Estimation of feature importance for food intake detection based on Random Forests classification

Abstract

Selection of the most representative features is important for any pattern recognition system. This paper investigates the importance of time domain (TD) and frequency domain (FD) features used for automatic food intake detection in a wearable sensor system by using Random Forests classification. Features were extracted from signals collected using 3 different sensor modalities integrated into the Automatic Ingestion Monitor (AIM): a jaw motion sensor, a hand gesture sensor and an accelerometer. Data was collected from 12 subjects wearing AIM in free-living for a 24-hr period where they experienced unrestricted intake. Features from the sensor signals were used to train the Random Forests classifier that estimated the importance of each feature as part of the training process. Results indicated that FD features from the jaw motion signal and TD features from the accelerometer signal were the most relevant features for food intake detection.