Liquid intake monitoring through breathing signal using machine learning

Bo Dong; Subir Biswas

doi:10.1117/12.2018130

29 May 2013 Liquid intake monitoring through breathing signal using machine learning

Bo Dong, Subir Biswas

Proceedings Volume 8723, Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring III; 872315 (2013) https://doi.org/10.1117/12.2018130
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

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.

Citation Download Citation

Bo Dong and Subir Biswas "Liquid intake monitoring through breathing signal using machine learning", Proc. SPIE 8723, Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring III, 872315 (29 May 2013); https://doi.org/10.1117/12.2018130

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