Wearable and automotive systems for affect recognition from physiology

By Jennifer A. Healey. 2000

312 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/76c5bea02f6d6d6a7acd8bbb09425606edd213f1

How researchers see this paper

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

Citations in the corpus

Using Noninvasive Wearable Computers to Recognize Human Emotions from Physiological Signals (2004, background): “Another study, which is very much related to one of the applications we will discuss in Section 5 (and which therefore we describe at length here), was conducted by Jennifer Healey from Massachusetts Institute of Technology (MIT) Media Lab [39].”
Objective measures, sensors and computational techniques for stress recognition and classification: A survey (2012, background): “[56] J.”
Multimodal Emotion Recognition in Response to Videos (2012, background): “[3] M. Soleymani, G. Chanel, J. J. M. Kierkels, and T. Pun,“ Affective Characterization of Movie Scenes Based on Content Analysis and Physiological Changes,”International Journal of Semantic Computing, vol. 3, no. 2, pp. 235-254, June 2009.”
Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach (2008, background): “Similar efforts aimed at finding physiological correlates, focusing on t-tests or analysis of variance comparisons [19], [20].”
Detecting stress during real-world driving tasks using physiological sensors (2005, background): “The algorithm detected the onsets and peaks of the orienting responses by first detecting slopes exceeding a critical threshold and then finding the local minimum preceding that point (onset) and the local maximum following that point (peak) [31].”
A driver fatigue recognition model based on information fusion and dynamic Bayesian network (2010, methodology): “In this study, we acquired information from several published papers [11,12,15,16,21,28,30,33,44].”
From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification (2005, methodology): “Although many efforts have been taken recently to recognize emotions using facial expressions, speech and physiological signals [1, 2, 3, 4, 5], current recognition systems are not yet advanced enough to be used in realistic applications.”
Short-term emotion assessment in a recall paradigm (2009, background): “Identification of critical states, such as stress, panic, and boredom, can be very useful in situations such as driving or when performing dangerous operations (Benoit et al., 2006; Healey, 2000).”
Emotion Assessment: Arousal Evaluation Using EEG's and Peripheral Physiological Signals (2006, unknown): “ElectroEncephalograms-EEG). In recent years interesting results have been obtained with the first category of signals ([7], [8], [ 9 ]).”
DEAP: A Database for Emotion Analysis ;Using Physiological Signals (2012, background): “Healey [16], [17] recorded one of the first affective physiological data sets.”
Affective e-Learning: Using "Emotional" Data to Improve Learning in Pervasive Learning Environment (2009, background): “Many efforts have been taken to recognize emotions using facial expressions, speech and physiological signals (Cowie et al., 2001; Healey, 2000; Picard et al., 2001).”
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State (2001, methodology): “, collecting a ective data from users driving automobiles in city and highway conditions [38] and from users placed in frustrating computer situations [51]; both of these areas aim at data generation in an event-elicited, close to real-world, feeling, open-recording, other-purpose experiment, with e”
The Role of Immersion and Narrative in Mediated Presence: The Virtual Hospital Experience (2011, unknown): “These results are consistent with the Healey’s theory on emotions, 31 afﬁrming that an increase in the heart-rate response is an important physiological indicator of arousal and valence activation.”
Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty (2011, background): “Blood pressure and HR variability are variables that correlate with defensive reactions [31], pleasantness of a stimuli [30], and basic emotions [32].”