Affect expression in ECAs: Application to politeness displays

Niewiadomski, Radosław; Pélachaud, Catherine

doi:10.1016/j.ijhcs.2010.07.004

Cited by 21 publications

(9 citation statements)

References 31 publications

(53 reference statements)

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“…It may be noted that the model was developed from empirical data. The data were collected from participants who represent homogeneous group (male, post graduate students with the age group of [22][23][24][25][26]. Moreover, the number of participants was also modest (57).…”

Section: Discussionmentioning

confidence: 99%

“…Isbister et al [16] used the continuous model to evaluate emotion from facial expressions. Niewiadomsk and Pelachaud [25] proposed embodied conversational agents (ECAs), which could synthesize and display a large number of complex facial expressions. The synthesis of facial expressions was based on a fuzzy method.…”

Section: Emotion Detection From Facial Expressionmentioning

confidence: 99%

“…[1]Positive emotion range: [50,105] [2]Negative emotion range: [1,12] [3]Neutral emotion range: [25,36] In order to obtain the other parameters (A's, B's and C's of Eqs. 12, 13 and 14), we used an elaborate assignment based approach.…”

Section: The Linear Regression Approachmentioning

confidence: 99%

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Towards affective touch interaction: predicting mobile user emotion from finger strokes

2015

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The role of affect and emotion in interactive system design is an active and recent research area. The aim is to make systems more responsive to user's needs and expectations. The first step towards affective interaction is to recognize user's emotional state. Literature contains many works on emotion recognition. In those works, facial muscle movement, gestures, postures and physiological signals were used for recognition. The methods are computation intensive and require extra hardware (e.g., sensors and wires). In this work, we propose a simpler model to predict the affective state of a touch screen user. The prediction is done based on the user's touch input, namely the finger strokes. We defined seven features based on the strokes. A linear combination of these features is proposed as the predictor, which can predict a user's affective state into one of the three states: positive (happy, excited and elated), negative (sad, anger, fear, disgust) and neutral (calm, relaxed and contented). The model alleviates the need for extra setup as well as extensive computation, making it suitable for implementation on mobile devices with limited resources. The model is developed and validated with empirical data involving 57 participants performing 7 touch input tasks. The validation study demonstrates a high prediction accuracy of 90.47 %. The proposed model and its empirical development and validation are described in this paper.

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Section: Discussionmentioning

confidence: 99%

Section: Emotion Detection From Facial Expressionmentioning

confidence: 99%

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Towards affective touch interaction: predicting mobile user emotion from finger strokes

2015

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“…Since affective processes rely on emotional contagion and mimicry (Hatfield et al, 1994;Niedenthal et al, 2010), the implication is that designers need to be able to express emotions as they are seen in nature. This speaks to the importance of having embodied computer interfaces, with virtual faces (Bartneck and Reichenbach, 2005;Beale and Creed, 2009;Cassell et al, 1994;Gratch et al, 2002;Niewiadomski and Pelachaud, 2010). On the other hand, if it is not desired or possible to have an embodied interface, then designers can at least explore inferential processes.…”

Section: Implications and Contributionsmentioning

confidence: 99%

“…There has been growing interest in the development of embodied social agents that show emotion facial expressions (Bartneck and Reichenbach, 2005;Beale and Creed, 2009;Cassell et al, 1994;Gratch et al, 2002;Niewiadomski and Pelachaud, 2010). Part of this interest stems from findings that emotional facial expressions affect people's decisions in human-agent interactions (de Melo et al, 2014;Gong, 2007;Kiesler et al, 1996;Yuasa and Mukawa, 2007).…”

Section: Introductionmentioning

confidence: 99%