Active Collection of Well-Being and Health Data in Mobile Devices

Code

• github.com/havatarproject/AndroidQuiz
  none★ 0

Abstract

In the context of self-reported health, where the subjective perception of the patients is reported through simple yet effective questionnaires, information gathering is very important to obtain consistent and meaningful data analysis. Smart phones are a good tool to gather self-reported variables, but the interaction with the user should go ahead of scheduled notifications. We develop an intelli- gent notification system that learns by exploration the most adequate time to perform a questionnaire, while using just the answers of the notification messages from the user. We address the smart notification as a Reinforcement Learning (RL) problem, considering several states representations and reward functions for the Upper Confidence Bound, Tabular Q-learning and Deep Q-learning. We evaluate the algorithms on a simulator, followed by an initial prototype where the approach with better performance in simulation is selected for a small pilot. The simulator acts as a person, accepting or discarding the notifications according to the behavior of a three typical users. From the simulation experiments the UCB algorithm showed the most promising results, so we implemented and deployed the RL algorithm in a smartphone application with several users. On this initial pilot with four users, the UCB algorithm was able to find the adequate hours to send notifications for quiz answering.

Tasks

Reproductions