Jun-Wen Tan

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Affective computing aims at the detection of users' mental states, in particular, emotions and dispositions during human-computer interactions. Detection can be achieved by measuring multimodal signals, namely, speech, facial expressions and/or psychobiology. Over the past years, one major approach was to identify the best features for each signal using(More)
BACKGROUND Research suggests that interaction between humans and digital environments characterizes a form of companionship in addition to technical convenience. To this effect, humans have attempted to design computer systems able to demonstrably empathize with the human affective experience. Facial electromyography (EMG) is one such technique enabling(More)
—Recently, affective computing findings demonstrated that emotion processing and recognition is important in improving the quality of human computer interaction (HCI). In the present study, new data for a robust discrimination of three emotional states (negative, neutral and positive) employing two-channel facial electromyography (EMG) over zygomaticus(More)
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