END_REF]. Since it would not have been possible, at this stage, to isolate this condition, we recruited the subjects in order to have a sample as uniform as possible. The participants belong to a Spanish organization for blind people (ONCE). The organization provides general IT education and technological support to their members. In particular, it offers a course that focuses on interaction with iPhone, in which attendees learn how to interact with the VoiceOver interface. All of our participants attended the course and are familiar with IT technologies. They use a desktop pc daily for working purposes and use touch-screen devices mainly as personal assistants both when they are at home (e.g. reading emails) or outside (e.g. getting the current location and routes). Moreover, all participants consider themselves as technologically proactive and eager to improve the access of their peers to mobile technologies. We conducted the study in a laboratory setting: we chose the ONCE central office to make participants confortable, since the location was well known by all.

The study was conducted on a 3.5 inches iPhone 4s running iOS 7.1.2. We developed an HTML5 and Javascript application that captures multi-touch and motion gestures. As in [START_REF] Kane | Usable gestures for blind people: understanding preference and performance[END_REF], we used an operating system-agnostic application aiming to provide a neutral setup for the experiment. The ubiquity of HTML allows to repeat the experiment with heterogeneous hardware without implementing an ad-hoc version of the application. The application stores gestures data in a XML-formatted file and provides the researchers with a visualization tool for the later analysis. Figure 1 (a) shows a participant performing a touch gesture (on the left) and the resulting data captured by the application (on the right). All the sessions were video-recorded with a stationary camera pointing to the device to capture the interactions and protect subjects' privacy. No other objects were present and the users sat to side of a table during the whole session. Just a ONCE helper was present in case of necessity. Previous to the experiment, we interviewed one of the ONCE IT specialists in order to finetune the setup. From the interview, we decided to give participants 10 minutes to get familiar with the device, since the IT specialist reported that "it is paramount for visually impaired users to understand the dimensions of the screen in order to create a mental map of the device they are going to use". We also estimated each session to last less than 40 minutes, which is the time limit the specialist suggested to avoid excessively stressing the users. Each session began with an interview on the kind of disability, technological skills, and the participant experience with mobile technologies. Participants were then introduced to the tasks. They had to invent two gestures (a preferred one and a second choice), motion or touch, which can be used to trigger a specific command. We used the command list from Kane et al. [START_REF] Kane | Usable gestures for blind people: understanding preference and performance[END_REF], which we reviewed and modified in collaboration with ONCE IT specialist in order to match the context of mobile devices and to assure that all the participants could know and understand it. Selected commands were: Context menu, Help, Undo, Switch application, Next, Previous, Ok, Reject, Move object, Open, Close, Copy, Cut, Paste, Quit, Select, Change input field, Answer up, Hang up. Participants were asked to think aloud while performing the gestures and motivate their preferences. Before performing a gesture for a command, the experimenter read the name of the command and a description of the expected outcome.

Results.

The 8 participants were asked to perform 2 gestures for 19 commands for a total of 304 samples. Some of them were not able to provide two different gestures for the same command, therefore we have 152 gestures as first choice plus 126 for the second choice (total of 278). Only 12 of the 278 (4,32%) were motion gestures while multifinger were 43 (15,46%). Figure 2 illustrates the gesture rationale for each category.

We classified motion and touch gestures using the nature dimension of the taxonomy from Wobbrock et al. [START_REF] Wobbrock | User-defined gestures for surface computing[END_REF], in the same way Kane et al. [START_REF] Kane | Usable gestures for blind people: understanding preference and performance[END_REF] did for touch gestures and Ruiz et al. [START_REF] Ruiz | User-defined motion gestures for mobile interaction[END_REF] for motion gestures. The nature dimension groups together symbolic, metaphorical, physical and abstract gestures. Gestures are defined as metaphorical when they represent the metaphor to act on an object different from a mobile phone (e.g., emulating an eraser on the touchscreen to remove an object). They are physical when they act directly on an object (e.g., drag and drop), symbolic when gestures indicate a symbol (e.g., typographic symbols like '?'). Finally gestures are abstract when their mapping is arbitrary (e.g., double tap). We grouped our samples taking into account users explanations and di- rect observations. The abstract gestures are the largest category, with 134 samples (48.02%), followed by physical gestures (93 samples), symbolic (34) and metaphorical [START_REF] Ruiz | User-defined motion gestures for mobile interaction[END_REF]. The abstract category presents the highest prevalence of multi-finger gestures (24 gestures, 15 physical and 4 metaphor). Multi-finger gestures have been employed 18 times as first choice, 25 as second. Finally, 37% of the provided gestures stem from previous experiences with desktop and touch interfaces as explicitly commented by the testers.

