Predict Moves
This work addresses the need for personalized physical activity interventions for users of mobile and on-body tracking devices, but it is incremental as it builds on existing prediction methods with new data and features.
The paper tackles the problem of predicting whether a user will reach their daily step goal using historical and qualitative/quantitative features from activity tracking data, and finds that the model derived from one platform can generalize to another.
Mobile applications and on-body devices are becoming increasingly ubiquitous tools for physical activity tracking. We propose utilizing a self-tracker's habits to support continuous prediction of whether they will reach their daily step goal, thus enabling a variety of potential persuasive interventions. Our aim is to improve the prediction by leveraging historical data and other qualitative (motivation for using the systems, location, gender) and, quantitative (age) features. We have collected datasets from two activity tracking platforms (Moves and Fitbit) and aim to check if the model we derive from one is generalizable over the other. In the following paper we establish a pipeline for extracting the data and formatting it for modeling. We discuss the approach we took and our findings while selecting the features and classification models for the dataset. We further discuss the notion of generalizability of the model across different types of dataset and the probable inclusion of non standard features to further improve the model's accuracy.