Investigation on the Use of Hidden Layers, Different Numbers of Neurons and Different Activation Functions to Detect Pupil Dilation Responses to Stress

Stress is an important problem for people that causes health problems and economic losses. When it becomes chronic, it paves the way for many diseases. Studies in this area have made significant progress in measuring stress levels with the help of data from wearable devices and sensors. In this study, using supervised deep learning methods, we worked on the detection of pupil dilation, which is accepted as one of the stress indicators. In our experiment, two different films containing positive and funny scenes and negative and stressful scenes were shown to the participants. Meanwhile, the pupil diameter was measured continuously. After the obtained signals were cleared of noises, deep learning studies were carried out on them. With these experiments, the effect of different activation functions used in hidden layers along with the different number of hidden layers and neuron numbers on learning were examined. After the trials with Hyperbolic Tangent, ReLU and Swish activation functions, the highest accuracy for classifying the stress of the participants from their pupil responses was obtained with the Swish activation function with 90.79%.