Active Data Collection of Health Data in Mobile Devices

Ana Machado, Heitor Cardoso, Plinio Moreno, Alexandre Bernardino

2022

Abstract

This paper aims to develop an intelligent notification system to help sustain user engagement in mHealth applications, specifically those that support self-management. We rely on Reinforcement Learning (RL), an approach where agent learns by exploration the most opportune time to perform a questionnaire, throughout their day, only from easily obtainable non-sensitive data and usage history. This history allows the agent to remember how the user reacts or has reacted in the past to its actions. We consider several options on algorithm, state representation and reward function under the RL umbrella (Upper Confidence Bound, Tabular Q-learning and Deep Q-learning). In addition, a simulator was developed to mimic the behavior of a typical user and utilized to test all possible combinations with users experiencing distinct lifestyles. We obtain promising promising results, which still requiring further testing to be fully validated. We demonstrate that an efficient and well-balanced notification system can be built with simple formulations of an RL problem and algorithm. Furthermore, our approach does not require to have access to sensitive user data. This approach diminishes privacy issues that might concern the user and limits sensor and hardware concerns, such as lapses in collected data or battery drainage.

Paper Citation

in Harvard Style

Machado A., Cardoso H., Moreno P. and Bernardino A. (2022). Active Data Collection of Health Data in Mobile Devices. In Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-584-5, pages 160-167. DOI: 10.5220/0011300700003277

in Bibtex Style

@conference{delta22,
author={Ana Machado and Heitor Cardoso and Plinio Moreno and Alexandre Bernardino},
title={Active Data Collection of Health Data in Mobile Devices},
booktitle={Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2022},
pages={160-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011300700003277},
isbn={978-989-758-584-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - Active Data Collection of Health Data in Mobile Devices
SN - 978-989-758-584-5
AU - Machado A.
AU - Cardoso H.
AU - Moreno P.
AU - Bernardino A.
PY - 2022
SP - 160
EP - 167
DO - 10.5220/0011300700003277