Deep Learning for Multimodal Wearable Data

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.

Project Members: 

Ozan Kılıç

Project Advisor: 

Özlem Durmaz İncel

Project Status: 

Project Year: 

  • Fall

Contact us

Department of Computer Engineering, Boğaziçi University,
34342 Bebek, Istanbul, Turkey

  • Phone: +90 212 359 45 23/24
  • Fax: +90 212 2872461

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