Feed Forward Classification Neural Network for Prediction of Human Affective States Using Continuous Multi Sensory Data Acquisition

Abstract

Machines that are able to recognize and predict human affective states or emotions have become increasingly desirable over the last three decades [10]. This can be attributed to their relevance to human endeavors accompanied by the ubiquity of computing devices and an increasing trend for technology to be ever more present and engrained in people’s daily lives. There have been advancements in AI applications that are able to detect a person’s affective states through the use of machine learning models. These advancements are mostly based on architectures that take as inputs facial expression image data instances or highly specific sensor data, such as ECG or skin conductivity readings. The problem with these approaches is that models are not being designed with capabilities to receive, and modularly add multiple sensory inputs, therefore failing to operate and deal with the differences that exist on how individuals experience emotions. The present publication proposes a methodology consisting of a continuous multi sensory data acquisition process, and the construction of a feed forward classification neural network with three, 200 neuron, hidden layers. Experimentation was carried out on six different subjects, collecting over 100 h of data points containing environmental and personal variables such as activity (accelerations), light exposure, temperature and humidity. A different model was trained for each one of the subjects for 60–1500 epochs, yielding individual prediction accuracies, on test sets, of 82(%)–95(%).