Automatic Dietary Monitoring Using Inertial Sensor in Smartwatch

This paper investigates the problem of eating activity detection using motion data from an off-the-shelf smartwatch. The development and integration of the algorithm for detecting eating activity will make it easier for users to monitor their eating habits. For development of a such algorithm about 27500 hours of data were collected from 91 participants. Moreover, a reliable and interpreted approach with adjustable tolerance for model quality estimation in real-world conditions is proposed in this work. The algorithm based on end-to-end neural network (NN) for eating events detection with special postprocessing was developed by our research group. It recognizes eating events with 1 minute delay from the beginning of food intake. For a such tolerance it achieves F1-score of 0.90 in average (at "free-living" scenario test) for users wearing smartwatches either on dominant or on non-dominant hand. To the best of authors’ knowledge, the algorithm provides the best performance of any existing solution or described in the literature.