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An Analysis of Fluid Intake Assessment Approaches for Fluid Intake Monitoring System.

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Accurate fluid intake monitoring is crucial for hydration. This study found that analyzing inertial sensor data from wrist-worn devices and smart containers, especially considering sip size, significantly improves accuracy in assessing fluid consumption.

Keywords:
fluid intake assessmentgesture recognitionsmart-container sensorvolume estimationwrist-worn sensor

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Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Health Monitoring

Background:

  • Effective fluid intake monitoring is vital for maintaining adequate hydration and managing personal fluid consumption behaviors.
  • Existing inertial sensor-based fluid intake assessment methods are primarily categorized into wrist-worn and smart-container approaches.
  • Understanding the performance differences and influencing factors of these approaches is essential for developing accurate hydration tracking systems.

Purpose of the Study:

  • To comparatively analyze wrist-worn-based and smart-container-based fluid intake assessment approaches utilizing inertial sensors.
  • To evaluate the impact of gesture recognition and volume estimation on the performance of these two approaches.
  • To investigate the influence of fluid container fill level and individual sip size on the accuracy of fluid intake assessment.

Main Methods:

  • Utilized inertial sensors for data acquisition in both wrist-worn and smart-container setups.
  • Implemented postprocessing techniques to enhance gesture recognition accuracy.
  • Developed and compared sip-size-dependent models against general Support Vector Regression (SVR) models for volume estimation.

Main Results:

  • Achieved high gesture recognition accuracy: 92.89% for wrist-worn and 91.8% for smart-container approaches postprocessing.
  • Demonstrated that sip-size-dependent models significantly outperform general SVR models for volume estimation in both approaches.
  • Observed substantial improvements (over 50%) in Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), and Root Mean Square Error (RMSE), except for MAPE with small sip sizes.

Conclusions:

  • Inertial sensor-based fluid intake assessment is feasible with both wrist-worn and smart-container methods.
  • Accurate recognition of gestures and precise estimation of sip sizes are critical factors for effective fluid intake monitoring.
  • Incorporating sip size information into models significantly enhances the accuracy of volume estimation, paving the way for more personalized hydration tracking.