DISCUSSION

When compared with the study of Kane et al [START_REF] Kane | Usable gestures for blind people: understanding preference and performance[END_REF], in which metaphorical and abstract gestures were dominant for a tablet form factor, our results show that physical and abstract are prevailing for a mobile device. Those results suggest that the physicality of the device, which determine the way users interact with it (e.g., making gestures with the same hand that is holding the device), the tasks selected for the mobile context (e.g., answering the phone) and the user background (e.g., technological knowledge) influence the kind of gestures made by blind users. In particular, the physical nature of the gestures is prevalent for the following commands: previous (60%), next (47%), move object (67%), cut (36%), reject (47 %). Some users commented to be inspired by the interaction mechanisms of real world that make them to feel the interaction as more "natural". In general abstract is the main gesture category and get the best percentages in commands such as ok (75%), copy (64%) and select (77%). Many of the abstract gestures stem from participants experience with the other user interfaces. For example, regarding the ok command, 7 users performed a tap or a double tap because it is what they normally do in their mobile or desktop devices. Moreover most of the elicited gestures (203) are one-finger and multi-finger variations of tap or flick (an unidirectional movement of the finger). This is probably due to the fact that the VoiceOver interface, which participants have learnt to operate in the IT courses, uses mostly tap and flick gestures. Flicks vary on the number of fingers, from one to four. Taps vary on the number of fingers, one or two, and repetitions. In VoiceOver multi-finger gestures are mainly used for system control or are personalized gestures. This suggests the reason why testers demonstrated to be familiar with multi-finger gestures but to prefer mainly one-finger strokes.

Our study shows that blind people tent to use gestures that are different from common touch interfaces for sighted users but that are familiar to them. For instance, the 2-finger double-tap gesture is used frequently in the VoiceOver interface: its uses include answering and ending phone calls, starting and stopping music and video playback, etc. A preliminary advice for the designers of mobile accessible interaction is to consider the technological literacy of the users and exploit their familiarity with touch gestures.

Another outcome of the study is related to the reduced use of motion gestures. Causes are due to users lack of familiarity with motion gestures and because they were afraid to hit some objects moving the device. Conversely from other studies that focus on sighted users [START_REF] Ruiz | User-defined motion gestures for mobile interaction[END_REF] and show that motion gestures are a significant for mobile devices, we found that motion gestures do not seem to be suitable for blind people: there is no general agreement in using a common gesture for a given command.

Together with touch gestures that can be easily recognized, participants provided other kinds of no traditional hand postures, consisting in covering portions of the screen, that are not detected by current mobile phones hardware and software. Such gestures were performed by three different users for four different commands. For instance, covering the bottom part of the screen was used to confirm a choice, as it recalls the paper's position in which we usually sign an agreement, as depicted in Figure 1(b). Another participant used the palm to cover the whole screen with the intent to trigger the quit command.

A considerable number of gestures was performed in specific screen locations that participants associated to a semantic interpretation. Performing gestures in the left side was interpreted as "go back" or "recover from an action". For instance gestures on the left side were associated to commands like undo, previous, reject, close, quit and hang up. On the other hand, the right side was associated to opposite actions, such as next, accept, open, answer a call.

With respect to spatial awareness, Kane et al. [START_REF] Kane | Usable gestures for blind people: understanding preference and performance[END_REF] suggest to favor edges and corners of the screen because they can be used as physical landmarks that help blind people to locate spots in the surface. Our study reveals that this behavior largely depends on the experience of the users with mobile technology. We observed that users with less experience start by searching for the edges of the touch-screen and, when they are ready, they perform the gesture nearby it. Users with more years of experience with touch-screens generally start their gestures from the center of the device.

