Alejandro Moreno
email: [a.m.moreno@utwente.nl

Robby Van Delden

Dennis Reidsma
email: d.reidsma@utwente.nl

Ronald Poppe
email: r.w.poppe@utwente.nl

Dirk Heylen

An Annotation Scheme for Social Interaction in Digital Playgrounds

Keywords: Observation scheme, children's play, social interaction, digital playground

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

1 Introduction "... civilization is, in its earliest phases, played. It does not come from play... it arises in and as play, and never leaves it." Huizinga [START_REF] Huizinga | Homo Ludens: A Study of the Play Element in Culture[END_REF] stated that play has a major role in shaping human behavior. Even though people of every age play, children's play in particular has attracted a lot of interest in research fields such as health and well-being, psychology, medicine, among others. Some studies have identified a relation between obesity and children's current play habits [START_REF] Vandewater | Linking obesity and activity level with children's television and video game use[END_REF]. Another study showed that children who fail to engage in social interaction during play, or show irregularities while doing so, can be linked to several specific mental or social impairments such as autism or mental retardation [START_REF] Guralnick | Stability, Change, and Correlates of the Peer Relationships of Young Children With Mild Developmental Delays[END_REF].

Children's play often takes place at playgrounds. Playgrounds can take many forms ranging from the traditional school or park playground to novel interactive digital playgrounds. Digital playgrounds differ from traditional ones in that they are augmented with different kinds of technologies that can enhance engagement, entertainment, social interaction, physical exertion and immersion [START_REF] Bichard | Pervasive play, immersion and story: designing interference[END_REF][START_REF] Seitinger | An ecological approach to children's playground props[END_REF]. For instance, Wyeth et al. [START_REF] Wyeth | Stomp: an interactive platform for people with intellectual disabilities[END_REF] designed an interactive floor to promote social and physical interaction among mentally challenged people. Soler and Parés designed an interactive slide to counter the lack of physical activity and socialization of children [START_REF] Soler-Adillon | Interactive slide: an interactive playground to promote physical activity and socialization of children[END_REF]. Seitinger et al. developed the Interactive Pathway to research how children's play patterns could be affected by modifying the playground itself [START_REF] Seitinger | A new playground experience: going digital?[END_REF].

We aim to develop a new type of interactive playground that is able to automatically sense social behavior during children's play and respond to it. To accomplish this we need data to train algorithms for the recognition of social behavior. This means that reliable ground truth needs to be obtained through annotation schemes. These schemes serve three main purposes. First, they would allow digital playgrounds to be capable of online, automated sensing of interesting behavior, trained from prior offline annotation. Second, they facilitate the evaluation of reaching higher end goals such as learning social skills, facilitating play for specific target groups [START_REF] Wyeth | Stomp: an interactive platform for people with intellectual disabilities[END_REF] or increasing physical interaction [START_REF] Lund | Playware technology for physically activating play[END_REF]. Third, they would help in understanding the impact of the responses of the playground on social behavior. However, due to the lack of annotation schemes, progress in the field is hampered [START_REF] Bakker | OPOS: an observation scheme for evaluating head-up play[END_REF]. Moreover, existing observation schemes are either very general (applicable to different settings but lacking depth) or are tailored and designed with specific settings in mind (only applicable in the intended setting).

We propose an observation scheme designed specifically to evaluate children's social interactions during play in digital playgrounds. It was designed by analyzing theories and schemes from literature and recorded play sessions. For the latter, we used the Interactive Playground, a digital playground developed to encourage physical activity, competition and cooperation [START_REF] Tetteroo | Design of an interactive playground based on traditional children's play[END_REF]. Our scheme provides an in-depth observation methodology focusing on the social domain that allows the annotation of key social interactions along with useful information such as physical activity. From a bottom-up processing standpoint, it would allow for online, automatic social signal processing by identifying low level features related to specific social behaviors (modeled from offline annotations). From a top-down processing standpoint, it would facilitate evaluating the impact of modifying game dynamics such as success in promoting/reducing specific behavior, enhancing/reducing levels of engagement, immersion and fun. It must be noted that we present a scheme to describe social behavior, not a user experience evaluation scheme, which will help design socially aware digital playgrounds.

Existing Observation Schemes

Below we present a list of some of the widely used or recently created observation schemes focused on children's play in playgrounds. For more schemes to measure (anti-)social behavior from a clinical standpoint, please refer to [START_REF] Cook | A review of defining and measuring sociability in children with intellectual disabilities[END_REF][START_REF] Leff | Using participatory research to develop a playground-based prevention program[END_REF].

Play Observation Scale

The Play Observation Scale (POS) was proposed by Rubin in 1989 and revised in 2001 [START_REF] Rubin | The Play Observation Scale (POS). Published by the Center for Children, Relationships, and Culture of the University of Maryland[END_REF]. Rubin tried to combine the two mainstream theories used in studying The scale is separated into three main play categories, each of which contains different aspects of play (Figure 1). The social play category deals with grouping and children's interactions, the cognitive play category is related to the type and role of play, and finally the non-play behaviors category addresses a broad range of items including social interactions as well as items outside of the actual play. The cognitive play category is nested into the social play category e.g. the child can show solitary-functional play, group-constructive play, etc.

The annotation is done by observing a child for 10 seconds, and then selecting the predominant behavior within this time lapse from a list of predefined actions. No child should be observed for more than five successive minutes to avoid subjective annotations. In total at least 15 minutes should be analyzed, thus several sessions are needed to be able to evaluate a single child.

According to Rubin, the scale is useful in identifying age and sex differences in play, effect of ecological setting in play and identifying children 'at risk' of developing psychological problems. This scheme was conceived to be very general, which has the advantage of making it applicable under different contexts to study children's play. However, when in need of more detailed behavior analysis, the categories are too coarse. For instance the distinction between competitive and collaborative play is not clearly present in the scheme. Also, there is no measurement for physical activity. Non-play behaviors do include some relevant social behaviors, such as aggression, rough-and-tumble and onlooker/unoccupied behavior.

Outdoor Play Observation Scheme

The Outdoor Play Observation Scheme (OPOS) was proposed by Bakker et al. in 2008 [1] and can be seen in Figure 2. They stated it was imperative for the HCI community to develop a structured methodology for observation of play. They were specifically interested in an observation scheme applicable in the evaluation of Head-Up games (HUGs) which are instrumented games for children that can be played without the need to look down [START_REF] Soute | Head Up Games: combining the best of both worlds by merging traditional and digital play[END_REF].

In OPOS, two different types of approach are considered: event coding (frequencybased approach) and state coding (duration-based approach). Event coding is related to event-sampling; i.e., registering specific point-events such as yelling. On the other hand, state coding refers to actions that are continuous in nature, or at least have a measurable duration such as running. They also decided it was beneficial to analyze every child individually to avoid missing important information not measurable when taking the whole group as the basic unit.

