Real-time swallowing detection based on tracheal acoustics

Wearable systems play an important role in continuous health monitoring and can contribute to early detection of abnormal events. The ability to automatically detect swallowing in real-time can provide valuable insight into eating behavior, medication adherence monitoring, and diagnosis and evaluation of swallowing disorders. In this paper, we have developed a real-time swallowing detection algorithm based on acoustic signals that combines computationally inexpensive features to achieve comparable performance with previously proposed methods using acoustic and non-acoustic data. With data from four healthy subjects that includes common tracheal events such as speech, chewing, coughing, clearing the throat, and swallowing of different liquids, our results show an overall recall performance of 79.9% and precision of 67.6%.