Deep Learning for Multimodal Wearable Data

Wearable devices can capture multimodal data corresponding to a person’s activity, stress, and sleep information to measure and improve health and well-being. Besides device measurement, there are also survey data as another information-gathering methodology. These can be related to gold standard questionnaires for sleep and st. Also, it may include some subjective assessments related to health satisfaction, overall health, happiness, diet, etc. In this project, the aim is to apply state-of-the-art deep learning techniques such as CNN, GNN and LSTM.