Towards a generalizable method for detecting fluid intake with wrist-mounted sensors and adaptive segmentation

Keum San Chun, Necole Streeper, Ashley B. Sanders, David E. Conroy, Rebecca Adaimi, Edison Thomaz

Research output: Contribution to conferencePaper

Abstract

Over the last decade, advances in mobile technologies have enabled the development of intelligent systems that attempt to recognize and model a variety of health-related human behaviors. While automated dietary monitoring based on passive sensors has been an area of increasing research activity for many years, much less attention has been given to tracking fluid intake. In this work, we apply an adaptive segmentation technique on a continuous stream of inertial data captured with a practical, off-the-shelf wrist-mounted device to detect fluid intake gestures passively. We evaluated our approach in a study with 30 participants where 561 drinking instances were recorded. Using a leave-one-participant-out (LOPO), we were able to detect drinking episodes with 90.3% precision and 91.0% recall, demonstrating the generalizability of our approach. In addition to our proposed method, we also contribute an anonymized and labeled dataset of drinking and non-drinking gestures to encourage further work in the field.

Original languageEnglish (US)
Pages80-85
Number of pages6
DOIs
StatePublished - Jan 1 2019
Event24th ACM International Conference on Intelligent User Interfaces, IUI 2019 - Marina del Ray, United States
Duration: Mar 17 2019Mar 20 2019

Conference

Conference24th ACM International Conference on Intelligent User Interfaces, IUI 2019
CountryUnited States
CityMarina del Ray
Period3/17/193/20/19

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All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction

Cite this

Chun, K. S., Streeper, N., Sanders, A. B., Conroy, D. E., Adaimi, R., & Thomaz, E. (2019). Towards a generalizable method for detecting fluid intake with wrist-mounted sensors and adaptive segmentation. 80-85. Paper presented at 24th ACM International Conference on Intelligent User Interfaces, IUI 2019, Marina del Ray, United States. https://doi.org/10.1145/3301275.3302315