Automatic Eating Detection Using Head-Mount and Wrist-Worn Accelerometers

Automatic Eating Detection (AED) provides an important tool to help users regulate their dietary behavior for many health applications, such as weight management. In this paper we propose an AED solution using a head-mount and a wrist-worn accelerometers that are commonly available in commercial wearable devices. Experimental results, using Google Glass and Pebble Watch, validated that the proposed approach is highly effective to detect head motion from chewing and to detect hand-to-mouth (HtM) gestures when eating, resulting in 89.5% to 95.1% detection accuracy. Further we combined the features from both devices to achieve 97% cross-person eating detection accuracy and the average error when predicting duration of eating meals was only 105 seconds.