OPOS is composed of four different behavioral categories. One of the categories was added just for practical purposes such as annotating children being out of sight. This category, named "general", is therefore not present in Figure 2. The physical activity category refers to the level of physical exertion exhibited by the players, the focus category indicates the player's gaze object of interest, and lastly the social interaction category deals with communication (verbal and non-verbal) in the broadest sense of the word.

OPOS was tested by evaluating a game called Lighthouse. The authors obtained a multi-rater κ measure of .7 for physical activity, .45 for focus and .24 for social interaction when comparing individual classified items among three coders. When comparing the total amount of occurrences of any of the items, all κ's increased (.9, .74, .73 respectively). Even though the scheme proves useful in finding differences in play, we find that the social interaction category is too coarse (functional vs non-functional only) for our purpose. For instance, there is no way to indicate social interactions such as competition/collaboration, roughand-tumble or leadership. The physical activity category, however, is useful.

Manchester Inventory Playground Observation

Manchester Inventory Playground Observation (MIPO) was proposed by Gibson et al. in 2011 with the goal of evaluating children's play for clinical analysis [START_REF] Gibson | Quantifying peer interactions for research and clinical use: the Manchester Inventory for Playground Observation[END_REF]. They tried to determine social skills problems in children during play since previous studies showed that developmental disorders were associated to social functioning difficulties [START_REF] Guralnick | Stability, Change, and Correlates of the Peer Relationships of Young Children With Mild Developmental Delays[END_REF]. Playgrounds, which are fairly challenging social environments that require complex social interactions, lend themselves for useful studies of real time social information processing.

MIPO was designed to be a real-time social interaction observation scheme usable both for clinical analysis and research in general. It was structured so that it could identify several different patterns of social impairments across several disorders. It is composed of four main categories of peer socialization; each one containing different observations resulting in 28 different items as seen in Figure 3. The pro-social skills category mainly deals with grouping behavior and inclusion. The conflict management deals with types of aggression. Confiding and care concerns helping behavior. Atypicality deals with indicators of unusual, unwanted social behavior such as shouting out for no reason.

Each of the items was rated on a 4-category, rank-ordered rating scale that went from socially competent in this domain to marked difficulties. The annotators first observed the behavior of a randomly chosen child from a control group with no psychopathologies. We found that MIPO was one of the most complete observation schemes in terms of social interactions. However, it neglects other areas relevant to children's play such as the phases of play and the level of physical activity. Evidently, since the scheme was designed specifically for clinical identification of social skill impairment, there was no need to observe other type of actions or behaviors.

Overall Conclusions

We found that current schemes provide several useful categories and items such as physical activity and some non-play behaviors (rough-and-tumble, aggression, etc). However, we found that the social categories did not suffice for our purpose. We are interested in studying a wide array of social behaviors (similar to MIPO), but also how they are related to play itself. We noticed that current schemes are built around play implicitly; they observe play through related categories but never address it as is. Morrison et al. proposed a set of terms to categorize phases that may emerge during play in the context of interactive art installations [START_REF] Morrison | Building sensitising terms to understand freeplay in open-ended interactive art environments[END_REF]. They proposed terms such as speculative play, interactive play or comprehension stages that we found applicable to describe play in interactive playgrounds as well. We decided that studying real play sessions in a digital playground was necessary to define other important items that could be added to the scheme.

The Interactive Playground

The Interactive Playground was implemented by Tetteroo in 2010. It was designed to stimulate physical activity, collaborative and competitive behavior during children's play. It is composed of a top-down projector and a near-infrared camera located on the ceiling, several hats with infra-red reflectors on it, balls and wristbands worn by the children, both with wireless motion sensors (Sun SPOTS). The projection on the floor of the room has several shapes like triangles, circles and squares that can be interacted with. Music is played and several sound effects are also emitted when certain events happen. The most important system interactions are listed below:

-If a player with a ball enters the playground he is surrounded with a colored shape; if entering without a ball, a short trail of shapes will follow him.

-If a player chases a free shape long enough the shape is added to his trail.

-If a player shakes the ball it will produce 'venom', killing nearby player's shapes. For every shape killed, his colored shape will grow. -If a player with a ball is being chased by another player with a ball, he will lose the last shape of his trail to the chasing player.

Experimental Setup

Children were instructed to play in groups of two to four befriended children. A play session was composed of two sub-sessions of about ten and a short break between the two sub-sessions. During the break, the functioning of some of the system interactions was explained to the children. In total, 39 children (aged between six and twelve) and around four hours of play were recorded. The system has been evaluated in a darkened controlled environment. At the end of every play session, a group discussion was carried out to get a better insight in the children's level of understanding of the provided interactions and their experiences. A complete description can be found in [START_REF] Tetteroo | Design of an interactive playground based on traditional children's play[END_REF].

General Behavioral Observations

Tetteroo noticed that although some interactions were targeting collaboration, most interactions witnessed were based on individual play with the system or were competitive by nature. Nonetheless, even when the children were engaged in individual play, some social interaction between players still occurred. For instance, explaining out loud how certain interactions worked, which sometimes led to responses from the other players.

Tetteroo concluded that most interactions with the system could be triggered easily and were attainable in several seconds mainly because of the limited size of the playground. This might have led to the relatively low occurrence rates of cooperation and intensive physical activity. Although children were observed sitting or standing still, the predominant behavior was non-intensive physical activity. Because of the diverse interactions, children took some time to realize / understand how the playground responded to their actions and explored with different possibilities. This, in turn, gradually led to further discussion amongst them and the discovery of new interaction forms.

An interesting observation is the remarkable number of times that roughand-tumble play occurred. Children were seen pushing one another, chasing / kicking each other and frantically throwing balls at each other. Very often the children were trying to step on the shapes on the floor and, sometimes, on other children's feet. They were also observed dragging each other around when someone fell down or was tripped by a sliding tackle. Sometimes, but not often, violent behavior was exhibited; i.e., having the intention to physically harm another. Aside from the physical contact, some children executed performances like swimming on the floor, spins, tricks, and dances. They were sometimes followed by others that showed the same kind of behavior on their own initiative or after being told or hinted to do so by another player.

Proposed Observation Scheme

The information gathered from observing play in the targeted context helped define and corroborate items that should be available in the scheme. We designed an observation scheme for interactive playgrounds with a focus on social interactions between children. Our goal is to analyze children's play from a social interaction standpoint; identifying the different interactions present and studying how playground characteristics influence their occurrence. The scheme borrows concepts from the previously discussed schemes, social and psychological theoretical background, but also proposes new categories and observable behaviors especially in the social domain. The scheme was refined iteratively in two rounds (a pilot and final evaluation). The proposed scheme can be seen in Figure 4.

The scheme is composed of three main categories covering the social interactions we want to focus on, the higher end goal of increasing physical activity and the relation to the phase of play a child is in. The social interactions included in the scheme were chosen because they occur often, their relation to play is important, can possibly be influenced by changing game dynamics and hopefully can be automatically detected.

