What Shall We Teach Our Pants?
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
Detection of eating and drinking arm gestures using inertial body-worn sensors
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Recognition of dietary activity events using on-body sensors
Artificial Intelligence in Medicine
Power efficient multi-band contextual activity monitoring for assistive environments
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
On-Body Sensing Solutions for Automatic Dietary Monitoring
IEEE Pervasive Computing
Analysis of chewing sounds for dietary monitoring
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
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Obesity is a worldwide epidemic and is an underlying cause for most major chronic diseases. In majority of cases the underlying cause for obesity is a greatly skewed imbalance between the food intake and the number of calories burnt by the patient. One of the first steps in managing obesity is the correct recording of food and fluids that are ingested in the body. Traditional methods like food diaries have generally produced grossly inaccurate results. In order to automate the process of capturing ingestion, a method for detecting, analyzing, and recording sounds related to ingestion is being developed. In this paper, preliminary swallow sound analysis is presented with the intention of implementing automated ingestion detection as part of an obesity and overweight management system. Three basic algorithmic approaches are discussed as well as filtering options. More complex methods for analysis are explored as well, which include nonlinear analysis and the use of Self Organizing Maps (SOM).