Detection of Swallowing Events to Quantify Fluid Intake in Older Adults Based on Wearable Sensors

The percentage of adults aged 65 an over, defined as older adults, is prospected to increase in the coming decades over the total population. With such an increase, it is essential that healthcare technologies evolve to cater for the needs of an aging population. One such need is hydration: due to both physiological and psychological reasons, older adults tend to be more prone to develop dehydration, which in turn increases the chances of morbidity and mortality. Currently, there are no gold standards to monitor hydration, with most methods relying on filling manual forms. Thus, there is an urgent need to develop techniques which can accurately monitor fluid intake and prevent dehydration in older adult, especially those residing in healthcare settings. Several methods, such as smart cups and on-body sensors such as microphones, were proposed in the literature, however none of these have been widely investigated, and often the presented results were based on an extremely small cohort. Therefore, the scope of this PhD project is to investigate and develop methods that can detect swallowing events and that can quantify the volume of ingested fluids by leveraging on signals harvested using non-invasive, on-body sensors. Two types of on-body sensors were selected and used throughout this research: namely surface Electromyographic sensors (sEMG) and microphones. These sensors were then used to collect sound and electric signals from the subjects while swallowing boluses of different viscosities and while performing actions not related to swallowing but that could recruit the same muscles or produce similar sounds, such as talking or coughing. Features were then extracted from the collected observations and used to train Machine Learning (ML) and Deep Learning (DL) models to analyse their ability to differentiate between swallowing and non-swallowing actions, to distinguish between different bolus types, and to quantify the volume of fluid ingested. Results showed a precision of 81.55±3.40% in differentiating between swallows and non-swallows and a precision of 81.74±8.01% in distinguishing between bolus types, both given by the sEMG. Also, a root mean square error (RMSE) of 3.94±1.31 ml in estimating fluid intake was obtained using the microphone. The significance of the findings exposed in this thesis rely on the fact that surface EMGs and microphones demonstrate a significant potential in fluid intake monitoring, and on the concrete possibility of developing a non-invasive, reliable system that could prevent dehydration in older adults living in healthcare settings