Our observations were carried out on a per child basis to prevent generalizing and avoid the risk of missing important individual behavior such as normally inactive participants suddenly engaging in play, increasing physical activity or social interaction [START_REF] Bakker | OPOS: an observation scheme for evaluating head-up play[END_REF][START_REF] Wyeth | Stomp: an interactive platform for people with intellectual disabilities[END_REF]. Also, one of the main annotators did not use the sound of the footage, neither in the pilot nor the final annotation, to analyze the suitability of using computer vision algorithms for automatic recognition in the future.

Units of Annotation

When annotating, the first decision to make is whether predefined time slots or manually annotating the exact duration of an action is more beneficial. Using time slots makes the annotation easier and faster, increasing the efficiency of the annotation. It also allows the annotation of several children concurrently. However, when only one label can be assigned to a time slot and two or more actions take place simultaneously, the dominant one should be chosen, which can lead to discrepancies. One could solve this by allowing multiple labels in one time slot when the nature of the category allows this. On the other hand, annotating the exact duration of an action gives more accurate labeling (useful for machine learning) but is much more time consuming since each child needs to be annotated individually and the entire video needs to be watched for each of the children. If time slots are chosen, another issue would be to define the duration of the time slots.

We noticed that for phase of play, a longer duration is useful since phases will not change that rapidly (we chose eight second sequences). In the case of social behavior, smaller sequences are more appropriate since most are usually completed rather quickly (we chose two second sequences). Lastly, physical activity will mostly show normal intensity, with bursts of intense/no activity, and thus the duration of the bursts was manually annotated. This led to the problem of thresholding which will be explained in the next section.

Detailed Description

Phase of play refers to the different stages that a child can go through when playing and are based on [START_REF] Morrison | Building sensitising terms to understand freeplay in open-ended interactive art environments[END_REF]. This dimension gives an indication of how simple the game is and whether children will play it. The observable behaviors are annotated exclusively, which means only one of them can be labeled in each time slot. One might find relationships between the phase of play and certain social behaviors. These findings could help in preventing or triggering certain social behaviors and in improving social signal processing.

Social behavior is the category that identifies different social interactions that occur when children play. Arguably, this might be one of the most important aspects of play. According to Bandura's Social Learning Theory: "Learning would be exceedingly laborious, not to mention hazardous, if people had to rely solely on the effects of their own actions... most human behavior is learned observationally through modeling: from observing others..." [2, p22] . In the same line, Vygotsky's Social Development Theory encompasses that social interaction plays a fundamental role in the process of cognitive development [START_REF] Vygotsky | Mind in Society: The Development of Higher Psychological Processes[END_REF]. Lastly, Erikson's Psychosocial Development Theory defines a specific stage (macrosphere play) where children master social interactions and realize they can be successful in the larger social world [START_REF] Erikson | Childhood and Society[END_REF].

The social behavior dimension contains two items based on POS: Violence (based on their item of aggression) and Rough-and-Tumble. Making the distinction is important because, according to Hughes [START_REF] Hughes | Children, Play, and Development[END_REF], aggressive play should not be discouraged as it allows children to release their anxiety. It should, however, be controlled. He also mentions that play should be nonliteral, which is related to the performance item (such as dancing or doing flips) which was observed quite often in the recordings and is also mentioned in [START_REF] Lund | Playware technology for physically activating play[END_REF]. Helping can be linked to the more detailed caring category of MIPO. Mimicry, besides being a specific interest of our research, is mentioned in [START_REF] Morrison | Building sensitising terms to understand freeplay in open-ended interactive art environments[END_REF]. Erikson mentioned that during the third stage of psychosocial development (Initiative vs. Guilt) children need to lead or direct play and social interactions in their attempt to assert their power over the world [START_REF] Erikson | Childhood and Society[END_REF]. Leadership, cooperation and competition were witnessed in the recordings and therefore found relevant to code in relation to play. The last item, Other social behavior (OSB) refers to any interaction not covered by the other items in the social behavior dimension, that would imply that 2 or more children are interacting and belong to a group for the duration of this interaction.

The social behavior dimension may inform whether the playground, or parts of the playground, increase these social interactions or if unwanted social behavior like violence could be diminished. At the same time, it paves the way for a more socially aware digital interactive playground. These behaviors are non-exclusive and thus multiple labels can be assigned simultaneously.

Physical activity categorizes the amount of physical effort that the children are doing at a given time. In [START_REF] Hughes | Children, Play, and Development[END_REF], Hughes mentions that play should keep children actively engaged, involved psychologically and physically. This conveys why this category is one of the common goals in interactive playgrounds. One might benefit from automatic measurement of this dimension by using, for instance, the number of steps [START_REF] Loprinzi | Measuring Children's Physical Activity and Sedentary Behaviors[END_REF], motion energy images or skeleton tracking. This is an exclusive category.

The complete table with an explanation of the specific observable behaviors can be found in the supplemental material provided1 . Potential uses of this scheme include possible identification of ambiguous social signals, physical cues related to certain social interactions, identifying how playground characteristics affect social interactions and its effect on the phases of play, among others.

Analysis and Evaluation

Training and Annotation Setup

We annotated a subset of the videos using our proposed scheme. The annotation was performed using ELAN [START_REF] Sloetjes | Annotation by Category: ELAN and ISO DCR[END_REF] from the Max Planck Institute for Psycholinguistics in Nijmegen, the Netherlands. Four annotators were involved in the coding. Two of the annotators, closely involved in the creation of the scheme, participated in both sessions. Two external annotators, one participating in the pilot and the other one in the final evaluation, had no participation in the creation of the scheme. The two main annotators had a training session of coding three minutes of footage. The two external annotators were given an explanation of the scheme and its use followed by a training session and a discussion of the labeled fragments. For the pilot scheme training, the explanation lasted 30 minutes and the training video lasted 30 seconds. For the final scheme training, the times were twice as much to provide a better explanation. The training sessions covered most items; examples of the unseen items from the scheme were shown by fast forwarding through the training video.

For the pilot annotation, all annotators went through 10 minutes of video, separated into three videos of three minutes and 20 seconds each. The subsets of the three videos covered the beginning, middle and end of different sessions, respectively. For the final evaluation, only three minutes and 20 seconds were annotated. As explained before, one of the main annotators did not use the sound of any of the footage.

Shortcomings of the Pilot Scheme

Comparing annotators in the pilot study resulted in rather low Cohen's κ-values for all the dimensions. For a dimension such as social behavior, which can be quite ambiguous or subjective, this could be expected. However, the physical activity [START_REF] Loprinzi | Measuring Children's Physical Activity and Sedentary Behaviors[END_REF] and grouping dimension are considered objective dimensions and should have obtained a high agreement. Phase of play, on the other hand, should have obtained at least a modest agreement.

