Automatic food intake detection based on swallowing sounds

This paper presents a novel fully automatic food intake detection methodology, an important step toward objective monitoring of ingestive behavior. The aim of such monitoring is to improve our understanding of eating behaviors associated with obesity and eating disorders. The 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. Principal component analysis and a smoothing algorithm are used to improve swallowing detection accuracy. Second, the frequency of swallowing is used as a predictor for detection of food intake episodes. The proposed methodology was tested on data collected from 12 subjects with various degrees of adiposity. Average accuracies of >80% and >75% were obtained for intra-subject and inter-subject models correspondingly with a temporal resolution of 30 s. Results obtained on 44.1 h of data 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 may potentially be used in free-living conditions.

► Automatic detection of food intake based on swallowing data from wearable non-invasive sensor. ► 44.1 h of data from 12 subjects with a total of 7305 swallows. ► Average detection accuracies of >80% and >75% for individual and group models respectively. ► Detection accuracies comparable for obese and lean subjects. ► Principal component analysis and smoothing algorithm improve swallowing detection accuracy.