The Internet of Things (IoT) is a network of physical devices (e.g., smartphones, smart watches etc.), having the capacity to sense, collect and communicate data seamlessly. The IoT-Health research program aims to capitalize on advancements in technology along with sophisticated information and data processing to better understand disease progression in chronic health conditions such as mental illness and cancer. In this research program we couple IoT processes with Machine Learning (Deep Learning Neural Networks), to gather passive data about human health and then use this information to develop predictive algorithms for chronic diseases. |
Project 1: Mood Inference Engine.
Project 2: Relapse Prediction in Mental Illness.
Project 3: Doctor's Resilience.
Project 4: Adherence prediction for chronic disease patients.
Project 5: Mood incorporated recommendation systems.
Project 6: Marker bias from mood inference.
Project 7: Distress Detection in Cancer Patient
The aim of the research is to develop an automated system to detect “distress” in cancer patients enabling early referral to interventions, to target anxiety and depression, to mitigate suicidal ideation and to improve adherence to treatment, which would significantly improve quality of life for individuals and reduce the costs on the health care system.
Collaborators |