User modeling via gesture and head pose expressivity features

Asteriadis, Stylianos; Karpouzis, Kostas

doi:10.1109/smap.2010.5706868

Cited by 1 publication

(4 citation statements)

References 8 publications

(8 reference statements)

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“…For example, frustration was observed to be linked to sudden and very quick head movements while low levels of challenge would normally be associated with smoother movements, probably due to lack of high interest [36]. For the above reasons, in this paper we examine the relation of a series of head movement features [45], [4] with experience models, along with gameplay and content features. In particular, we track player's head motion through head horizontal and vertical (yaw and pitch) rotational movements.…”

Section: Head Movement Featuresmentioning

confidence: 99%

“…A series of further head movement features [45] have also been considered in order to elicit emotional information of the player during each game session (Mean Head Movement Features). More specifically, we considered:…”

Section: Head Movement Featuresmentioning

confidence: 99%

“…• Overall Activation (OA), which comes as the sum of quantities of motion [45] for each rotational movement, separately. In other words, OA stands for the quantity of movement during certain periods of time.…”

Section: Head Movement Featuresmentioning

confidence: 99%

“…The detailed list of extracted head movement features is presented in Table II. For more details regarding the extraction of the above criteria, please refer to [45]. In this paper, in addition to the above features, the median values of horizontal, M horizontal , and vertical, M vertical rotations, as well as medians of head rotation norms M are also considered.…”

Section: Head Movement Featuresmentioning

confidence: 99%

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Fusing Visual and Behavioral Cues for Modeling User Experience in Games

Shaker

Asteriadis

Yannakakis

et al. 2013

IEEE Trans. Cybern.

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Abstract-Estimating affective and cognitive states in conditions of rich human-computer interaction, such as in games, is a field of growing academic and commercial interest. Entertainment and serious games can benefit from recent advances in the field as, having access to predictors of the current state of the player (or learner) can provide useful information for feeding adaptation mechanisms that aim to maximize engagement or learning effects. In this paper, we introduce a large data corpus derived from 58 participants that play the popular Super Mario Bros platform game and attempt to create accurate models of player experience for this game genre. Within the view of the current research, features extracted both from player gameplay behavior and game levels, and player visual characteristics have been used as potential indicators of reported affect expressed as pairwise preferences between different game sessions. Using neuroevolutionary preference learning and automatic feature selection, highly accurate models of reported engagement, frustration, and challenge are constructed (model accuracies reach 91%, 92% and 88% for engagement, frustration and challenge, respectively). As a step further, the derived player experience models can be used to personalize the game level to desired levels of engagement, frustration and challenge, as game content is mapped to player experience through the behavioral and expressivity patterns of each player.

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Section: Head Movement Featuresmentioning

confidence: 99%

Section: Head Movement Featuresmentioning

confidence: 99%

Section: Head Movement Featuresmentioning

confidence: 99%

Section: Head Movement Featuresmentioning

confidence: 99%

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