@inproceedings{d5618e46861040a3bf6e90ddea64e284,
title = "Proximity-based active learning for eating moment recognition in wearable systems",
abstract = "Detecting when eating occurs is an essential step toward automatic dietary monitoring, medication adherence assessment, and diet-related health interventions. Wearable technologies play a central role in designing unobtrusive diet monitoring solutions by leveraging machine learning algorithms that work on time-series sensor data to detect eating moments. While much research has been done on developing activity recognition and eating moment detection algorithms, the performance of the detection algorithms drops substantially when the model is utilized by a new user. To facilitate the development of personalized models, we propose PALS, Proximity-based Active Learning on Streaming data, a novel proximity-based model for recognizing eating gestures to significantly decrease the need for labeled data with new users. Our extensive analysis in both controlled and uncontrolled settings indicates F-score of PALS ranges from 22% to 39% for a budget that varies from 10 to 60 queries. Furthermore, compared to the state-of-the-art approaches, off-line PALS achieves up to 40% higher recall and 12% higher F-score in detecting eating gestures.",
keywords = "active learning, eating detection, machine learning, mobile health, wearable computing",
author = "Marjan Nourollahi and Rokni, {Seyed Ali} and Parastoo Alinia and Hassan Ghasemzadeh",
note = "Funding Information: This work was supported in part by the National Science Foundation, under grants CNS-1750679 and CNS-1932346. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations. Publisher Copyright: {\textcopyright} 2020 ACM.; 6th ACM Workshop on Wearable Systems and Applications, WearSys 2020, Part of MobiSys 2020 ; Conference date: 19-06-2020",
year = "2020",
month = jun,
day = "19",
doi = "10.1145/3396870.3400011",
language = "English (US)",
series = "WearSys 2020 - Proceedings of the 6th ACM Workshop on Wearable Systems and Applications, Part of MobiSys 2020",
publisher = "Association for Computing Machinery, Inc",
pages = "7--12",
booktitle = "WearSys 2020 - Proceedings of the 6th ACM Workshop on Wearable Systems and Applications, Part of MobiSys 2020",
}