Physical Exercise Recommendation and Success Prediction Using Interconnected Recurrent Neural Networks (Preprint)
BACKGROUND 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. OBJECTIVE This paper proposes an exercise recommendation system to recommend daily exercises to elderly population. METHODS 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. RESULTS 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. The proposed system is able to predict the next exercise for each individual with 80% accuracy. CONCLUSIONS The dual-RNN system for recommending workout exercises along with predicting individual success rates achieves high accuracy for individuals from whom we do not have any training data. The proposed system was validated this achievement by training the proposed model on a set of users and testing on a new set of test users. Future studies will involve combinations of explanatory computational models such as ACT-R and machine learning approaches such as the dual-RNN system to address the shortcomings of existing recommendations systems in need of large sample size.