A self-monitoring water bottle for tracking liquid intake

This paper presents the key concepts, system architecture, implementation details, and performance of an accelerometer-equipped bottle for monitoring and tracking liquid intake. The key system component is an elastic band, equipped with sensor and other electronics, which can be attached to a regular water bottle in order to track the bottle's usage movements. The software running on the band captures and detects acceleration signatures that the bottle experiences specifically during drinking events. Detecting such drinking events can lead to higher level monitoring such as tracking the consumed liquid volume. A Bluetooth based wireless link out of the electronic band is used for sending the detected drinking events to a smartphone or to a notebook computer for higher level tracking and data management. Different machine learning methods were adopted and experimented with for both drinking event detection and intake volume estimation. Through experiments on nine healthy subjects, the system is shown to be able to achieve up to 99% accuracy in drinking event detection, and up to 75% accuracy for intake volume estimation.