Affective games:a multimodal classification system

Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation

A review on data fusion in multimodal learning analytics and educational data mining

The new educational models such as smart learning environments use of digital and context-aware devices to facilitate the learning process. In this new educational scenario, a huge quantity of multimodal students' data from a variety of different sources can be captured, fused, and analyze. It offers to researchers and educators a unique opportunity of being able to discover new knowledge to better understand the learning process and to intervene if necessary. However, it is necessary to apply correctly data fusion approaches and techniques in order to combine various sources of multimodal learning analytics (MLA). These sources or modalities in MLA include audio, video, electrodermal activity data, eye-tracking, user logs, and click-stream data, but also learning artifacts and more natural human signals such as gestures, gaze, speech, or writing. This survey introduces data fusion in learning analytics (LA) and educational data mining (EDM) and how these data fusion techniques have been applied in smart learning. It shows the current state of the art by reviewing the main publications, the main type of fused educational data, and the data fusion approaches and techniques used in EDM/LA, as well as the main open problems, trends, and challenges in this specific research area

Multimodal recognition of frustration during game-play with deep neural networks

Frustration, which is one aspect of the field of emotional recognition, is of particular interest to the video game industry as it provides information concerning each individual player’s level of engagement. The use of non-invasive strategies to estimate this emotion is, therefore, a relevant line of research with a direct application to real-world scenarios. While several proposals regarding the performance of non-invasive frustration recognition can be found in literature, they usually rely on hand-crafted features and rarely exploit the potential inherent to the combination of different sources of information. This work, therefore, presents a new approach that automatically extracts meaningful descriptors from individual audio and video sources of information using Deep Neural Networks (DNN) in order to then combine them, with the objective of detecting frustration in Game-Play scenarios. More precisely, two fusion modalities, namely decision-level and feature-level, are presented and compared with state-of-the-art methods, along with different DNN architectures optimized for each type of data. Experiments performed with a real-world audiovisual benchmarking corpus revealed that the multimodal proposals introduced herein are more suitable than those of a unimodal nature, and that their performance also surpasses that of other state-of-the–art approaches, with error rate improvements of between 40% and 90%.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The first author acknowledges the support from the Spanish “Ministerio de Educación y Formación Profesional” through grant 20CO1/000966. The second and third authors acknowledge support from the “Programa I+D+i de la Generalitat Valenciana” through grants ACIF/2019/042 and APOSTD/2020/256, respectively

Multimodal Affective State Recognition in Serious Games Applications

A challenging research issue, which has recently attracted a lot of attention, is the incorporation of emotion recognition technology in serious games applications, in order to improve the quality of interaction and enhance the gaming experience. To this end, in this paper, we present an emotion recognition methodology that utilizes information extracted from multimodal fusion analysis to identify the affective state of players during gameplay scenarios. More specifically, two monomodal classifiers have been designed for extracting affective state information based on facial expression and body motion analysis. For the combination of different modalities a deep model is proposed that is able to make a decision about player’s affective state, while also being robust in the absence of one information cue. In order to evaluate the performance of our methodology, a bimodal database was created using Microsoft’s Kinect sensor, containing feature vectors extracted from users' facial expressions and body gestures. The proposed method achieved higher recognition rate in comparison with mono-modal, as well as early-fusion algorithms. Our methodology outperforms all other classifiers, achieving an overall recognition rate of 98.3%