IDEA: Instant detection of eating action using wrist-worn sensors in absence of user-specific model

Junghyo Lee, Prajwal Paudyal, Ayan Banerjee, Sandeep Gupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Eating activity monitoring using wearable sensors can potentially enable interventions based on eating speed for critical healthcare problems such as obesity or diabetes. We propose a novel methodology, IDEA that performs accurate eating action identification and provides feedback on eating speed. IDEA uses a single wristband with IMU sensors and functions without any manual intervention from the user. The F1 score for eating action identification was 0.92.

Original languageEnglish (US)
Title of host publicationUMAP 2018 - Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages371-372
Number of pages2
ISBN (Electronic)9781450355896
DOIs
StatePublished - Jul 3 2018
Event26th ACM International Conference on User Modeling, Adaptation and Personalization, UMAP 2018 - Singapore, Singapore
Duration: Jul 8 2018Jul 11 2018

Other

Other26th ACM International Conference on User Modeling, Adaptation and Personalization, UMAP 2018
CountrySingapore
CitySingapore
Period7/8/187/11/18

Keywords

  • Diet Monitoring
  • Gesture
  • User Adaptive Modeling
  • Wearable

ASJC Scopus subject areas

  • Software

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    Lee, J., Paudyal, P., Banerjee, A., & Gupta, S. (2018). IDEA: Instant detection of eating action using wrist-worn sensors in absence of user-specific model. In UMAP 2018 - Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (pp. 371-372). Association for Computing Machinery, Inc. https://doi.org/10.1145/3209219.3209265