A machine learning model for emotion recognition from physiological signals
Emotions are affective states related to physiological responses. This study proposes a model for recognition of three emotions: amusement, sadness, and neutral from physiological signals with the purpose of developing a reliable methodology for emotion recognition using wearable devices. Target emo...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8721
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8721
- Palabra clave:
- Affective computing
Biosignal processing
Emotion recognition
Machine learning
Physiological signals
Decision trees
Electrophysiology
Feature extraction
Learning systems
Machine learning
Physiological models
Speech recognition
Statistical tests
Support vector machines
Time domain analysis
Affective computing
Bio-signal processing
Emotion recognition
Frequency and time domains
Machine learning models
Physiological response
Physiological signals
Random forest-recursive feature eliminations
Biomedical signal processing
Adult
Article
Clinical article
Electrodermal response
Feature selection
Female
Heart rate
Human
Human experiment
Male
Photoelectric plethysmography
Random forest
Recursive feature elimination
Sadness
Support vector machine
Videorecording
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/