Stress Assessment with Convolutional Neural Network Using PPG Signals
This work addresses stress assessment for individuals using wearable sensors, but it is incremental as it applies a hybrid neural network to an existing dataset.
The researchers tackled stress detection by developing a novel adaptive CNN-MLP model using raw PPG signals from the WESAD dataset, achieving an accuracy of 96.7% in distinguishing between normal and stressful events.
Stress is one of the main issues of nowadays lifestyle. If it becomes chronic it can have adverse effects on the human body. Thus, the early detection of stress is crucial to prevent its hurting effects on the human body and have a healthier life. Stress can be assessed using physiological signals. To this end, Photoplethysmography (PPG) is one of the most favorable physiological signals for stress assessment. This research is focused on developing a novel technique to assess stressful events using raw PPG signals recorded by Empatica E4 sensor. To achieve this goal, an adaptive convolutional neural network (CNN) combined with Multilayer Perceptron (MLP) has been utilized to realize the detection of stressful events. This research will use a dataset that is publicly available and named wearable stress and effect detection (WESAD). This dataset will be used to simulate the proposed model and to examine the advantages of the proposed developed model. The proposed model in this research will be able to distinguish between normal events and stressful events. This model will be able to detect stressful events with an accuracy of 96.7%.