Physical Exercise Recommendation and Success Prediction Using Interconnected Recurrent Neural Networks

TL;DR

This paper introduces an interconnected RNN-based system that recommends personalized exercises and predicts success probabilities, leveraging workout history to promote healthier lifestyles through mobile health data.

Contribution

It presents a novel interconnected RNN architecture for personalized exercise recommendation and success prediction, improving accuracy over previous models.

Findings

Improved prediction accuracy over prior models

Effective use of workout history for recommendations

Validated on real mobile health data

Abstract

Unhealthy behaviors, e.g., physical inactivity and unhealthful food choice, are the primary healthcare cost drivers in developed countries. Pervasive computational, sensing, and communication technology provided by smartphones and smartwatches have made it possible to support individuals in their everyday lives to develop healthier lifestyles. In this paper, we propose an exercise recommendation system that also predicts individual success rates. The system, consisting of two inter-connected recurrent neural networks (RNNs), uses the history of workouts to recommend the next workout activity for each individual. The system then predicts the probability of successful completion of the predicted activity by the individual. The prediction accuracy of this interconnected-RNN model is assessed on previously published data from a four-week mobile health experiment and is shown to improve upon previous predictions from a computational cognitive model.