CONCLUSIONS AND FUTURE WORK

We presented a preliminary elicitation study to understand preferences of blind people with respect to touch and motion gestures on mobile devices. First insights from our study can be summarized as follows: 1) our results highlight that gestures are influenced by the form factor and the users background, 2) the gesture rationale is prevalently abstract and physical, 3) users tend to assign a behavior to screen locations, 4) users prefer touch gestures rather than motion gestures. Our work provides new insights on how blind people use mobile technologies. However, given the openended nature of the study, further research is needed. Since the lack of prior knowledge and experience affects the users' preferences, we consider that an in-depth study could reveal new interesting insights. We plan to modify the experiment requiring users to provide and then evaluate both a touch and a motion gesture for each command. This is to confirm whether motion gestures can be an effective input modality. The final aim is to reach an agreement on the mapping between gestures and commands and thus to define a robust and accessible gestures set for mobile phones.

Fig. 1 .

 1 Fig. 1. (a) Example of a touch gesture and its visualization. (b) A no traditional gesture.

Fig. 2 .

 2 Fig. 2. Gestures rationale according to the nature dimension.

https://www.apple.com/es/accessibility/ios/voiceover/

https://code.google.com/p/eyes-free/

Acknowledgement This work is supported by the project CREAx grant funded by the Spanish Ministry of Economy and Competitivity (TIN2014-56534-R). We also thank the collaboration and the support of the ONCE organization of Madrid.


Jens Bauer
email: j_bauer@cs.uni-kl.de

Achim Ebert
email: ebert@cs.uni-kl.de

Virtual Buttons for Eyes-Free Interaction: A Study

Keywords: Eyes-Free, Evaluation, Virtual Buttons, Mobile Devices 1

The touch screen of mobile devices, such as smart phones and tablets, is their primary input mechanism. While designed to be used in conjunction with its output capabilities, eyes-free interaction is also possible and useful on touch screens. One of the several possible techniques for eyes-free interaction is the virtual button method, where the screen is divided into a regular grid of buttons that can be pressed even without looking at the screen.

This paper contains an exploratory study about influence factors on this interaction method. Results indicate, that not only the size of the buttons matter, but also the device orientation and user dependent factors, such as the age or general experience with touch screens. By involving small children in the evaluation we can see the validity of this approach even for the youngest users.

Introduction

In most cases, interaction with mobile phones and tablets is done via the touch screen of those devices. Users will naturally look at the screen while using it. However, there are some cases, where looking at the screen might not be possible or viable, e.g. while driving or when interacting with another system via the mobile device. This is called eyes-free interaction. A comprehensive list of such cases is presented by Yi et.al. [START_REF] Yi | Exploring user motivations for eyes-free interaction on mobile devices[END_REF]. Consumer-level devices do not provide haptic feedback on their screens, thus making this style of interaction difficult. One possibility of dealing with this is to divide the screen into a grid of virtual buttons. Other common approaches are described in the Related Work section later. There are studies showing the impact of different grid sizes on the accuracy (i.e., the hit-miss-ration of the user) for this kind of interaction. The paper from Wang et.al [START_REF] Wang | Understanding performance of eyes-free, absolute position control on touchable mobile phones[END_REF] is a prime example of this. More are covered later in the Related Work. No research has yet been done on the impact of different devices and their sizes on the accuracy and the duration of actually interacting, since the device size impacts those values inversely.

In this paper, we describe an exploratory study of eyes-free virtual button presses. Unlike previous studies, we look for the impact of not only the number of virtual buttons on the touch screen, but also the size of the device itself, attributes of the user, etc. To get further insight into the general usability of this method our participants are not only adults, but also small children. Children are getting their own mobile phones (with touch screens) in early age, thus it is important to know, if methods working well for adults are actually viable for them.

In the next section we will give a brief overview of related literature. Then we will present the design of our evaluation and the process, followed by the actual evaluation of the results. We finish with the conclusions of the results.

Related Work

Yi et.al [START_REF] Yi | Exploring user motivations for eyes-free interaction on mobile devices[END_REF] presented a classification of motives for eyes-free interaction and identified several actual reasons. The four main categories are Environmental, Social, Device Features and Personal. The number and variety of reasons shows that eyesfree interaction is an important topic.

