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predicting mobile user emotion from finger
strokes
Abstract
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.
The validation study demonstrates a high prediction accuracy of
90.47 %.
This paper presents and discusses key issues in “affective computing,” computing that relates to, arises from, or influences emotions.
Fear, Emotion and Science
How advances in affective computing, especially combined with wearable computers, can help advance emotion and cognition theory
Once the emotion process is initiated, deliberate cognitive processing and physiological activity may influence the emotional experience, but the generation of emotion itself is hypothesized to be a perceptual process.
People are quick to polarize thoughts and feelings as if they were opposites. But, neurologically, the brain draws no hard line between thinking and feeling:
Years of studies on patients with frontal-lobe disorders indicate that impaired ability to feel yields impaired ability to make decisions; in other words, there is no “pure reason” . Emotions are vital for us to function as rational decision-making human beings.
A machine will not pass the Turing test unless it is also capabl