Emotional Status Determination using Physiological Parameters Data Set
This dataset is created with the usage of Galvanic Skin Response Sensor and Electrocardiogram sensor of MySignals Healthcare Toolkit. MySignals toolkit consists of the Arduino Uno board and different sensor ports. The sensors were connected to the different ports of the hardware kit which was controlled by Arduino SDK.
MySignals is a development platform for medical devices and e-Health applications. It is a multichannel physiological signal recorder which measures more than 15 different biometric parameters such as pulse, breath rate, oxygen in blood, electrocardiogram signals, blood pressure, muscle electromyography signals, glucose levels, galvanic skin response, lung capacity, snore waves, patient position, airflow and body scale parameters (weight, bone mass, body fat, muscle mass, body water, visceral fat, Basal Metabolic Rate and Body Mass Index).
This novel dataset can be applied for training and evaluating deep learning, machine learning and data analytics models to deal with binary and multi-class stress and emotion classification problem.
The manuscript utilizes the proposed dataset is available at https://journal.portalgaruda.org/index.php/EECSI/article/view/1943
The variation of user volunteering for providing readings for dataset is limited to only males between ages 20-22.
The dataset can be further split into training and testing set. The labels associated with each row is presented in the dedicated *.csv files for each of the sets.
The class assignment and specified labels associated with the dataset are as follows: Fear - (0), Angry- (1), Happy- (2) and Sad - (3)