Alternatives for eyes-free interaction to the virtual button approach include Bezel Swipe [START_REF] Roth | Bezel swipe: conflict-free scrolling and multiple selection on mobile touch screen devices[END_REF] and Touch Gestures [START_REF] Bragdon | Experimental analysis of touch-screen gesture designs in mobile environments[END_REF]. Bezel Swipe requires the user to touch one of the sides of the screen and swipe inwards. The tactile feedback with this technique makes it viable for eyes-free interaction. Touch gestures, which do not require any special position on the screen, such as a simple pinch gesture are also easy to use eyes-free.

Azenkot and Zhai [START_REF] Azenkot | Touch behavior with different postures on soft smartphone keyboards[END_REF] evaluated the effect of using a finger, a thumb or two thumbs on text input on touch capable smart phones.

An evaluation about virtual buttons on smart phones was done by Wang et.al. [START_REF] Wang | Understanding performance of eyes-free, absolute position control on touchable mobile phones[END_REF]. They found that grid sizes of bigger than 3x3 are not feasible to use for almost any user. Their evaluation was only done with one touch screen capable phone and only using one handed-interaction.

A huge evaluation done by Henze et.al. [START_REF] Henze | 000 taps: analysis and improvement of touch performance in the large[END_REF] focuses on the impact of several hardware design factors, such as device size, screen resolution, target sizes and position. They gathered data through a game distributed in an official app store. However their study was by design not abled to measure those effects on eyes-free interaction.

Evaluation

Design

The main goal of the study was to find out how different factors impact on the eyes-free usability of virtual buttons. Those factors are the size of the virtual buttons, as given by the touch screen size and the layout of the button grid. According to the results of Wang et. al. [START_REF] Wang | Understanding performance of eyes-free, absolute position control on touchable mobile phones[END_REF], button grids with more than 3 buttons in any direction were excluded, as were trivial layouts with only one or two buttons in total. The remaining grid layouts were : 1x3, 2x3, 3x3, 3x2 and 3x1. To gain insight about the effect of device size, we also used three different devices, an iPad 2 (241.2mm x 185.7mm, 9.7 inch display), iPad Mini (200mm x 134.7mm, 7.9 inch display) and an iPhone 4S (115.2mm x 58.6mm, 3.5 inch display). We chose to use only this device family to minimise hardware influences on the tests. For example the resolution of touch input (not screen pixels) is similar on all devices. This assures the App to behave the same on all devices, too. Since the device orientation might impact user performance as well, all tests were to be done in both landscape and portrait orientation. As shown by Azenkot and Zhai [START_REF] Azenkot | Touch behavior with different postures on soft smartphone keyboards[END_REF], usage of thumb vs. finger can have an impact, too, and according to Karlson et.al. [START_REF] Karlson | Handbook of research on user interface design and evaluation for mobile technology[END_REF] this is the primary way of mobile touch interaction. Therefore each user was asked to complete all tests a second time using only one thumb in portrait orientation and two thumbs in landscape. In the other test runs, user had the freedom of choice of how to interact with the device, the only constraint being, the device has to be held with at least one hand and may not be put down on a table or anywhere else. Because of the physical size of the devices, this was only done for the phone. For additional data, users were asked for their experience with touch devices (self perceived), age and gender.

The evaluation design is straightforward. Every user is presented a grid of virtual buttons, displayed not on the mobile device, but on a separate computer screen, as we focus on eyes-free interaction. The mobile device screen stays completely blank, so if a user accidentally looks at the mobile device, they get no additional insight, and thus no advantage over other participants not looking at the device.

Depending on the complexity of the grid, 20, 30, or 40 touches were needed per grid layout and device orientation (more complex layouts needing more touches). Considering that every participant was asked to perform the test with three different devices and one device having an additional test run under special conditions described later, every user had to perform a total of 1520 touches. We used three different mobile device sizes, large tablet, small tablet and mobile phone to measure the impact of the device/screen size on the test.

To have a wider age range in the evaluation, we decided to not only have adult participants. To see how age actually affects the results of our study, we created another design, targeted at small children. This will help to understand if virtual buttons is a concept simple enough for even children to understand. We can also get an insight on the effect the grid layout and device size has on their performance, given their smaller hand sizes.

