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

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  • 1Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA. griff561@msu.edu.

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This study improves fluid intake estimation using a container-attachable sensor. It enhances accuracy for drink volume and aggregate consumption, outperforming previous wearable sensors.

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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Wearable Technology

Background:

  • Inadequate fluid consumption has negative health impacts.
  • Motion-based sensors estimate fluid intake via drinking kinematics.
  • Existing methods face challenges with accuracy and individual biomechanics.

Purpose of the Study:

  • To enhance fluid intake estimation accuracy using a container-attachable triaxial accelerometer.
  • To address limitations in motion-based sensing for drink volume and fill level.
  • To improve upon previous state-of-the-art accuracy for fluid consumption monitoring.

Main Methods:

  • Utilized support vector machine regression with hand-engineered features for drink volume estimation.
  • Collected data from 84 individuals consuming 1908 drinks from a refillable bottle.
  • Explored fill level regression and segmentation of drink motion into transport and sip phases.

Main Results:

  • Reduced per-drink mean absolute percentage error by 11.05% compared to wrist-wearable IMU sensors.
  • Achieved improved estimates of aggregate consumption compared to prior attachable sensor results.
  • Demonstrated improved accuracy and reduced inter-subject variability with fill level regression models.

Conclusions:

  • The container-attachable sensor offers a more accurate and reliable method for monitoring fluid intake.
  • Fill level estimation presents a promising avenue for enhanced accuracy and reduced variability.
  • A multi-target framework effectively addresses the interdependence of volume and fill level in motion signatures.