Liquid intake monitoring through breathing signal using machine learning
Bo Dong, Subir Kumar Biswas · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
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.