We identified some problems both in the scheme design and evaluation procedure. In regards to the design, we noticed that some items were not defined explicitly which led to ambiguities when annotating. For example, it was not stated if rough-and-tumble was to be used for every physical contact, intentional or not, or if it should be labeled for both children or only the one doing the contact. This problem was solved with a more comprehensive description of each item in the scheme manual and a more thorough training session. Besides this, we had also included a grouping dimension which created a lot of confusion due to distance restrictions between children and amount of interaction required to be labeled a group. Since physical grouping can be obtained accurately using computer vision, manual annotation seemed redundant. We decided to move towards a higher level meaning of grouping where intention, and not location, was the focus. We added the other social behavior item to the social behavior dimension and removed the grouping dimension.

In regards to the evaluation procedure, the physical activity dimension was first coded using defined time slots of eight seconds. However, agreeing on which was the dominant behavior for some time slots was problematic. To try to overcome this problem, we manually labeled bursts of intensive or none physical activity in the final version.

Results and Analysis of Final Scheme

Phase of Play Dimension had a fair agreement between the two main annotators (κ of .41). However, it had a low reliability when comparing the external annotator (κ of .08 and .09). When grouping together playing with the system and exploration, the resulting κ with the external annotator went up to .53 and .51 respectively (see Table 1). Tetteroo's Digital Playground is designed to allow children to explore and interact freely with the system, find new interactions, explore and interact with the system again. Our results show that differentiating playing with the system from exploration will be challenging in playgrounds that promote free play and emerging interactions due to the numerous phase transitions in the sessions. In these settings, combining both items would yield a higher inter-observer reliability while still providing useful information about the phase of play. For games with more strict or structured phase transitions it will be easier to annotate this dimension.

It is noteworthy that audio played some role in the ability to correctly annotate this dimension. For instance, sometimes children were asking "how can you get the Pacman?", clearly indicating it was an exploratory endeavor that could not be coded correctly without the use of audio. Nonetheless, the degree to which audio affected the results was not high.

Physical Activity Dimension had a low agreement between the main annotators (κ of .32), especially considering physical activity ought to be an objective measurement, shown to have high agreement when observers are intensively trained using strict protocols [START_REF] Loprinzi | Measuring Children's Physical Activity and Sedentary Behaviors[END_REF]. When comparing the external annotator to the main ones, we obtain κ values of .5 and .32. Two important observations were seen i)intensive and none items were never confused (see Tables 2 and4) and ii) whenever either annotator A or the external annotator labeled a burst of activity, annotator B almost always agreed (92% for none and 88% for intensive with annotator A, 86% and 83% for the external annotator). Thus, we can conclude that the problem is the threshold at which the annotators separate intensive activity from normal, and normal from none. For very intensive or obvious lack of physical activity, the agreement is extremely high, but in cases where the activity is somewhere in between, agreement is lacking. Agreeing on a threshold for physical activities proves quite challenging: first, because expressing this with words is not trivial, second, because listing every action the children could execute is near to impossible. Using for instance up to two steps as threshold for no physical activity would not suffice as children might be intensively shaking using their entire upper body while standing still. The reliability of this dimension will depend on the type of playground, or on being able to define strictly the threshold between the level of activities before the annotation. For playgrounds with a lot of freedom of movement it will be hard to annotate this dimension.

Social Behavior Dimension had a high agreement between the main annotators with a κ of .67 (see Table 5). On the other hand, agreement between the external annotator and main annotators A and B was κ 0.28 and 0.37 (see Table 6) respectively. Agreeing on when a behavior happened was problematic, thus we also analyzed, when both annotators agreed some behavior was underway, if they agreed on which one it was. The results showed that the agreement was quite high considering this is a subjective dimension (External annotatormain annotator A 0.66, External annotator -main annotator B 0.67, two main annotators 0.92).

Table 5. Confusion matrix for the social category of the two main annotators, κ is .67 (when taking out the empty slots, κ is 0.92) -lea rat vio hel per mim coo com osb -204 0 3 0 0 9 0 1 12 6 lea 8 2 1 0 0 0 0 1 0 0 rat 3 0 67 0 0 0 0 0 0 0 vio 0 0 1 1 0 0 0 0 0 0 hel 5 0 0 0 2 1 0 0 0 2 per 5 0 0 0 0 18 0 0 2 0 mim 1 0 0 0 0 0 5 0 0 0 coo 5 0 0 0 0 0 0

3 0 0 com 2 1 0 0 0 0 2 0 35 0 osb 27 0 0 0 0 0 0 1 1 22
Table 6. Confusion matrix for the social category of main annotator B and the external annotator, κ is 0.37 (when taking out the empty slots, κ is 0.67)

-lea rat vio hel per mim coo com osb -186 6 1 0 3 3 Rough-and-tumble was the most frequently annotated behavior and it was often accompanied by signs of competition (although the external annotator failed to see several occurrences of quick physical contact) which shows that identifying relations between social interactions is possible. Performance was also seen often during various parts of the game where the kids wanted to show off or just have more fun. There were some discrepancies in leading but when the videos were analyzed, all but one could be accounted to not using audio by one of the annotators. It is interesting that actually all but one coded instance of leadership were accompanied with speech. The influence of speech was also seen in help, where helping verbally could not be annotated without audio.

It can be seen in Tables 5 and6 that interestingly, competition and cooperation were not confused even without the use of sound. In many cases speech contained information about the correct social signal, however it was often ac-companied with clear physical cues. We noticed that various types of social interactions can be identified accurately without using audio but could benefit from it nonetheless. This seems promising for the automatic recognition of social signals in digital playgrounds using computer vision. For instance, sudden invasion of personal space by other children could signal aggressive behavior or competition, whereas synchrony in movement and actions could signal cooperation.

Conclusions

We have proposed a new annotation scheme for observational studies in digital playgrounds. The scheme is composed of three dimensions and focuses on children's social interactions during play, and their relation to play itself. It was designed using relevant elements from current schemes, psychology and sociology theories and observations from recorded play sessions. On average, the agreement was fair which means the scheme still needs to be refined. Nonetheless, results show that the scheme is useful in identifying relations between different social interactions. This provides information directly related to the impact that changing certain game dynamics to promote a specific kind of interaction can have on the game. They also show that, for certain social interactions, automatic recognition could be possible with a reasonable accuracy even without the use of audio. The scheme was designed having our own goals in mind, however it remains generic enough to be used in other settings. We regard the current state of the scheme as a good starting point, either as a base scheme for more specific ones, or for further development in creating a general socially focused annotation scheme for the evaluation of digital playgrounds.

  This publication was supported by the Dutch national program COMMIT.

Fig. 1 .

 1 Fig. 1. Play Observation Scale composition.

Fig. 2 .

