LSTM Step Prediction and Ontology-Based Recommendation Generation in Activity eCoaching

An eCoach system may allow people to manage a healthy lifestyle with health state monitoring (e.g., physical activity) and personalized recommendation generation. Daily step count is an important feature to provide a direct and indirect reflection on individual activity levels. Therefore, a personalized, predictive model may be beneficial to forecast future "steps" to motivate participants based on the temporal "step" pattern. Here, we have conceptualized the idea with a Bidirectional Long-Short-Term-Memory (LSTM) model for weekly activity forecasting and a rule-base for personalized recommendation generation with Ontology reasoning and querying in activity eCoaching. First, we have used the publicly available "PMData" dataset of 16 adults (M: 13; F:3) to train and test the models and explore the possibility of accurate univariate time-series forecasting of "step counts". Second, we have created an Ontology and a rule-base to generate personalized activity recommendations to motivate participants to accomplish their activity goals (e.g., complete "X" steps daily and stay active for the entire week).