Lids: Mobile System To Monitor Type And Volume Of Liquid Intake

Fluid intake tracking is crucial in providing interventions that assist individuals to stay hydrated by maintaining an adequate amount of fluid. It also helps to manage calorie intake by accounting for the amount of calorie consumed from beverages. While staying hydrated and controlling calorie intake is critical in both physical wellness and cognitive health, existing technologies do not provide a solution for monitoring both fluid type and fluid volume. To address this limitation, we present the design, implementation, and validation of Liquid Intake Detection System (LIDS) for real-time tracking of fluid intake type and volume. The system devises a sensing module that is composed of ultrasonic, RGB color, temperature, and accelerometer sensors as well as a computational framework for machine-learning-based fluid intake type classification, volume estimation, and bottle-state-recognition. The developed sensing unit is small and light-weight that can be mounted, from inside, on the lid of a drinking bottle. We conduct extensive experiments to collect data in a variety of bottles and environmental settings. The results show that the accuracy of fluid type detection ranges from 74.93% to 94.98% while trying to detect the fluid of an unseen bottle. Our results for volume estimation show that the regression-based volume estimation supports a root-relative-squared-error that ranges from 1.12% to 13.36%.