 2 Fig. 2. Outdoor Play Observation Scheme composition children's play: the social one proposed by Parten and the cognitive one promoted by Piaget. The purpose of this scale is to assess children's free play preferences.The scale is separated into three main play categories, each of which contains different aspects of play (Figure1). The social play category deals with grouping and children's interactions, the cognitive play category is related to the type and role of play, and finally the non-play behaviors category addresses a broad range of items including social interactions as well as items outside of the actual play. The cognitive play category is nested into the social play category e.g. the child can show solitary-functional play, group-constructive play, etc.The annotation is done by observing a child for 10 seconds, and then selecting the predominant behavior within this time lapse from a list of predefined actions. No child should be observed for more than five successive minutes to avoid subjective annotations. In total at least 15 minutes should be analyzed, thus several sessions are needed to be able to evaluate a single child.According to Rubin, the scale is useful in identifying age and sex differences in play, effect of ecological setting in play and identifying children 'at risk' of developing psychological problems. This scheme was conceived to be very general, which has the advantage of making it applicable under different contexts to study children's play. However, when in need of more detailed behavior analysis, the categories are too coarse. For instance the distinction between competitive and collaborative play is not clearly present in the scheme. Also, there is no measurement for physical activity. Non-play behaviors do include some relevant social behaviors, such as aggression, rough-and-tumble and onlooker/unoccupied behavior.

Fig. 3 .

 3 Fig. 3. Manchester Inventory Playground Observation composition

Fig. 4 .

 4 Fig. 4. Final annotation scheme composition with abbreviations between brackets.

Table 1 .

 1 Confusion matrix for the category of phase of play of the two main annotators, κ is .41

		not exp pws pns -
	not 0	0	0	0 0
	exp 0 35 15 1 0
	pws 0	8 28 0 0
	pns 0	0	0	0 0
	-	0	7	3	0 3

Table 2 .

 2 Confusion matrix for the physical activity category of the two main annotators, κ is .32

		non int -
	non 23 0 2
	int 0 30 4
	-	26 21

Table 3 .

 3 Confusion

		matrix for the
	physical activity category of the exter-
	nal annotator and main annotator A, κ
	is .0.5
		non int -
	non 24 0 2
	int 0 30 7
	-	11 6

Table 4 .

 4 Confusion

		matrix for the
	physical activity category of the exter-
	nal annotator and main annotator B, κ
	is 0.29
		non int -
	non 31 0 5
	int 0 29 6
	-	24 23

hmi.ewi.utwente.nl/playground-annotation-scheme-manual.pdf


Benjamin Walther-Franks

Florian Biermann

Nikolaas Steenbergen

Rainer Malaka

The Animation Loop Station: Near Real-time Animation Production

Keywords: computer animation, computer puppetry, performance animation, layered animation, loop stations, performing arts

We present the animation loop station, a near real-time animation production system. It brings the concept of loop stations or loopers commonly used for live music performances to the animation domain. Our system allows a single animator to build an animation layer by layer by capturing his own movement and building up complex motion through several passes or takes. In order not to interfere with capture and not break the performer's flow, control commands are handled by a speech interface.

Introduction

Motion capture is a method for fast, cost-efficient computer animation. It is widely used in the film, television and computer game industries. The increasing availability of low-cost full-body and facial capturing devices such as Microsoft's Kinect even moves motion capture in the reach of novices and small companies [START_REF] Leite | Anim-actor: understanding interaction with digital puppetry using low-cost motion capture[END_REF]. But while this approach already speeds up production times in comparison to offline animation practices such as keyframe animation, the motion capture workflow is still complex, requiring animators to clean, edit and apply capture data to rigged characters. Thus, while mocap includes real-time elements, the entire workflow production is still far from real-time.

In the music industry, changing consumer behavior has shifted the emphasis from selling studio productions toward live events. Simple devices, loop stations, enable small bands or even single person acts to create high fidelity songs on stage without pre-recorded material. By recording samples on stage, looping them, and cleverly layering multiple sample loops a single artist can create complex pieces entirely on his own, live, in front of an audience. The realtime music production becomes as much part of the experience as the "resulting" piece.

Looper workflows enable a rapid production style with a "live" quality that other areas of media production, such as animation, can benefit from. All that is lacking are the right concepts and interfaces. As a first step in this direction, we present the animation loop station, a near real-time animation production system. It enables a single artist to create complex animations just by acting out and layering the animation, much like a musician constructs a song on stage by layering tracks. Building on recent developments in the area of computer puppetry [START_REF] Dontcheva | Layered acting for character animation[END_REF][START_REF] Sturman | Computer Puppetry[END_REF], we have developed an environment that truly moves computer animation into the domain of real-time production.

The Animation Loop Station

Loop stations, loopers, or live looping samplers, are hardware or software devices that enable easy recording, playback and layering (overdubbing) of samples. They usually take the form of small hardware boxes, with pedals for foot activation, such as the DigiTech JamMan series1 . Software loopers, such as Sooper-Looper2 (which is in turn based on the Echoplex Digital Pro LoopIII and LoopIV hardware devices) run on common operating systems and only require the appropriate audio I/O to produce the same functionality.

These devices enable recoding and editing in the production domain (audio), while control (record, play, undo, stop, clear) is administered through hardware or software pedals and buttons, essentially low-fidelity spatial input. For animation, we need to record spatial input to bring virtual characters to life, occupying spatial input for the production domain (3D motion). In our animation looping system, we thus switch the modalities to aural input for control and gestural input for content creation. Since only 3D body motion is recorded, the aural channel is free for recording, layering and playback control.

Capture

The animation loop station offers two control modes, based on distinct animation metaphors. For "classical" full body motion capture we have the Performer mode, which maps the movements of the loop station user's skeleton onto a humanoid-like character's skeleton. This is how most motion capture works and it enables a user with a certain capability of bodily expression to create a wide variety of character motion. For animation of non-humanoid characters or other scene elements we have the Puppeteer mode that employs manipulation techniques from virtual reality and computer puppetry. Our current implementation focuses on 6DOF capture for 3D animation, although the loop station concept could be applied to other input devices such as multi-touch interactive screens.

Control

For control via the aural modality we use spoken commands that are detected by state-of-the-art speech detection technology. These are simple one or two word commands that change the mode of the loop station. We implemented commands for basic recording and playback control much like audio loopers offer: "Record", "Play", "Stop", "Undo", and "Clear".

Live Animation Techniques

For both capture modes, we employ the principle of animation layering [START_REF] Dontcheva | Layered acting for character animation[END_REF]. In each pass, certain features will be controlled live by captured input. If these features already have motion, there are three modes of layering:

-Destructive layering replaces the existing motion with the new motion.

-Additive layering adds the new motion to the existing motion (cf. [START_REF] Dontcheva | Layered acting for character animation[END_REF]).

-Blended layering combines new and existing motion with a given weighting.

our system also includes timing modes that use spatiotemporal input to only drive the timing of a previously defined motion, without changing its component. Say the animator is happy with a trajectory but wishes to speed up parts of the motion while slowing down others. The animation loop station offers Dragimation-style performance timing [START_REF] Walther-Franks | DRAGIMATION: Direct Manipulation Keyframe Timing for Performance-based Animation[END_REF] that allows the animator to drag an object through time along its trajectory at the desired pace, and use the new pacing as the new timing.

