This paper deals with steering player behavior in the Interactive Tag Playground (ITP). The ITP, an ambient environment instrumented with contact-free sensor technology and ambient display capabilities, enhances the traditional game of tag by determining when a valid tag has been made and visualising the current tagger. We present three modifications of the ITP that aim to steer the gameplay actions of the players. The modifications are intended to influence who will be chased next by the tagger; to make good players easier to tag and less skilled players harder to tag; and to influence the locations visited by the players. We report on a user study showing that two of the three modifications have a significant effect on the behavior of players in the ITP and discuss opportunities for future research that follow from this study.

Abstract. In this paper we investigate whether remote touch in the form of force feedback from another player's actions can enhance feelings of social presence and enjoyment of a collaborative, spatially distributed rope pulling game. Dyads of players situated in different rooms were either given an 'elastic band' type force feedback, or were given force feedback of the other player's actions (i.e. remote touch). Results showed that feedback from another player's actions enhanced feelings of social presence but not enjoyment of the game.

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We have built and implemented a set of metaphors for breathing games by involving children and experts. These games are made to facilitate prevention of asthma exacerbation via regular monitoring of children with asthma through spirometry at home. To instruct and trigger children to execute the (unsupervised) spirometry correctly, we have created interactive metaphors that respond in real-time to the child's inhalation and exhalation. Eleven metaphors have been developed in detail. Three metaphors have been fully implemented based on current guidelines for spirometry and were tested with 30 asthmatic children. Each includes multi-target incentives, responding to three different target values (inhalation, peak expiration, and complete exhalation). We postulate that the metaphors should use separate goals for these targets, have independent responses, and allow to also go beyond expected values for each of these targets. From the selected metaphors, most children preferred a dragon breathing fire and a soccer player kicking a ball into a goal as a metaphor; least liked were blowing seeds of a dandelion and applying lotion to a dog to grow its hair. Based on this project we discuss the potential and benefits of a suite-of-games approach: multiple games that each can be selected and adapted depending on personal capabilities and interests. CCS CONCEPTS• Human-centered computing → Empirical studies in ubiquitous and mobile computing; User studies.

Access to performance data during matches and training sessions is important for coaches and players. Although there are many video tagging systems available which can provide such access, these systems require manual effort. Data from Inertial Measurement Units (IMU) could be used for automatically tagging video recordings in terms of players’ actions. However, the data gathered during volleyball sessions are generally very imbalanced, since for an individual player most time intervals can be classified as “non-actions” rather than “actions”. This makes automatic annotation of video recordings of volleyball matches a challenging machine-learning problem. To address this problem, we evaluated balanced and imbalanced learning methods with our newly proposed ‘super-bagging’ method for volleyball action modelling. All methods are evaluated using six classifiers and four sensors (i.e., accelerometer, magnetometer, gyroscope and barometer). We demonstrate that imbalanced learning provides better unweighted average recall, (UAR = 83.99%) for the non-dominant hand using a naive Bayes classifier than balanced learning, while balanced learning provides better performance (UAR = 84.18%) for the dominant hand using a tree bagger classifier than imbalanced learning. Our super-bagging method provides the best UAR (84.19%). It is also noted that the super-bagging method provides better averaged UAR than balanced and imbalanced methods in 8 out of 10 cases, hence demonstrating the potential of the super-bagging method for IMU’s sensor data. One of the potential applications of these novel models is fatigue and stamina estimation e.g., by keeping track of how many actions a player is performing and when these are being performed.