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Over the years, researchers have explored various approaches for capturing and monitoring the eating activity, one among which is via Wi-Fi channel state information (CSI). CSI-based approaches commonly rely on multi-antenna systems for the capturing and monitoring tasks. With the advent of low-cost, single-antenna IoT devices with CSI measuring capabilities, a question that arises is whether these inexpensive devices can monitor human activities? In this paper we present the SandDune system that demonstrates the possibility of monitoring one human activity–eating–using only inexpensive single-antenna Wi-Fi devices. SandDune is an infrastructure-based system that continuously monitors CSI information to detect the eating activity occurring in its vicinity. When it detects an eating activity, it scrutinizes the signals further to identify all hand-to-mouth eating gestures in the eating episode. We tested SandDune and observed that SandDune can distinguish eating from other activities with an F1-score of 85.54%. Furthermore, it can detect the number of hand-to-mouth gestures that occurred in the eating episode with an error of ±3 gestures. Overall, we believe that a SandDune-like system can enable low cost, unobtrusive eating activity detection and monitoring with potential use-cases in several health and well-being applications.
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Proximity-based active learning for eating moment recognition in wearable systems
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.
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- Award ID(s):
- 1932346
- PAR ID:
- 10209064
- Date Published:
- Journal Name:
- WearSys '20: Proceedings of the 6th ACM Workshop on Wearable Systems and Applications
- Page Range / eLocation ID:
- 7 to 12
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3396870.3400011
