Automatic food intake detection based on swallowing sounds,Oleksandr Makeyev, Paulo Lopez-Meyer, Stephanie Schuckers, Walter Besio, Edward Sazonov, Biomedical Signal Processing and Control, Available online 6 April 2012, ISSN 1746-8094, 10.1016/j.bspc.2012.03.005.


This paper presents a novel fully automatic food intake detection methodology, an important step toward objective monitoring of ingestive behavior. Aim of such monitoring is to improve our understanding of eating behaviors associated with obesity and other eating disorders. Proposed methodology consists of two stages: first, acoustic detection of swallowing instances based on mel-scale Fourier spectrum features and classification using support vector machines is performed and principal component analysis and a smoothing algorithm are used to improve swallowing detection accuracy. Second, frequency of swallowing is used as a predictor for detection of food intake episodes. Proposed methodology was tested on data collected from 12 subjects with various degrees of adiposity. Average accuracies of >80% and >70% were obtained for intra-subject and inter-subject models correspondingly with a fine time resolution of 30s. Results obtained on 44.1 hours with a total of 7305 swallows show that detection accuracies are comparable for obese and lean subjects. They also suggest feasibility of food intake detection based on swallowing sounds and potential of the proposed methodology for automatic monitoring of ingestive behavior. Based on a wearable non-invasive acoustic sensor the proposed methodology can potentially be used in free-living conditions.