Autonomic specificity in emotion: The induction method matters

McGinley, Jared J.; Friedman, Bruce H.

doi:10.1016/j.ijpsycho.2017.06.002

Cited by 23 publications

(27 citation statements)

References 54 publications

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“…imagining the future, remembering the past or creating fictitious imaginings), physiological manipulations (e.g. through pharmacological means) or affective connotations in terms of evocative words [12,13,14,15]. Amongst all the emotion induction methods, visual induction stimuli can be easily categorized in terms of emotional content and are well controlled with regard to the size and duration of the material.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Various Emotion Classification Approaches from Physiological Signals

Hazer-Rau¹,

Zhang²,

Traue³

2018

IJAIA

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This paper aims at evaluating the performance of various emotion classification approaches from psychophysiological signals. The goal is to identify the combinations of approaches that are most relevant for assessing human affective states. A classification analysis of various combinations of feature selection techniques, classification algorithms and evaluation methods is presented. The emotion recognition is conducted based on four physiological signals: two electromyograms, skin conductivity and respiration sensors. Affective states are classified into three different emotion classes: 2-categoryclass (Arousal), 3-category-class (Valence) and 5-category-class (Valence/Arousal). The performance of the various combinations of approaches is evaluated by comparing the resulting recognition rates. For all the category-classes, the best results are obtained when considering skin conductivity combined with the respiration signals. Highest rates when fusing all physiological channels resulted when applying the SFS feature selection, the LDA classifier and the normal split evaluation approach, showing a robust combination of approaches leading to good performance.

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Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Various Emotion Classification Approaches from Physiological Signals

Hazer-Rau¹,

Zhang²,

Traue³

2018

IJAIA

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“…The study of positive emotions through mood induction procedures is scarce, but some conclusions can be derived. Previous research has shown that amusement is an emotion with a high rate of correct classification (e.g., McGinley & Friedman, ). However, tenderness has not been correctly classified.…”

Section: Resultsmentioning

confidence: 99%

“…Indeed, responses from the autonomic nervous system vary as a function of the valence and arousal level of affective stimuli. Even beyond the dimensions associated with the emotional response, there is a substantial support to the association between specific autonomic response patterns and discrete emotional states (McGinley & Friedman, 2017;Stephens, Christie, & Friedman, 2010).…”

Section: Multiparametric Analysis and Advanced Classificationmentioning

confidence: 99%

Film mood induction and emotion classification using physiological signals for health and wellness promotion in older adults living alone

et al. 2019

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This paper introduces a wearable hardware/software system specifically tailored to detect seven emotions (neutral, tenderness, amusement, anger, disgust, fear, and sadness) aimed at promoting health and wellness in older adults living alone at home. The complete software and hardware architectures acquiring and processing electrodermal activity and photoplethysmography signals are introduced. The wearable emotion detection system is trained by eliciting the desired emotions on 39 older adults through a film mood induction procedure. Seventeen features are calculated on skin conductance response and heart rate variability data, grouped into five statistical, four temporal, and eight morphological features. Then, these features are used to run emotion classification considering support vector machines, decision trees, and quadratic discriminant analysis. In line with psychological findings, the results offer a global accuracy of 82% in negative emotion (anger, disgust, fear, and sadness) classification. For positive emotions (tenderness and amusement), also in conformity with previous psychological outcomes, amusement shows the highest ratio of hits (92%) but tenderness the lowest one (66%). These results demonstrate that our wearable emotion detection system can be used by ageing adults, especially for detecting negative emotions that usually damage health and wellness and lead to social isolation.

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“…Back-and-forth translation and the exchange of emotional qualities made the tool more suitable for traffic settings [ 38 , 39 ]. According to [ 40 ], pleasure was also a high-frequency driving emotion. We summarized the 18 emotions involved in [ 37 , 38 , 39 , 40 ] into a scale.…”

Section: Study 2-driving Intention Prediction Models Adapting To Mmentioning

confidence: 99%

“…According to [ 40 ], pleasure was also a high-frequency driving emotion. We summarized the 18 emotions involved in [ 37 , 38 , 39 , 40 ] into a scale. The participants were asked to give a score from 0 to 5 for each emotion according to their experience during driving.…”

Section: Study 2-driving Intention Prediction Models Adapting To Mmentioning

confidence: 99%

Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions

Liu

Wang

2020

IJERPH

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Joining worldwide efforts to understand the relationship between driving emotion and behavior, the current study aimed at examining the influence of emotions on driving intention transition. In Study 1, taking a car-following scene as an example, we designed the driving experiments to obtain the driving data in drivers’ natural states, and a driving intention prediction model was constructed based on the HMM. Then, we analyzed the probability distribution and transition probability of driving intentions. In Study 2, we designed a series of emotion-induction experiments for eight typical driving emotions, and the drivers with induced emotion participated in the driving experiments similar to Study 1. Then, we obtained the driving data of the drivers in eight typical emotional states, and the driving intention prediction models adapted to the driver’s different emotional states were constructed based on the HMM severally. Finally, we analyzed the probabilistic differences of driving intention in divers’ natural states and different emotional states, and the findings showed the changing law of driving intention probability distribution and transfer probability caused by emotion evolution. The findings of this study can promote the development of driving behavior prediction technology and an active safety early warning system.

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Autonomic specificity in emotion: The induction method matters

Cited by 23 publications

References 54 publications

Performance Evaluation of Various Emotion Classification Approaches from Physiological Signals

Performance Evaluation of Various Emotion Classification Approaches from Physiological Signals

Film mood induction and emotion classification using physiological signals for health and wellness promotion in older adults living alone

Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions

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