Identificador persistente para citar o vincular este elemento: https://accedacris.ulpgc.es/jspui/handle/10553/165285
Título: e-Health tools on emotional detection
Autores/as: Travieso González, Carlos M. 
Clasificación UNESCO: 3307 Tecnología electrónica
Fecha de publicación: 2020
Editor/a: Institute of Electrical and Electronics Engineers (IEEE)
Conferencia: International Conference on Contemporary Computing and Applications (IC3A) 2020 Lucknow
Resumen: The physiological signals also known biosignals, most common and used for biomedical and biometric identification, are the electrocardiogram (ECG) and electroencephalogram (EEG). ECG measures the electrical activity of the heart and EEG measures the electrical activity of the brain. There are other very rarely used signals that we consider studying as part of this work. For example, the electromyogram (EMG) which is a record of the electrical activity produced by the muscles and nerves and the galvanic skin response (GSR)or skin conductance, which is an indication of psychological or physiological arousal such as fear, anger or other feelings. The detection of the degree of emotion through physiological signals is a very poorly studied area that can offer a new and efficient system, which deals with using the combination of several physiological signals as a method of identifying the degree of emotion. The objective of this proposal is to analyze the physiological signals that show people's emotions, quantify it and perform an automatic detection, which can become an innovative and robust tool that shows the degree of emotion. To implement the system, digital image processing techniques and artificial intelligence methods will be applied to obtain an objective low-cost emotion measurement system using physiological signals.
URI: https://accedacris.ulpgc.es/jspui/handle/10553/165285
ISBN: 9781728154336
Fuente: 2020 International Conference on Contemporary Computing and Applications (IC3A)
Colección:Actas de congresos
Adobe PDF (149,97 kB)
Vista completa

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.