Implementation

Our current implementation of the animation loop station is written in python as an add-on to the open source 3D modeling and animation software Blender3 . We chose to write an extension for Blender as it offers a powerful python API and a full classical animation pipeline. We use a Microsoft Kinect sensor for fullbody motion capture and audio input. A server application translates skeleton data and detected speech to Open Sound Control4 commands which are sent to the python add-on client. For speech recognition we use the Microsoft Speech Platform SDK.

Use Cases

As a main use case for the animation loop station we see hobbyist and semiprofessional animation production. At little cost, even a single artists can achieve animation results in short time spans. Professional studios could also make use of the technology to quickly create animated storyboards or initial sketches that can then be refined by other animation methods. Combined with high precision tracking devices, it is conceivable that the loop station approach will find its way into professional motion capture workflows.

Another interesting aspect is that by moving animation production closer to realtime it becomes more of an art form that can be consumed for its own sake. Rather than only watching the resulting animation as part of a motion picture or computer game, audiences might find satisfaction in watching the act of production itself. Artists could use the animation loop station as a new means of expression, creating entire animated sequences on the fly in real-time, based on audience feedback and intuition.

Challenges

The key idea of the animation loop station is to enable fast meta-control for capture and layering modes via a different modality than actual capture input so that a single user can do both without having to leave the "flow". This approach does pose problems in certain scenarios. One such scenario is facial motion capture, where the same muscles for producing speech commands are used for creating capture input. Yet the overlap is minimal: After initiating capture with a speech command, facial motion capture can proceed unhindered. The spoken command for ending capture can be recognized and the relevant segment of the capture edited out automatically.

Fig. 1 .

 1 Fig. 1. The animation loop station in Puppeteer (virtual reality-style remote hand interaction; left) and Performer (body-to-body mapping; right) control modes.

http://www.digitech.com/

http://essej.net/sooperlooper/

http://www.blender.org/

http://opensoundcontrol.org/


Vincent Koenig
email: vincent.koenig@uni.lu

Franziska Boehm
email: franziska.boehm@uni.lu

Rod Mccall
email: roderick.mccall@uni.lu

Pervasive Gaming as a Potential Solution to Traffic Congestion: New Challenges Regarding Ethics, Privacy and Trust

Keywords: ethics, privacy, trust, game, car, traffic

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

Introduction

The I-GEAR project (incentives and gaming environments for automobile routing) aims to understand the motivations that drivers have while undertaking their daily commute and then to provide them with a range of incentives to change their behaviour with the aim of reducing traffic congestion. A key aspect of the project is on ways in which the problem could potentially be solved without recourse to expensive roadside infrastructure. As a result our solution involves encouraging commuters to use their mobile devices within a serious pervasive (and persuasive) gaming context. While technical and human-computer interaction aspects are highly relevant within this project a number of the challenges lie outside these areas. For example there is a complex interplay between the technical, human, legal and ethical issues that need to be dealt with. This paper illustrates how the I-GEAR project triggers questions pertaining to ethics, privacy and trust in the context of a serious game that is meant to reduce traffic within Luxembourg. Furthermore, while there are no doubt issues relating purely to Luxembourg the higher level ethical, privacy and trust issues should also be applicable in other countries. We start by outlining the main ideas behind our game model and describe in the second stage specific questions regarding ethics, privacy and trust. It is important to note that this is a sample game design concept and the final design will depend on early user requirements modelling. Additionally, rather than providing out-of-the-box answers, we discuss these issues under the light of specific expertise, that we believe goes someway beyond the traditional interdisciplinary approaches within IT research. Ultimately, we would like this paper to foster awareness for the importance of ethics and legal frameworks to be taken into account in pervasive gaming.

I-Gear: sample game model

The game we intend to develop will run on smartphones for two reasons: firstly, the number of smartphones sold to adults has expanded significantly in recent years (thereby creating a ready potential user base) and secondly, the vast majority of smartphones come with localization (GPS most of the time) and data networking capabilities. Furthermore, in contrast to systems integrated into the cars, this allows us to build on the most recent and widespread technology, to easily bind the device to a specific user, to easily deploy updates and to have easy and instant access to a large number of potential users. I-GEAR relies on the concept of incentives being provided to drivers within a game-like environment. Incentives have already been used within traffic management and studies have found that even for a comparatively low cost people will alter their driving behaviour [START_REF] Bliemer | Rewarding instead of charging road users: a model case study investigating effects on traffic conditions[END_REF]. I-GEAR does not claim to be able to remove costs completely, but instead aims to minimize costs by providing financial and non-financial incentives to encourage drivers to change their behaviour. I-GEAR will develop a pervasive game in the sense that it attempts to alter (or persuade) drivers to change their behaviour through the use of social, psychological, financial or game design incentives. Drivers for example may undertake individual actions such as: giving other drivers priority, taking the slow traffic lane, stopping in designated areas during peak times to reduce congestion, taking alternative routes or car sharing. Such behaviours may also benefit other drivers or groups to which the driver belongs. Part of the challenge within this approach is not only to identify relevant incentives but also gaming techniques that remove some of the need to provide purely financial incentives.

The project will utilize a contextual design [START_REF] Beyer | Contextual design: designing customer-centered systems[END_REF][3] methodology in order to identify specific incentives and gaming strategies. It is our opinion that the nature of the project requires that we allow the end-users to identify and test the incentives that may work given their particular preferences, circumstances or hard requirements. It is also important to explore the order in which people are given incentives such that we encourage good driving patterns early on while also encouraging them to progress further up the ladder. For example we want to specifically avoid traps where one highergrade incentive is perceived as being worth less than one which is easier to attain.

Motivations are a key part of the driving game that is proposed within I-GEAR, for example we do not envisage that one particular type of incentive will work for all drivers. Instead, through the contextual enquiry process, we will seek to identify combinations of incentives and motivations that are applicable either on an individual or group defined basis. Furthermore, we plan to use two main forms of incentive: immediate and status. Immediate rewards will be given when a driver undertakes an action e.g. stopping at a café for which they receive a ½ price coffee. Alternatively status rewards or points will be used to encourage longer-term behaviour where drivers collect points for consistently undertaking good actions.

As noted earlier, I-GEAR will also encourage drivers to collaborate and compete through the use of gaming approaches. This will be achieved by offering both individual and social incentives. The underlying main objective always aims to reduce traffic congestion and improve the quality of mobility in Luxembourg.

The originality of our approach is partially reflected by a combination of aspects, all of them potentially raising issues, if they are not properly dealt with, such as:

• Our game concept uses location data and requires users to be identified; • It is meant to be pervasive as it aims to alter driver behaviour;

• It is meant to share data, enabling collaborative approaches and a shared incentive / reward system; • It clearly combines real-and virtual-world scenarios and, as such, is a serious game; • It is meant to integrate an environment of potential high risk, with complex human factors aspects.

E-P-T issues

We are aware our approach sets several challenges in terms of ethics, privacy and trust. There are other challenges in addition, that will not be dealt with in the context of this paper (e.g. technical aspects, human factors and user acceptance). In the following section, we will discuss separately issues concerning ethics, privacy and to some extent also trust.

