@inproceedings{07d4ce9a8e8948dcaf61bb734231ad69,
title = "IDEA: Instant detection of eating action using wrist-worn sensors in absence of user-specific model",
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.",
keywords = "Diet Monitoring, Gesture, User Adaptive Modeling, Wearable",
author = "Junghyo Lee and Prajwal Paudyal and Ayan Banerjee and Sandeep Gupta",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 26th ACM International Conference on User Modeling, Adaptation and Personalization, UMAP 2018 ; Conference date: 08-07-2018 Through 11-07-2018",
year = "2018",
month = jul,
day = "3",
doi = "10.1145/3209219.3209265",
language = "English (US)",
series = "UMAP 2018 - Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization",
publisher = "Association for Computing Machinery, Inc",
pages = "371--372",
booktitle = "UMAP 2018 - Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization",
}