Predict Moves

TL;DR

This paper develops a predictive model for daily step goal achievement using data from activity trackers, exploring feature selection, model generalizability across platforms, and potential improvements with additional features.

Contribution

It introduces a data pipeline and evaluates feature and model choices for predicting step goal success, emphasizing cross-platform generalizability.

Findings

Model performance varies with feature selection.

Cross-platform models show potential but need further refinement.

Including non-standard features could enhance accuracy.

Abstract

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.