Capturing entertainment through heart rate dynamics in the playware playground

This paper introduces a statistical approach for capturing entertainment in real-time through physiological signals within interactive playgrounds inspired by computer games. For this purpose children’s heart rate (HR) signals and judgement on entertainment are obtained from experiments on the innovative Playware playground. A comprehensive statistical analysis shows that children’s notion of entertainment correlates highly with their average HR during the game.peer-reviewe

Promoting children’s physical activity using adaptive playgrounds

This abstract introduces the innovative Playware playground and how it can be utilized for promoting children’s physical activity and thus partly addressing issues related to increasing obesity problems in the western society. Playware allows for the use of intelligent technology to create the kind of leisure activity normally labeled play, i.e. intelligent hardware and software which aims at producing play and playful experiences amongst users. Playware with ambient intelligence characteristics can be personalized, adaptive and anticipatory: it can be integrated into real physical environments (i.e. playgrounds) so that users can freely and interactively utilize it allowing emergence of creative and active plays. Experiments within the Playware playground have demonstrated a significant correlation between the level of children’s perceived entertainment (fun) and the average response time that children interact with the playground. The obtained effect appears to be consistent with theoretical approaches on the interplay between response time and the engagement level within human computer interactive systems. Moreover, preliminary studies on physiological signals of children playing with Playware games have already shown the significant effect of average heart rate (HR) to children’s entertainment. Thus the hypothesis drawn here is that the higher the average response time of children during a game the higher the entertainment value of the game and furthermore the higher their physical activity through their average HR. The Playware playground has been augmented with an intelligent adaptation mechanism, which efficiently recognizes an individual child’s playing behavior and adapts the playground game according to the child’s individual desires. Several experiments have been conducted using adaptation mechanisms designed in order to increase children’s physical activity. It has been shown, that individual play characteristics, such as the total number of interactions with the playground and the average response time of the interactions increase significantly with the use of the adaptation mechanism, providing evidence for the mechanism’s appropriateness to effectively augment the game’s entertainment value and promote children’s physical activity.peer-reviewe

Preliminary studies for capturing entertainment through physiology in physical play

This report presents preliminary physical control experiments for capturing and modeling the affective state of entertainment — that is, whether people are having "fun" — of users of the innovative Play-ware playground, an interactive physical playground. The goal is to con-struct, using representative statistics computed from children's physio-logical hear rate (HR) signals, an estimator of the degree to which games provided by the playground engage the players. For this purpose chil-dren's HR signals, and their expressed preferences of how much "fun" particular game variants are, are obtained from experiments using games implemented on the Playware playground. Neuro-evolution techniques combined with feature set selection methods permit the construction of user models that predict reported entertainment preferences given HR features. These models are expressed as artificial neural networks and are demonstrated and evaluated on two Playware games and the pre-liminary control task requiring physical activity. Results demonstrate that the proposed preliminary control experiment is not an appropriate control for physical activity effects since it may generate HR dynamics rather easy to separate from game-play HR dynamics, and allows one to distinguish entertaining game-play from exercise purely on the artificial basis of the kind of physical activity taking place. Conclusions derived from this study constitute the basis for the design of more appropriate control experiments and user models in future studies.peer-reviewe

Entertainment modeling in physical play through physiology beyond heart-rate

An investigation into capturing the relation of physiology, beyond heart rate recording, to expressed preferences of entertainment in children’s physical gameplay is presented in this paper. An exploratory survey experiment raises the difficulties of isolating elements derived (solely) from heart rate recordings attributed to reported entertainment and a control experiment for surmounting those difficulties is proposed. Then a survey experiment on a larger scale is devised where more physiological signals (Blood Volume Pulse and Skin Conductance) are collected and analyzed. Given effective data collection a set of numerical features is extracted from the child’s physiological state. A preference learning mechanism based on neuro-evolution is used to construct a function of single physiological features that models the players’ notion of ‘fun’ for the games under investigation. Performance of the model is evaluated by the degree to which the preferences predicted by the model match those expressed by the children. Results indicate that there appears to be increased mental/emotional effort in preferred games of children.peer-reviewe

Feature selection for capturing the experience of fun

Several approaches for constructing metrics to capture player experience have been presented previously. In this paper, we propose a generic methodology based on feature selection and preference machine learning for constructing such metric models of the degree to which a player enjoys a given game. For that purpose, previous and new survey experiments on computer and physical interactive games are presented. Given effective data collection a set of numerical features is extracted from a player’s interaction with the game and its physiological state. Then feature selection algorithms are employed together with a function approximator based on artificial neural networks to construct feature sets and function that model the players’ notion of ‘fun’ for the game under investigation. Performance of the model is evaluated by the degree to which the preferences predicted by the model match those ‘fun’ (entertainment) preferences expressed by the subjects. The results show that effective models can be constructed using the proposed approach. The limitations and the use of the methodology as an effective adaptive mechanism to entertainment augmentation are discussed.This work was supported in part by the Danish Research Agency, Ministry of Science, Technology and Innovation (project no: 274-05-0511).peer-reviewe

Capturing player enjoyment in computer games

The current state-of-the-art in intelligent game design using Artificial Intelligence (AI) techniques is mainly focused on generating human-like and intelligent characters. Even though complex opponent behaviors emerge through various machine learning techniques, there is generally no further analysis of whether these behaviors contribute to the satisfaction of the player. The implicit hypothesis motivating this research is that intelligent opponent behaviors enable the player to gain more satisfaction from the game. This hypothesis may well be true; however, since no notion of entertainment or enjoyment is explicitly defined, there is therefore no evidence that a specific opponent behavior generates enjoyable games.peer-reviewe

Towards optimizing entertainment in computer games

Mainly motivated by the current lack of a qualitative and quantitative entertainment formulation of computer games and the procedures to generate it, this article covers the following issues: It presents the features—extracted primarily from the opponent behavior—that make a predator/prey game appealing; provides the qualitative and quantitative means for measuring player entertainment in real time, and introduces a successful methodology for obtaining games of high satisfaction. This methodology is based on online (during play) learning opponents who demonstrate cooperative action. By testing the game against humans, we confirm our hypothesis that the proposed entertainment measure is consistent with the judgment of human players. As far as learning in real time against human players is concerned, results suggest that longer games are required for humans to notice some sort of change in their entertainment.peer-reviewe