WiEat: Fine-grained Device-free Eating Monitoring Leveraging Wi-Fi Signals
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ID: 282362
2020
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Abstract
Eating is a fundamental activity in people's daily life. Studies have shown
that many health-related problems such as obesity, diabetes and anemia are
closely associated with people's unhealthy eating habits (e.g., skipping meals,
eating irregularly and overeating). Traditional eating monitoring solutions
relying on self-reports remain an onerous task, while the recent trend
requiring users to wear expensive dedicated hardware is still invasive. To
overcome these limitations, in this paper, we develop a device-free eating
monitoring system using WiFi-enabled devices (e.g., smartphone or laptop). Our
system aims to automatically monitor users' eating activities through
identifying the fine-grained eating motions and detecting the chewing and
swallowing. In particular, our system extracts the fine-grained Channel State
Information (CSI) from WiFi signals to distinguish eating from non-eating
activities and further recognizing users' detailed eating motions with
different utensils (e.g., using a folk, knife, spoon or bare hands). Moreover,
the system has the capability of identifying chewing and swallowing through
detecting users' minute facial muscle movements based on the derived CSI
spectrogram. Such fine-grained eating monitoring results are beneficial to the
understanding of the user's eating behaviors and can be used to estimate food
intake types and amounts. Extensive experiments with 20 users over 1600-minute
eating show that the proposed system can recognize the user's eating motions
with up to 95% accuracy and estimate the chewing and swallowing amount with 10%
percentage error.
| Reference Key |
chen2020wieat
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|---|---|
| Authors | Chen Wang; Zhenzhe Lin; Yucheng Xie; Xiaonan Guo; Yanzhi Ren; Yingying Chen |
| Journal | arXiv |
| Year | 2020 |
| DOI |
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