Performance Exploration of RNN Variants for Recognizing Daily Life Stress Levels by Using Multimodal Physiological Signals

Enduring stress can have negative impacts on human health and behavior. Widely used wearable devices are promising for assessing, monitoring and potentially alleviating high stress in daily life. Although numerous automatic stress recognition studies have been carried out in the laboratory environment with high accuracy, the performance of daily life studies is still far away from what the literature has in laboratory environments. Since the physiological signals obtained from these devices are time-series data, Recursive Neural Network (RNN) based classifiers promise better results than other machine learning methods. However, the performance of RNN-based classifiers has not been extensively evaluated (i.e., with several variants and different application techniques) for detecting daily life stress yet. They could be combined with CNN architectures, applied to raw data or handcrafted features. In this study, we created different RNN architecture variants and explored their performance for recognizing daily life stress to guide researchers in the field.