Extracting Markers from physiological and survey data collected from wearable devices

Wearable devices can capture multimodal data corresponding to a person’s activity, stress, 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 stress, also it may include some subjective assessments related to health satisfaction, overall health, happiness, diet etc.

In this project, the aim is to examine the relation between physiological and survey data, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions using statistical and machine learning methods.