Toward Machine Emotional Intelligence: Analysis of Affective Physiological State

By Rosalind W. Picard, E. Vyzas, Jennifer A. Healey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001

2,453 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/538b21803b23b3d870beab87440e79c4fed84e67

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Cognitive Neuroscience

In Experimental and Cognitive Psychology

Citations in the corpus

Emotion recognition from physiological signals using wireless sensors for presence technologies (2004, methodology): “conducted another study [29] by showing emotion specific pictures to elicit happiness, sadness, anger, fear, disgust, surprise, neutrality, platonic love, and romantic love.”
To feel or not to feel: The role of affect in human-computer interaction (2003, background): “These types of interactions are already being explored in a number of research laboratories ( see Paiva , 2001 ; Picard et al .”
Multimodal affect recognition in learning environments (2005, positive): “Exceptions include the work of Picard et al.[ 15 ], which achieved 81% classication accuracy of eight emotional states of an individual over many days of data, based on four physiological signals, and several eorts that have combined audio of the voice with video of the face, e.g.”
Measures of emotion: A review (2009, positive): “Third, there are likely to be moderator variables that affect convergence across measures of emotion (Fridlund, Schwartz, & Fowler, 1984 ; Lacey, Bateman, & Vanlehn, 1953 ; Picard, Vyzas, & Healey, 2001 ).”
An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals (2020, background): “HUMAN emotions are complex psychological and physiological expressions, which are often related to subjective feelings, temperament, personality, motivational tendencies, behavioral reactions and physiological arousal [1], [2].”
EEG-Based Emotion Recognition in Music Listening (2010, background): “More recently, accessing physiological responses has gained increasing attention in characterizing the emotional states [2]–[5].”
A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies (2007, methodology): “For example, Picard et al. (2001) performed a feature-based recognition of eight emotional states from GSR, EMG of the jaw, BVP, and respiration over multiple days.”
Survey on Emotional Body Gesture Recognition (2018, background): “tual man-machine interfaces, which are used in intelligent affective machines and computers that understand and react to human emotions [142].”
Experience-Driven Procedural Content Generation (2011, background): “guided imaginary [9]) or specific stimuli are provided via the interaction.”
Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty (2011, background): “One of the pioneering work on emotion assessment from peripheral signals is [5] where the authors detected eight selfinduced emotional states with an accuracy of 81%.”
Does Body Movement Engage You More in Digital Game Play? and Why? (2007, background): “And accordingly, recent studies (see [20-25] for some examples) have set to establish a framework for grounding the recognition of affective states into body postures.”
Using Noninvasive Wearable Computers to Recognize Human Emotions from Physiological Signals (2004, result): “We have combined the results of our survey of other relevant literature [18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38] into an extensive survey-table.”
Affective e-Learning: Using "Emotional" Data to Improve Learning in Pervasive Learning Environment (2009, methodology): “The Russell's model is widely used in recent researches (Craig et al., 2004; Fagerberg et al., 2004; Kort et al., 2001; Leon et al., 2007; Picard et al., 2001).”
Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey (2010, background): “Correlation Between Movement and Emotions Picard et al.[17] propose a framework for emotion recognition in order to understand the correlation between intelligence and emotion in people and to build machines appearing more intelligent.”
Fundamentals of physiological computing (2009, background): “Interested readers are referred to Picard (1997) and Picard et al. (2001), Allanson and Fairclough (2004), or Mandryk et al. (2006) for discussion of these issues.”