Fast Physical Activity Suggestions: Efficient Hyperparameter Learning in Mobile Health
This work provides a more computationally affordable and efficient method for delivering personalized physical activity suggestions, which is crucial for mHealth researchers and practitioners.
This paper addresses the challenge of providing efficient and timely physical activity suggestions on mobile devices using reinforcement learning. They developed a contextual bandit algorithm with a linear mixed effects model and an efficient hyperparameter updating procedure, achieving up to 99% speed improvement and 56% accuracy improvement over state-of-the-art methods.
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.