Rating vs. preference : a comparative study of self-reporting

This paper introduces a comparative analysis between rating and pairwise self-reporting via questionnaires in user survey experiments. Two dissimilar game user survey experiments are employed in which the two questionnaire schemes are tested and compared for reliable affect annotation. The statistical analysis followed to test our hypotheses shows that even though the two self-reporting schemes are consistent there are significant order of reporting effects when subjects report via a rating questionnaire. The paper concludes with a discussion of the appropriateness of each self-reporting scheme under conditions drawn from the experimental results obtained.peer-reviewe

Affective Medicine: a review of Affective Computing efforts in Medical Informatics

Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions”. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field

Multi-modal Approach for Affective Computing

Throughout the past decade, many studies have classified human emotions using only a single sensing modality such as face video, electroencephalogram (EEG), electrocardiogram (ECG), galvanic skin response (GSR), etc. The results of these studies are constrained by the limitations of these modalities such as the absence of physiological biomarkers in the face-video analysis, poor spatial resolution in EEG, poor temporal resolution of the GSR etc. Scant research has been conducted to compare the merits of these modalities and understand how to best use them individually and jointly. Using multi-modal AMIGOS dataset, this study compares the performance of human emotion classification using multiple computational approaches applied to face videos and various bio-sensing modalities. Using a novel method for compensating physiological baseline we show an increase in the classification accuracy of various approaches that we use. Finally, we present a multi-modal emotion-classification approach in the domain of affective computing research.Comment: Published in IEEE 40th International Engineering in Medicine and Biology Conference (EMBC) 201

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

Game adaptivity impact on affective physical interaction

Adaptive human computer interaction is necessary for successfully closing the affective loop within intelligent interactive systems. This paper investigates the impact of adaptivity on the physiological state and the expressed emotional preferences of users. A physical interactive game is used as a test-bed system and its real-time adaptation mechanism is evaluated using a survey experiment. Results reveal that entertainment preferences expressed are consistent with the affective model constructed and that adaptation generates dissimilar physiological responses with respect to preferences.peer-reviewe

Computing the Affective-Aesthetic Potential of Literary Texts

In this paper, we compute the affective-aesthetic potential (AAP) of literary texts by using a simple sentiment analysis tool called SentiArt. In contrast to other established tools, SentiArt is based on publicly available vector space models (VSMs) and requires no emotional dictionary, thus making it applicable in any language for which VSMs have been made available (>150 so far) and avoiding issues of low coverage. In a first study, the AAP values of all words of a widely used lexical databank for German were computed and the VSM’s ability in representing concrete and more abstract semantic concepts was demonstrated. In a second study, SentiArt was used to predict ~2800 human word valence ratings and shown to have a high predictive accuracy (R2 > 0.5, p < 0.0001). A third study tested the validity of SentiArt in predicting emotional states over (narrative) time using human liking ratings from reading a story. Again, the predictive accuracy was highly significant: R2adj = 0.46, p < 0.0001, establishing the SentiArt tool as a promising candidate for lexical sentiment analyses at both the micro- and macrolevels, i.e., short and long literary materials. Possibilities and limitations of lexical VSM-based sentiment analyses of diverse complex literary texts are discussed in the light of these results

Rating vs. preference : a comparative study of self-reporting

This paper introduces a comparative analysis between rating and pairwise self-reporting via questionnaires in user survey experiments. Two dissimilar game user survey experiments are employed in which the two questionnaire schemes are tested and compared for reliable affect annotation. The statistical analysis followed to test our hypotheses shows that even though the two self-reporting schemes are consistent there are significant order of reporting effects when subjects report via a rating questionnaire. The paper concludes with a discussion of the appropriateness of each self-reporting scheme under conditions drawn from the experimental results obtained.peer-reviewe

Preference learning for affective modeling

There is an increasing trend towards personalization of services and interaction. The use of computational models for learning to predict user emotional preferences is of significant importance towards system personalization. Preference learning is a machine learning research area that aids in the process of exploiting a set of specific features of an individual in an attempt to predict her preferences. This paper outlines the use of preference learning for modeling emotional preferences and shows the methodology's promise for constructing accurate computational models of affect.peer-reviewe

Neurophysiological Assessment of Affective Experience

In the field of Affective Computing the affective experience (AX) of the user during the interaction with computers is of great interest. The automatic recognition of the affective state, or emotion, of the user is one of the big challenges. In this proposal I focus on the affect recognition via physiological and neurophysiological signals. Long‐standing evidence from psychophysiological research and more recently from research in affective neuroscience suggests that both, body and brain physiology, are able to indicate the current affective state of a subject. However, regarding the classification of AX several questions are still unanswered. The principal possibility of AX classification was repeatedly shown, but its generalisation over different task contexts, elicitating stimuli modalities, subjects or time is seldom addressed. In this proposal I will discuss a possible agenda for the further exploration of physiological and neurophysiological correlates of AX over different elicitation modalities and task contexts