Emotion Recognition Based on Physiological Signals for Neuroscience and Marketing
Date : 12-Apr-2024

Principal Investigator

Talal Bin Afzal

Lecturer, Department of Software Engineering Science

(FE &CS)    

This research focuses on the evaluation of neuroscience and emotional marketing. It identifies 6 basic emotions (sadness, anger, frustration, fear, surprise, amusement) based on some physical parameters: GSR (Galvanic Skin Response), HRV (Heart Rate Variability), and SKT (Skin temperature measurements). The sensor will capture physiological signals from the human body and pass these signals to the database. Here, the signals will be analyzed, and machine learning algorithms will detect emotions. These results will be used to analyze the impact of our product on the target audience.

Completed Activites

1- Physiological Signal Acquisition Setup:

    Description: Establishment of a reliable setup for acquiring physiological signals such as heart rate, skin conductance, and facial

                          expressions for emotion recognition.

Ongoing Activites

2- Data Collection and Labeling:

    Description: Collection of diverse datasets, including physiological signals synchronized with labeled emotional states, for training and

                           testing emotion recognition models.

Future Activites

3- Model Training for Emotion Recognition:

    Description: Training machine learning models using acquired data to accurately recognize and classify emotions based on physiological


4- Integration with Marketing Strategies:

    Description: Integration of the developed emotion recognition system with marketing strategies, exploring applications for targeted

                           advertising and consumer engagement.

Completion Status