Ubiquitous emotion-aware computing
Egon L. van den Broek
- 01 Jan 2013
- Vol. 17, Iss: 1, pp 53-67
TL;DR: This study explores the rare combination of speech, electrocardiogram, and a revised Self-Assessment Mannequin to assess people’s emotions, providing a significant leap toward robust, generic, and ubiquitous emotion-aware computing.
read more
Abstract: Emotions are a crucial element for personal and ubiquitous computing. What to sense and how to sense it, however, remain a challenge. This study explores the rare combination of speech, electrocardiogram, and a revised Self-Assessment Mannequin to assess people's emotions. 40 people watched 30 International Affective Picture System pictures in either an office or a living-room environment. Additionally, their personality traits neuroticism and extroversion and demographic information (i.e., gender, nationality, and level of education) were recorded. The resulting data were analyzed using both basic emotion categories and the valence--arousal model, which enabled a comparison between both representations. The combination of heart rate variability and three speech measures (i.e., variability of the fundamental frequency of pitch (F0), intensity, and energy) explained 90% (p < .001) of the participants' experienced valence--arousal, with 88% for valence and 99% for arousal (ps < .001). The six basic emotions could also be discriminated (p < .001), although the explained variance was much lower: 18---20%. Environment (or context), the personality trait neuroticism, and gender proved to be useful when a nuanced assessment of people's emotions was needed. Taken together, this study provides a significant leap toward robust, generic, and ubiquitous emotion-aware computing.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks
TL;DR: The proposed DGCNN method can dynamically learn the intrinsic relationship between different electroencephalogram (EEG) channels via training a neural network so as to benefit for more discriminative EEG feature extraction.
1.1K
Real-Time Movie-Induced Discrete Emotion Recognition from EEG Signals
TL;DR: A real-time movie-induced emotion recognition system for identifying an individual's emotional states through the analysis of brain waves from EEG signals with the advantage over the existing state-of-the-art real- time emotion recognition systems in terms of classification accuracy and the ability to recognise similar discrete emotions that are close in the valence-arousal coordinate space.
328
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications
TL;DR: The current status of BCI and signal sensing technologies for collecting reliable EEG signals are reviewed and state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, are demonstrated to detect, monitor, and maintain human cognitive states and task performance in prevalent applications.
EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder.
TL;DR: A novel deep neural network is proposed for emotion classification using EEG systems, which combines the Convolutional Neural Network, Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together.
Multi-channel EEG-based emotion recognition via a multi-level features guided capsule network.
TL;DR: The proposed MLF-CapsNet is an end-to-end framework, which can simultaneously extract features from the raw EEG signals and determine the emotional states and incorporates multi-level feature maps learned by different layers in forming the primary capsules so that the capability of feature representation can be enhanced.
189
References
Measuring emotion: The self-assessment manikin and the semantic differential
Margaret M. Bradley,Peter Lang +1 more
TL;DR: Reports of affective experience obtained using SAM are compared to the Semantic Differential scale devised by Mehrabian and Russell (An approach to environmental psychology, 1974), which requires 18 different ratings.
9K
•Book
Computational Geometry: Algorithms and Applications
Mark de Berg,Otfried Cheong,Marc van Kreveld,Mark H. Overmars +3 more
- 01 Jan 1997
TL;DR: In this article, an introduction to computational geometry focusing on algorithms is presented, which is related to particular applications in robotics, graphics, CAD/CAM, and geographic information systems.
5.8K
•Book
Affective Computing
Rosalind W. Picard
- 01 Jan 1997
TL;DR: Key issues in affective computing, " computing that relates to, arises from, or influences emotions", are presented and new applications are presented for computer-assisted learning, perceptual information retrieval, arts and entertainment, and human health and interaction.
5.7K
Core Affect and the Psychological Construction of Emotion
TL;DR: At the heart of emotion, mood, and any other emotionally charged event are states experienced as simply feeling good or bad, energized or enervated, which influence reflexes, perception, cognition, and behavior.