MIT EECS | Aptiv Undergraduate Research and Innovation Scholar
Electrical Engineering and Computer Science
Research Project Title:
Visualizing and Predicting Human Behavior
abstract:Human behavior and psychosocial factors contribute significantly to a myriad of global diseases, including cardiovascular disease, respiratory disease, diabetes, and drug addiction. Understanding, predicting, and analyzing human behavior remains a major unresolved problem in healthcare and medicine. One technique developed to begin addressing this issue is digital phenotyping, or the use of digital and sensor information to infer human behavior.
Previously, we developed a mobile phone-based platform that collects sensor data to help track, predict, and visualize human behavior and mental health. We have also created a data preprocessing scripts and algorithms in order to predict if someone is asleep or awake based on the mobile sensor data. Ultimately, the purpose is to create a visual representation of data and digital phenotyping outputs to help users detect patterns in their behavior. To achieve this goal, we will be running a pilot study to evaluate the efficacy of our system and show how the system can be extrapolated for predicting behaviors beyond wakefulness.
Digital phenotyping is a way to use the technology we already have to make healthcare more accessible and user-oriented.