Share Email Print
cover

Proceedings Paper

Liquid intake monitoring through breathing signal using machine learning
Author(s): Bo Dong; Subir Biswas
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

This paper presents the design, system structure and performance for a wireless and wearable diet monitoring system. Food and drink intake can be detected by the way of detecting a person’s swallow events. The system works based on the key observation that a person’s otherwise continuous breathing process is interrupted by a short apnea when she or he swallows as a part of solid or liquid intake process. We detect the swallows through the difference between normal breathing cycle and breathing cycle with swallows using a wearable chest-belt. Three popular machine learning algorithms have been applied on both time and frequency domain features. Discrimination power of features is then analyzed for applications where only small number of features is allowed. It is shown that high detection performance can be achieved with only few features.

Paper Details

Date Published: 29 May 2013
PDF: 8 pages
Proc. SPIE 8723, Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring III, 872315 (29 May 2013); doi: 10.1117/12.2018130
Show Author Affiliations
Bo Dong, Michigan State Univ. (United States)
Subir Biswas, Michigan State Univ. (United States)


Published in SPIE Proceedings Vol. 8723:
Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring III
Šárka O. Southern, Editor(s)

© SPIE. Terms of Use
Back to Top