Spectrogram-based audio classification of nutrition intake

Author

Kalantarian, Haik ; Alshurafa, Nabil ; Pourhomayoun, Mohammad ; Sarin, Shruti ; Tuan Le ; Sarrafzadeh, Majid

Author_Institution

Comput. Sci. Dept., Univ. of California, Los Angeles, Los Angeles, CA, USA

fYear

2014

fDate

8-10 Oct. 2014

Firstpage

161

Lastpage

164

Abstract

Acoustic monitoring of food intake in an unobtrusive, wearable form-factor can encourage healthy dietary choices by enabling individuals to monitor their eating patterns, maintain regularity in their meal times, and ensure adequate hydration levels. In this paper, we describe a system capable of monitoring food intake by means of a throat microphone, classifying the data based on the food being consumed among several categories through spectrogram analysis, and providing user feedback in the form of mobile application. We are able to classify sandwich swallows, sandwich chewing, water swallows, and none, with an F-Measure of 0.836.

Keywords

biomechanics; body sensor networks; medical signal detection; medical signal processing; microphones; mobile radio; patient monitoring; signal classification; telemedicine; acoustic monitoring; food intake monitoring; hydration levels; mobile application; nutrition intake monitoring; sandwich chewing classification; sandwich swallow classification; spectrogram-based audio classification; throat microphone; unobtrusive form-factor; water swallow classification; wearable form-factor; Biomedical monitoring; Classification algorithms; Feature extraction; Monitoring; Obesity; Spectrogram; Time-frequency analysis; nutrition; spectrogram; swallow detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Healthcare Innovation Conference (HIC), 2014 IEEE

Conference_Location

Seattle, WA

Type

conf

DOI

10.1109/HIC.2014.7038899

Filename

7038899