Ethics issues

Ethics requirements and constraints are very strong in social sciences; this also applies to research contexts. [4][5]. The main ethical principles are [START_REF]The Official EFPA meta-code of ethics[END_REF]:

1. Respect for the person's rights and dignity 2. Competence 3. Responsibility 4. Integrity

The first principle strongly relates to legal aspects and aims at a person's privacy, confidentiality, self-determination, autonomy and consistency with the law [START_REF]The Official APA Ethics[END_REF].

The second principle describes how techniques of dealing with patients or conducting research need to meet the highest professional standards of current knowledge and training.

The third principle refers to scientific responsibilities regarding consequences of one's actions and work. It basically stipulates that a professional way of acting requires you to be accountable for your actions and the ways they are conducted.

The final principle describes how attitudes and actions should be in line with honesty, fairness and respect of others.

While some of these principles may rather obviously link to our use case, some others reflect the fact that they have been set up for defining rules in a therapeutic setup, typically how psychologists should deal with patients. There are other ethical frameworks that more explicitly fit a context of research; the best known and most cited with regard to research in social sciences is the APA code of conduct "Ethical principles of psychologists and code of conduct" [START_REF]The Official EFPA meta-code of ethics[END_REF]. The most relevant principle with regard to our context is that of informed consent. It should be noted that it strongly relates to the first principle described in EFPA. This principle defines that participants have to be thoroughly informed beforehand about the precise processes they are going to face, their outcomes and objectives. In addition, it stipulates that, once the participants have understood all these facets, they are required to express their consent or disapproval, free of any influences.

Relating the principle of informed consent with respect to our game concept, there are two sequential stages that need to be differentiated: the research stage with intermittent experimental setups (simulator in laboratory environment, later on the road) and the final game release. While ethics is quite well defined for research contexts [], we think this is less obvious for final game releases as planned within I-Gear. A crucial point that needs to be dealt with is the end user license agreement (EULA): this allows making sure (1) the user explicitly consents with all the information about the game provided and (2) certifies being of sufficient age. On the other hand, while we think the EULA is a crucial point, we are also convinced it is a very difficult challenge to constitute an EULA that is both sufficiently detailed with regard to information completeness and still sufficiently readable in order to improve chances the user really considered all the information before expressing his consent. Who has not yet been annoyed by "excessively long" EULA texts, scrolling through endless pages, looking for an "OK" button to hit? Of course, bridging between both aspects, explicitness and readability, may constitute an arguable compromise and legal requirements may conflict with those of readability and event ethics to some extent.

Finally, the ethics aspects could also be discussed under a perspective of freedom of choice (e.g. is a pervasive game, namely that developed in I-GEAR, impeding on the freedom of choice?). We think that this question is already covered by our discussion on informed consent und thus will not be treated as a separate question any further here.

Privacy issues

While ethics requirements with regard to privacy play an important role in social science, they are equally codified in legal provisions. When collecting and analysing different kinds of information in the framework of the I-GEAR project, it is clear that there are legal implications. Questions arising in relation to the amount of data col-lected, the possibilities to share the data with third parties or the general legitimacy of data processing for research purposes are worthy of consideration.

As mentioned before, the I-GEAR project collects different types of information, amongst other, data related to the location of the smart phone, telephone or tablet, GPS data, user IDs, data related to the profile of the user (age, gender, car type, transport references etc.) and data derived from the location information (location data, which direction the users took, preferences regarding the driving behaviour etc.). However, before discussing how to process these data, it is important to distinguish between information and personal data collected during the project. Only when personal data is concerned, some legal limits, named in the following, are applicable. Personal data can be described as "any information relating to an identified or identifiable natural person; an identifiable person is one who can be identified, directly or indirectly, in particular by reference to an identification number or to one or more factors specific to his physical, physiological, mental, economic, cultural or social identity".[6] According to this definition of the EU Data Protection Directive 95/46/EC, many of the data collected during the I-GEAR project meet this definition and are therefore personal data. They are protected by the above mentioned Directive 95/46/EC and by various legal acts transposing this directive into the national law of the EU Member States.

With regard to the standards of processing, it is central that the purpose of the processing must be clearly defined before the processing, which should exclude both the processing for unspecified and unknown purposes and the possibility to subsequently change the original purpose [7]. In more concrete terms this means that the data collected in the framework of the I-GEAR project can only be used for the (pre-defined) purposes of the project and are not allowed to be used for other, not pre-defined purposes afterwards. Further, personal data must be "processed fairly and lawfully, collected for specified, explicit and legitimate purposes and not further processed in a way incompatible with those purposes; adequate, relevant and not excessive in relation to the purposes for which they are collected and/or further processed; accurate and, where necessary, kept up to date" [7].

Some other criteria for making data processing legitimate are further detailed in Directive 95/46/EC. One very important condition in the framework of the I-GEAR project is that the processing is legitimate, if the data subject has unambiguously given his consent [8]. The data subject's consent means "any freely given specific and informed indication of his wishes by which the data subject signifies his agreement to personal data relating to him being processed" [9].

Another element of a fair processing of data is the information provided to the data subject. Knowing that one's personal data are processed guarantees transparency and enables the person concerned to assess its own position and to adapt their behaviour to a given situation. The information includes (a) the identity of the controller and of his representative, (b) the purposes of the processing for which the data are intended and (c) any further information in so far as such further information is necessary having regard to the specific circumstances in which the data are collected and to guarantee fair processing in respect of the data subject[10].

On condition that the data are collected with respect to the above principles, the question of further use of the data arises. It is planned to share the data originally only used for research purposes with local business partners to ensure that the incentives (e.g. coffee for ½ of the price) are used and that the rewards are distributed. While taking into account that this change in purpose has to be communicated to the users from the beginning on and that consent is needed for such use, certain other conditions apply as well. Transferring the data to companies within the EU, which are also subject to the rules of Directive 95/46/EC, has to comply with national law provisions transposing the directive. Transfer to companies outside of the EU is regulated more strictly and requires more protection 1 .

When however discussing the rules of Directive 95/46/EC, it should be taken into account that this instrument will be replaced by a new regulation in 2014 2 . This means that EU data protection law will be more effectively harmonised than before: if adopted, the regulation would apply directly in the EU Member States and its provisions would be directly binding. One important change would, for instance, relate to the fact that in case of data breaches, the responsible data protection authority as well as the individual would have to be notified [11].