To get results across all possible grid setups, we reduced the number of touches needed per grid layout to ten. Also to help the children understand the task they were supposed to accomplish, their test started with a short example of the test with the grid visible on both the mobile device and the computer screen. These touches were, of course, not counted in the evaluation. The Thumb-Only run on the mobile phone was also not done by the children, since their hands were not big enough to accomplish it. To not negatively influence the study due to the children getting bored by the tasks, they had the option of stopping the test after each device. Thus, less data is available from the children's test, but still enough for an evaluation.

For the evaluation we developed a simple mobile application connected to a desktop application. The desktop application will display the current grid of virtual buttons and highlight the button the user is supposed to be pressing. To avoid confusion, the system guarantees that no button will be highlighted twice in a row, giving reliable feedback to the participants. The system automatically randomizes the test order (order of device, grid layout and button to press) to avoid learning effects affecting the results. The grid was displayed on a standard LCD computer display with 21 inches of size, connected to a desktop PC. A dedicated WiFi connected the PC and the mobile device.

Each touch was recorded individually. Accuracy and duration was measured by aggregating the gathered data. Accuracy is simply the ration of hitting the correct button and the total number of button requests. Duration is the total time between a button prompt being displayed and it being pressed, summed up for each test run.

For the test involving adults, we had 44 participants (30 of them male, 14 female) in the age range of 21 to 62 years. All test candidates were asked to rate their own experience with touch screen interaction (on a Likert-scale of 1-5) and to state if they own a touch device themselves (which the majority of 34 users did). The average experience level was 3.57, unsurprisingly due to the high number of touch device owners in the test. As a side note, the high number of touch device owners shows the ubiquity of these devices and does not affect the viability of the test as we still have 10 participants not owning such a device.

The 29 children (18 male, 11 female) were between 4 and 6 years old. They were not asked to rate their experience, as children of that age are unreliable of their selfassessment. But they were asked, if anybody in their family owns a touch device that they are allowed to use on a regular basis. 18 of the 29 children confirmed that, while 9 had no prior access to touch devices (2 children opted to not to answer that question).

Results

To get basic results from the study, we tested for influence of four basic factors on the accuracy. The factors are the device orientation (Landscape/Portrait), a combined factor of the device used and if the user was asked to use only their thumb, if the user actually owns a touch device, and the self-rated experience value.

The mean values are given in Table 1 below. As supposed from the related work, the accuracy results are above 90% for adults. Children's accuracy is significantly lower at values between 70% and up to more than 90%. This shows, that children are actually able to understand the concept of virtual buttons, even for eyes-free interaction. But due to the lower hit-ratio, care must be taken to allow undo-operations or similar mechanisms when designing applications for children.

Portrait orientation supports higher accuracy for both adults and children. It also influences the duration, with adults being faster in portrait orientation. Interestingly children were faster using the (less accurate) landscape orientation. More evaluation is needed in order to gain insight into this.

As expected, bigger devices allow for higher accuracy. It also seems, that the bigger device size does not cause the duration to go up (due to longer ways to move fingers), but instead seem to lower the duration. This is probably caused by users being more confident in hitting the desired area.

Using the thumbs only for interaction with the phone seems to actually increase accuracy and interaction speed. Since the alternative involves moving the whole hand to reposition the finger, this is not surprising.

Experience levels do also influence both accuracy and duration, mostly in the expected way. There is a outlier in the data, as the low experience level of 2 performed faster than levels 1, 3 and 4. The cause of this is unclear at this point.

