Dr. Mohamed Younis, Tasnim Nishat Islam, Department of Computer Science and Electrical Engineering
Mental health has drawn increased attention in recent years, motivated by the social impact of the COVID-19 pandemic. Given the need to detect issues in early stages, relying on the conventional doctor visit based approach is not scalable and is too costly. The development of wearable mental health monitoring solutions is an effective means for filling the gap where individuals are provided with an assessment that can help them in adjusting their lifestyle. Stress is one of the most causes of mental health problems. We opt to serve such a goal by devising a novel wearable-based approach for detecting stress in real time. In this paper, we are experimenting with two benchmark datasets in this domain, namely, the WESAD Dataset and the SWELL Knowledge Work Dataset. For these datasets, the subjects are shown some videos; based on the situation provided, the subjects document their reaction by rating their feelings like valence, arousal, etc. Specifically, we are exploring the use of Electrocardiogram (ECG) and Photoplethysmography (PPG) data. Although Electroencephalogram (EEG) is usually used to assess stress, acquisition of EEG data is much more logistically-involving and expensive than ECG and PPG data. To determine the relevant features, we are studying how ECG and PPG can be correlated with EEG data using a third dataset, called Deap. To assess stress, we apply ConvLSTM and ResNet to the ECG and PPG signals. The paper evaluates the different statistical features and their computational complexity.