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Towards a Generalizable Method for Detecting Fluid Intake with Wrist-Mounted Sensors and Adaptive Segmentation.

Keum San Chun1, Ashley B Sanders2, Rebecca Adaimi3

  • 1The University of Texas at Austin, Austin, Texas, gmountk@utexas.edu.

IUI. International Conference on Intelligent User Interfaces
|April 30, 2019
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Summary
This summary is machine-generated.

This study introduces a new method using a wrist-worn device to automatically detect fluid intake gestures. The system achieved high accuracy, paving the way for better health behavior monitoring.

Keywords:
ACM proceedingsLATEXtext tagging

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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Wearable Technology

Background:

  • Mobile technologies enable health behavior monitoring.
  • Automated dietary monitoring is researched, but fluid intake tracking is less explored.
  • Passive sensing for health behaviors is a growing area.

Purpose of the Study:

  • To develop and evaluate a method for passively detecting fluid intake gestures using inertial data from a wrist-worn device.
  • To assess the generalizability of the proposed detection method across different participants.
  • To contribute a labeled dataset for future research in fluid intake monitoring.

Main Methods:

  • Adaptive segmentation of continuous inertial data streams.
  • Utilizing a practical, off-the-shelf wrist-mounted sensor.
  • Leave-one-participant-out (LOPO) cross-validation for performance evaluation.

Main Results:

  • High precision (90.3%) and recall (91.0%) in detecting drinking episodes.
  • Demonstrated generalizability of the fluid intake detection method.
  • Successful recording of 561 drinking instances across 30 participants.

Conclusions:

  • The proposed method effectively and passively detects fluid intake gestures.
  • The approach shows strong generalizability, suitable for real-world health monitoring.
  • The released dataset will foster further research in automated fluid intake analysis.