Fast Physical Activity Suggestions: Efficient Hyperparameter Learning in Mobile Health

TL;DR

This paper introduces a computationally efficient contextual bandit algorithm using linear mixed effects models for providing timely physical activity suggestions in mobile health, significantly improving speed and accuracy.

Contribution

It presents a novel hyper-parameter learning method tailored for mobile health, combining domain knowledge with efficient matrix algebra updates.

Findings

Achieves up to 99% faster hyper-parameter updates

Improves suggestion accuracy by up to 56%

Demonstrates effectiveness in mobile health settings

Abstract

Users can be supported to adopt healthy behaviors, such as regular physical activity, via relevant and timely suggestions on their mobile devices. Recently, reinforcement learning algorithms have been found to be effective for learning the optimal context under which to provide suggestions. However, these algorithms are not necessarily designed for the constraints posed by mobile health (mHealth) settings, that they be efficient, domain-informed and computationally affordable. We propose an algorithm for providing physical activity suggestions in mHealth settings. Using domain-science, we formulate a contextual bandit algorithm which makes use of a linear mixed effects model. We then introduce a procedure to efficiently perform hyper-parameter updating, using far less computational resources than competing approaches. Not only is our approach computationally efficient, it is also easily implemented with closed form matrix algebraic updates and we show improvements over state of the art approaches both in speed and accuracy of up to 99% and 56% respectively.