Abstract
Various sensors have been proposed to address the negative health ramifications of inadequate fluid consumption. Amongst these solutions, motion-based sensors estimate fluid intake using the characteristics of drinking kinematics. This sensing approach is complicated due to the mutual influence of both the drink volume and the current fill level on the resulting motion pattern, along with differences in biomechanics across individuals. While motion-based strategies are a promising approach due to the proliferation of inertial sensors, previous studies have been characterized by limited accuracy and substantial variability in performance across subjects. This research seeks to address these limitations for a container-attachable triaxial accelerometer sensor. Drink volume is computed using support vector machine regression models with hand-engineered features describing the container’s estimated inclination. Results are presented for a large-scale data collection consisting of 1908 drinks consumed from a refillable bottle by 84 individuals. Per-drink mean absolute percentage error is reduced by 11.05% versus previous state-of-the-art results for a single wrist-wearable inertial measurement unit (IMU) sensor assessed using a similar experimental protocol. Estimates of aggregate consumption are also improved versus previously reported results for an attachable sensor architecture. An alternative tracking approach using the fill level from which a drink is consumed is also explored herein. Fill level regression models are shown to exhibit improved accuracy and reduced inter-subject variability versus volume estimators. A technique for segmenting the entire drink motion sequence into transport and sip phases is also assessed, along with a multi-target framework for addressing the known interdependence of volume and fill level on the resulting drink motion signature.
Highlights
The underconsumption of water is a considerable global health concern [1,2]
Consumption estimates were formulated using support vector machine regression models with hand-engineered features describing the inclination of the container during drinking
Results were presented for an experiment consisting of 1908 drinks consumed by 84 participants
Summary
The underconsumption of water is a considerable global health concern [1,2]. Estimates suggest that 20% of adults exhibit significant dehydration, which is associated with numerous adverse health outcomes [3]. Recent evidence suggests that even mild underconsumption of water may have various negative health ramifications, including reduced cognitive function, obesity, and cancer [4]. The lack of an appropriate fluid intake is especially concerning for elderly individuals, due to the degradation of regulatory mechanisms with age [5]. Numerous sensing technologies have been demonstrated for tracking fluid consumption. Solutions include augmented drinking containers [6], which are currently available in the commercial marketplace, along with wearable [7] and contactless architectures [8]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call