Predicting Eating Events in Free Living Individuals -- A Technical Report
This work addresses the problem of monitoring dietary behaviors for health applications, but it is incremental as it applies existing methods to new data without major innovations.
The study applied multiple machine learning algorithms to predict eating and food purchasing events in free-living individuals using sensor data, achieving a highest accuracy of 0.7289 for eating events with Gradient Boosting and 0.7395 for food purchasing events with RBF-SVM.
This technical report records the experiments of applying multiple machine learning algorithms for predicting eating and food purchasing behaviors of free-living individuals. Data was collected with accelerometer, global positioning system (GPS), and body-worn cameras called SenseCam over a one week period in 81 individuals from a variety of ages and demographic backgrounds. These data were turned into minute-level features from sensors as well as engineered features that included time (e.g., time since last eating) and environmental context (e.g., distance to nearest grocery store). Algorithms include Logistic Regression, RBF-SVM, Random Forest, and Gradient Boosting. Our results show that the Gradient Boosting model has the highest mean accuracy score (0.7289) for predicting eating events before 0 to 4 minutes. For predicting food purchasing events, the RBF-SVM model (0.7395) outperforms others. For both prediction models, temporal and spatial features were important contributors to predicting eating and food purchasing events.