Gender does not seem to influence accuracy at all for the adults. Though male candidates performed the requested tasks quicker than females. The cause of this needs more evaluation. Even more interesting is the fact, that with children this is actually reversed. Girls were more accurate hitting the virtual buttons and even performed fasters. Looking at the actual target button sizes, as defined by touch screen size and the grid layout yields the results seen in Table 2 and3. For Table 2 the results were grouped by the minimal dimension of the button. For example, a button size of 10x15 mm would then have a minimal dimension of 10mm. In Table 3 the whole square size is used to group the results. Roughly the results reinforce the hypothesis, that bigger target sizes will lead to better accuracy and execution speed. Not all bigger sizes perform better than smaller sizes. More research is needed here to find the cause of this. Generally the standard deviation (and therefore variance) of accuracy and duration decreases with increasing button size (in both tables). This might be a sign of users being more confident with bigger sizes or simply more users can reliably use the vir-tual buttons as their size increases. There also seems to be a point till where user performance increases faster with increasing size. Unfortunately our data is not abled to support such a hypothesis statistically, so this is also a point needing further research. We did a Pearson's test of correlation between the accuracy, user's age, the virtual button size, the minimal length or width of a button (called MinSize for the remainder of this paper) and the duration of a test. We got a significant correlation between accuracy and age, button size and MinSize for both test groups. The same goes for duration, respectively. Since Pearson's test of correlation is well known to be impacted by outliers, we confirmed our results with an additional Spearman-correlation test. All correlations were confirmed, except for the correlation of duration and MinSize in the children group, so correlation here is improbable.

Analyses of Variance (i.e. ANOVE) was then done to test the influence of the virtual button position on the accuracy. Separate ANOVAs were performed for each combination of device and grid layout. One of the interesting findings here is the fact, that for all devices the middle button in the 1x3 (one single row of 3 buttons) layout was significantly (on a 5% level) harder to hit, while this does not hold true for the 3x1 layout. Here no significant differences could be found. Other layouts with statistically significant inaccurate buttons are 2x3, 3x2 and 3x3.

For the 3x3 layout, the buttons in the middle column had least accuracy, with the top and bottom button in this column having even lesser accuracy than the middle one. This holds true on all devices. For this layout the middle device size has the highest accuracy for all individual buttons. In the 3x2 and 2x3 layouts, also the middle buttons had lower accuracy than the other buttons, again on all devices. This is probably due to the fact that buttons near a corner are easier to find without looking on the device.

Conclusion

Our evaluation shows, that there are several influence factors confirmed for eyesfree virtual button interaction on smart phones and tablets. The device size is important as one might assume as is the actual size of the target. As the improvement of accuracy when using the iPad over the iPad Mini is not significant, this hints, that there is a point, where enlarging the mobile device does not yield better results for accuracy. Since grid sizes bigger than 3x3 virtual buttons are strongly discouraged by Yi et.al. [START_REF] Yi | Exploring user motivations for eyes-free interaction on mobile devices[END_REF], this is understandable.

For the design of virtual buttons we can conclude, that it is important to use the minimal possible amount of buttons and to move secondary functionality into other interaction mechanisms, like gestures or an on-screen menu. Especially since application designers cannot influence the factors of age and touch screen experience of their potential users, they are well-advised to keep the user interface as simple as possible. Especially when developing for platforms that target several different device sizes (such as Android or iOS), the designers have to keep in mind, that they might not know the actual device size their application is used and thus not the actual target sizes of virtual buttons they use.

When using only three different buttons, it is advised to arrange them in column order, as there is no button with lower accuracy in that layout as found out above. In general the 3x1 and 2x2 layouts are most suited for eyes-free interaction. If more functions are needed, applications designers should take care to identify critical functionality, that causes most problems if invoked by mistake. Important functions should be assigned to a button in a corner of the device, unimportant ones can be connected with the middle buttons. The centre button in the 3x3 layout has a special role, as it has higher accuracy than the buttons to its sides, but less than the corner buttons.

We can also conclude, that virtual buttons is a concept, that is simple enough for even some small children to understand and use properly. As the number of children owning their own touch device is steadily increasing, this is an important point for application design. While the actual accuracy numbers are fairly low for the children test group, one has to keep in mind that eyes-free interaction is not easy for children at all. As future work the results mentioned in this paper need thorough evaluation to statistically prove their validity. Especially looking for a perfect size and the influence of grid layout and device size is very interesting for further evaluation. Still the results presented can be used as a hint for designing apps using the virtual button approach.

Table 1 .

