Swallowing is an important part of the dietary process. This paper presents an investigation to detect and classify normal swallowing during eating and drinking from electromyography and microphone sensors. The non-invasive sensors are selected in order to integrate them into a collarlike fabric for continuous monitoring of swallowing activity over a day. We compare methods for the detection of individual swallowing events from continuous sensor data. Furthermore we present a classifier comparison for the swallowing event properties volume and viscosity. The methods are evaluated on experimental data and a performance analysis is shown. Moreover we present a class skew analysis based on the metrics precision and recall.
|Title of host publication||Proceedings 2006 Pervasive Health Conference and Workshops, PervasiveHealth, 29 November 2006 through 1 December 2006, Innsbruck|
|Place of Publication||Piscataway|
|Publisher||Institute of Electrical and Electronics Engineers|
|Publication status||Published - 2007|