Toward computers that recognize and respond to user emotion

By Rosalind W. Picard. IBM Systems Journal, 2000

304 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Social Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/94d47826a77ff1e40bcb26f71163c353061d1a63

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In Experimental and Cognitive Psychology

In Social Psychology

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Citations in the corpus

Measuring Emotion: Development and Application of an Instrument to Measure Emotional Responses to Products (2005, unknown): “Researchers in the field of affective computing are most active in developing ANS instruments, such as IBM’s emotion mouse (Ark, Dryer, & Lu, 1999) and a variety of wearable sensors designed by the Affective Computing Group at MIT (e.g. Picard, 2000).”
Antecedents and effects of social network fatigue (2014, background): “Since fatigue has been defined as a subjective, individual feeling involving multiple dimensions (Piper, Lindsey, & Dodd, 1987), and given the fact that the experiential and emotional dimensions of user experiences are important considerations in design (Norman, 2002; Picard, 2000), a broader focus ”
Modeling naturalistic affective states via facial and vocal expressions recognition (2006, background): “A large part of this naturalistic interaction concept is expressivity [4], both in”
To feel or not to feel: The role of affect in human-computer interaction (2003, background): “…wearable computers such as earrings collecting physiological data (Picard and Healey, 1997), ‘expression glasses’ detecting interest or confusion (Picard, 2000), through integrated systems functioning as socially intelligent agents (e.g. robots and avatars interacting with autistic children…”
Educating Entrepreneurship Students About Emotion and Learning From Failure (2004, background): “Affective computing “relates to, arises from, or deliberately influences emotions” (Picard, 1997: 2). Picard (2000) argues that “wearable devices” that allow people to monitor their emotions will be able to help the wearer regulate stress and potentially improve health.”
Classification of human emotion from EEG using discrete wavelet transform (2010, background): “Some of the main physiological signals highly adopted for human emotion assessment are: Electromyogram (EMG), Electrocardiogram (ECG), Skin Conductive Resistance (SCR), and Blood Volume Pressure (BVP) [4,5].”
Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System (2004, background): “The computer could also learn to recognise the particular likes and dislikes of its user and anticipate appropriate actions in an unobtrusive manner [8].”
Describing the emotional states that are expressed in speech (2003, background): “The work of Picard and her colleagues (Picard, 2000) conveys the state of progress on a task with a particular appeal to the speech community, i.e. classifying emotional states on the basis of physiological measurements.”
Ambient intelligence: A survey (2011, background): “Recognizing human emotions and using this recognition to reduce user frustration, for example in games or tutoring systems, are also active areas of research within AmI (e.g. Picard 2000 and 2007, and Paiva et al. 2007).”