 1 Base Results. Values are divided into children and adult values. Duration is average duration per touch in seconds.

	Classification	Accuracy		Duration
		Adults	Children	Adults	Children
	Orientation
	Portrait	0.9638	0.8222	0.7840	3.008
	Landscape	0.9538	0.7928	0.8534	2.972
	Device
	iPad 2	0.9736	0.8571	0.7689	1.629
	iPad Mini	0.9687	0.8115	0.7736	1.434
	iPhone 4S	0.9300	0.7566	0.8863	5.887
	iPhone 4S (Thumb)	0.9500	N/A	0.8440	N/A
	Owns Touch Device
	No	0.9515	0.8595	0.9207	6.175
	Yes	0.9631	0.7756	0.7947	1.579
	Experience Level
	1	0.9203	N/A	0.9637	N/A
	2	0.9391	N/A	0.7848	N/A
	3	0.9717	N/A	0.8681	N/A
	4	0.9798	N/A	0.8277	N/A
	5	0.9695	N/A	0.7405	N/A
	Gender
	Male	0.9557	0.7637	0.7640	3.967
	Female	0.9597	0.8948	0.9341	1.648

Table 2 .

 2 Accuracy and duration by minimal dimension of target button (i.e., a button size of 10x15mm has a minimal dimension of 10mm). Columns contain mean values and standard deviation. Size is given in milimeters and duration in seconds.

	min.	Accuracy	Duration
	Size	Adults	Children	Adults	Children
	19.5	0.9349 ± 0.1103	0.7448 ± 0.2805	0.9191 ± 1.2987 5.647 ± 16.3594
	29.3	0.9569 ± 0.1009	0.8139 ± 0.2489	0.8223 ± 0.6596 6.238 ± 23.7814
	38.4	0.9464 ± 0.1071	0.7595 ± 0.2443	0.7452 ± 0.2164 5.681 ±17.0854
	44.9	0.9645 ± 0.0907	0.8050 ± 0.2595	0.7843 ± 0.1672 1.524 ± 0.6258
	61.9	0.9683 ± 0.0679	0.8464 ± 0.1685	0.7896 ± 0.2002 1.660 ± 0.6454
	66.7	0.9665 ± 0.0749	0.7970 ± 0.2514	0.7764 ± 0.2366 1.344 ± 0.6166
	67.4	0.9790 ± 0.1026	0.8735 ± 0.2013	0.7333 ± 0.1874 1.385 ± 0.6058
	80.4	0.9692 ± 0.0666	0.8711 ± 0.1413	0.7591 ± 0.1591 1.678 ± 0.7999
	92.9	0.9938 ± 0.0166	0.8965 ± 0.1803	0.7190 ± 0.1111 1.503 ± 0.5611

Table 3 .

 3 Accuracy and duration by size of the button. Columns contain mean values and standard deviation. Size is given in square millimeters and duration in seconds.

	Sq.	Accuracy		Duration
	Size	Adults	Children	Adults	Children
	750	0.9223 ± 0.1048 0.7192 ± 0.3213 0.9038 ± 0.2660 5.8143 ± 17.620
	1125	0.9397 ± 0.1203 0.7679 ± 0.2690 0.8764 ± 0.9219 6.4241 ± 24.571
	1688	0.9777 ± 0.0805 0.8415 ± 0.2279 0.8240 ± 0.9643 5.6302 ± 16.959
	2250	0.9538 ± 0.0918 0.7573 ± 0.2474 0.8348 ± 1.5992 5.5466 ± 16.877
	2993	0.9611 ± 0.0781 0.7941 ± 0.2644 0.8260 ± 0.1703 1.6114 ± 0.5039
	4490	0.9645 ± 0.0992 0.8012 ± 0.2779 0.7965 ± 0.2172 1.4944 ± 0.6702
	4977	0.9625 ± 0.0836 0.8297 ± 0.1682 0.8210 ± 0.1668 1.7047 ± 0.4986
	6735	0.9790 ± 0.1026 0.8735 ± 0.2013 0.7333 ± 0.1874 1.3853 ± 0.6058
	7465	0.9700 ± 0.0625 0.8734 ± 0.1535 0.7905 ± 0.1977 1.8205 ± 0.8531
	8980	0.9710 ± 0.0569 0.8058 ± 0.2277 0.7063 ± 0.1505 1.3203 ± 0.6133
	11198 0.9938 ± 0.0166 0.8965 ± 0.1803 0.7190 ± 0.1111 1.5032 ± 0.5611
	14930 0.9718 ± 0.0583 0.8527 ± 0.1573 0.7108 ± 0.1632 1.4917 ± 0.6016