Deep Learning for Wearable Data

Machine learning and deep learning algorithms have led to good human activity recognition and behavior recognition results, such as stress, depression, and anxiety. We have examined the effects of wearable  and survey data on students’ grades, stress, anxiety, and sleep. In this project, we want to add different modalities in the scope of the currently employed dataset, Nethealth, which will be communication-based information. Advanced Deep Learning algorithms such as GNN will be employed. Graph Theory is a plus.