3.3

Trust issues

Trust issues are the least concrete to deal with and there are multiple valid ways of looking at trust. In contrast to ethics and privacy, it is more of a perceived quality than a tangible and easily controllable quality. In the context of I-GEAR, we think trust is mainly relying on two requirements:

5. Strict compliance with and transparency of that compliance with both ethics and legal aspects; 6. Specific emphasis on those principles that reassure a user our pervasive game is developed in a way that it will not jeopardize driving safety under any condition, provided the user behaves as a responsible driver;

While the first requirement may easily be generalized over a broader context, the second one is much more specific to high-risk domains. In addition, there remain questions about the trustworthiness of a system that intents to alter your driving behaviour: some people may perceive this property of a system as not acceptable and as a result not be in favour of pervasive games. This of course assumes they understand the term as a precise definition, especially when used in the public context this may not be immediately obvious. Finally, on a less general level, there may also be doubts regarding the performance of a system or game that has an impact on your driving waypoints: does this system suggest the best route in regard of the user's preferences and the system's expected behaviour? This applies to many properties that define a navigation path: travel time, travel distance, fuel economy, etc. It appears, trust is a very specific property and, as a perceived property, may be extremely dependent not only on the system itself, but very much on the users' specific expectations. We think I-GEAR has to consider the two above-mentioned requirements and also look more specifically into the results on user acceptance testing in order to understand whether there may be issues of trustworthiness amongst those cases where user acceptance is not given.

Conclusion

While not all aspects of ethics, privacy and trust could be discussed in this paper, we believe that the brief discussion presented should inform people about these aspects with respect to serious gaming. At present it is our view that these issues are at times not sufficiently explored, and indeed this is due in part to the contradiction which arises between aspects such as informed consent and the basics of playing serious games. In other respects it is simply that such issues have not been considered in the context of serious gaming merely due to its often perceived fringe appeal or level of interest. Furthermore, another explanation could be that gathering the interdisciplinary expertise is a difficult challenge in a domain that often concentrates on technical aspects. Our work within this area is still evolving and we expect to publish more about our experiences within I-GEAR at a later date.

Regarding trust issues, we highlighted how we think trust is linked to both ethics and privacy within a conditional relationship while an important part of trust also falls into the domain of user acceptance. We explained why we think that ad-hoc approaches only work to some extent when it comes to guaranteeing trust; in contrast to ethics and privacy, there is no guarantee that respecting predefined requirements is sufficient for establishing trustworthiness.

Finally, it is interesting to note that most of the requirements regarding ethics and privacy call for the same underlying principles that are expressed in different terms while converging towards the same objective of protecting a person's rights.

For details, compare Article

and

of the Directive 95/46/EC, OJ 1995, L-281/31. 2 Proposal for a Regulation of the European Parliament and of the Council on the protection of individuals with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation), 25 January 2012, COM (2012) 11 final.

Acknowledgements

The I-GEAR project is partially funded by Fonds National de la Recerche, Luxembourg under grant number 11/IS/1204159. The authors would also like to acknowledge other members of the I-GEAR project including: Thomas Engel, Nicolas Louveton, Tigran Avanesov and Martin Kracheel.


Markus Krause

Roberta Cuel
email: roberta.cuel@unitn.it

Maja Vukovic

Yaniv Corem

Robert Kern

Alexander Sorokin

Crowdflower Com

Massimo Poesio

Otto Chrons

Microtask Com

Cto

Simon Caton

Francesco Schiavone

Paolo Massa

Tindara Abbate

Carl Goodman

Claudio Bartolini

Harnessing Collective Intelligence with Games

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

Introduction

The idea of designing systems that solve tasks and incorporate the playfulness of games has recently become an extensively discussed topic. Many Web 2.0 and crowdsourcing initiatives take advantage of the vast amount of hours spent on online playing, by extracting meaningful information, acquiring knowledge, or outsourcing production tasks. The workshop on "System with Homo Ludens in the loop" aims at providing a discussion forum for both researchers and practitioners in the fields of computer science, web technologies, and sociology, as well as psychology and business studies. Many areas of knowledge acquisition inherently rely on the availability on large quantities of human input. The problem is that in many of these domains, users lack the motivation to contribute the required metadata. At the same time, there is a steady trend of people spending a substantial amount of time in playing games. As initially proposed by Luis von Ahn's "Games with a Purpose", one can benefit from the vast amount of hours spent on online playing, by applying mechanisms to extract meaningful information from game inputs. Since then, there have been many proposals to use casual games which capitalize on fun and competition as two key motivators for people, to willingly invest time and effort in knowledge acquisition related tasks hiding behind an entertaining collaborative game experience. The workshop will provide a forum for researchers and practitioners in Web technologies to discuss and exchange positions on the topic of using games for acquiring knowledge following the paradigm of human computation.

Areas of Interest

The objective of the workshop is to foster the thinking process about how to effectively involve the users in the loop of a production system or crowdsoucing initiative.



Accepted Submission Abstracts

Full submissions can be found in the proceedings of the 3'rd International Conference on Serious Games Development and Applications SGDA'12.

Value-based Design for Gamifying Daily Activities

Computing technologies allow us to gamify our daily activity by embedding computers in our daily environments. In this paper, we propose a value-based gamification framework to increase pleasure in our daily life. Traditional gamification frameworks add game mechanics in our daily activities to motivate people. However, it is hard to maintain the motivation for a long time. Our approach is based on values to increase intrinsic human motivation. In this paper, we introduce five values, and how the values are used in Augmented Trading Card Game as a case study. Then, we show a framework to design gamification of daily activities to increase intrinsic human motivation.

3.2

Squaring and Scripting the ESP Game: Trimming a GWAP to Deep Semantics

The ESP Game, like other Games With A Purpose (GWAP), tends to generate "surface semantics" tags. This article first discusses why this is the case, then proposes two approaches called "squaring" and "scripting" to collecting "deep semantics" tags that both consist in deploying the ESP Game in unconventional manners. It also reports on a promising first experimental evaluation of the two approaches. It finally briefly discusses the relevance of squaring and scripting for other GWAPs than the ESP Game.

Logical Thinking by Play Using the Example of the Game Space Goats

The idea of "Serious Games" mainly describes games that generate overvalue. According to James P. Gee's learning theories, game worlds are some of the best learning environments imaginable as they encourage the utilization of the actively learned skills in other domains. The game "Space Goats" is designed according to these principles. It uses a graphical scripting interface to encourage the player to reason logically, while it stays a game all the time, and the player does not realize, that he has been taught.

Betaville -a massively participatory mirror world game

Betaville is an editable online mirror world designed to develop broader positive participation in development of new ideas for urban environments, particularly for new initiatives in public art, urban design, and development/redevelopment. A "mirror world" of any city, based on public terrain and GIS data supplemented by embedded links to background information, can be further developed by user-created proposals for additions and modifications to the environment. Multiple proposals can be offered, debated, and iterated for the same location. Betaville is a research and development collaboration between the Brooklyn Experimental Media Center of the Polytechnic Institute of New York University and the M2C Institute for Applied Media Technology and Culture at the University of Applied Sciences of Bremen

  Games for Collective Intelligence  Human Computation Games (Games with a Purpose)

 Applications of games in science, industry and public sector  Games for data collection, verification and classification  Game-based surveys  Task decomposition and gamification  Quality management in collective play  Cost-benefit analysis for collective play  Games and new business models  Commodification of play (uses and abuses of free time)  Collective play as socialization (using social networks platforms)  Game propagation in the social networks  Game architectures and technology  Incentives and adoption of games in enterprise environments