A self-monitoring water bottle for tracking liquid intake

Abstract

-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.

A Dynamic Partitioning Algorithm for Sip Detection using a Bottle-Attachable IMU Sensor

“…The proposed algorithm was benchmarked against a slight variation of our previously considered technique [17]. Partitioning was performed using an SSW scheme ( ).…”

“…Partitioning was performed using an SSW scheme ( ). A slightly modified version of the four-element feature space used in [17] was employed as specified in (10)- (13).…”

“…We have previously introduced an alternative solution for real-time hydration tracking using an attachable inertial measurement unit (IMU) sensor as shown in Fig. 1 [17]. This architecture offers similar advantages to traditional smart bottles with respect to privacy and usability, while providing reconfigurability across multiple drinking containers.…”

“…While promising as an initial proof-of-concept, the excessive sliding window duration considered in [17] inherently limits the ability to resolve drinks which are closelyseparated in time, thereby limiting pragmatic viability without additional post-processing modifications. While such concerns may be partially addressed through reduction of window duration, SSW paradigms are still characterized by many known disadvantages, including inherent processing inefficiencies for sporadically occurring events, edge effects at event boundaries, and limited spotting precision for variableduration events.…”

A Container-Attachable Inertial Sensor for Real-Time Hydration Tracking

“…Previous research has also proposed a container-attachable IMU sensor for hydration tracking applications [11]. This approach alleviates the restrictiveness of augmented containers by allowing for simplistic reconfiguration across multiple drinking vessels [12].…”