Title :
Wearable diet monitoring through breathing signal analysis
Author :
Bo Dong ; Biswas, Santosh
Author_Institution :
Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA
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 extracted time and frequency domain features. It is shown that high detection performance can be achieved with only few features.
Keywords :
biomechanics; body sensor networks; feature extraction; learning (artificial intelligence); medical signal detection; medical signal processing; patient monitoring; pneumodynamics; signal classification; breathing interruption; breathing signal analysis; diet monitoring system design; diet monitoring system performance; diet monitoring system structure; drink intake; extracted frequency domain feature; extracted time domain feature; food intake; high detection performance; liquid intake process; machine learning algorithm; normal breathing cycle; short apnea; solid intake process; swallow event detection; wearable chest-belt; wearable diet monitoring system; wireless diet monitoring system; Biomedical monitoring; Feature extraction; Frequency-domain analysis; Liquids; Monitoring; Obesity; Solids; Breathing Pattern Analysis; Classifiers; Food Intake Monitoring; Swallow Detection; Wearable Sensors;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Conference_Location :
Osaka
DOI :
10.1109/EMBC.2013.6609718