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- W4309157615 abstract "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-ShortTerm-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)." @default.
- W4309157615 created "2022-11-24" @default.
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- W4309157615 date "2022-10-10" @default.
- W4309157615 modified "2023-10-14" @default.
- W4309157615 title "LSTM Step Prediction and Ontology-Based Recommendation Generation in Activity eCoaching" @default.
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- W4309157615 doi "https://doi.org/10.1109/wimob55322.2022.9941356" @default.
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