Enabling Eating Detection in a Free-living Environment : Integrative Engineering and Machine Learning Study
©Bo Zhang, Kaiwen Deng, Jie Shen, Lingrui Cai, Bohdana Ratitch, Haoda Fu, Yuanfang Guan. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 01.03.2022..
BACKGROUND: Monitoring eating is central to the care of many conditions such as diabetes, eating disorders, heart diseases, and dementia. However, automatic tracking of eating in a free-living environment remains a challenge because of the lack of a mature system and large-scale, reliable training set.
OBJECTIVE: This study aims to fill in this gap by an integrative engineering and machine learning effort and conducting a large-scale study in terms of monitoring hours on wearable-based eating detection.
METHODS: This prospective, longitudinal, passively collected study, covering 3828 hours of records, was made possible by programming a digital system that streams diary, accelerometer, and gyroscope data from Apple Watches to iPhones and then transfers the data to the cloud.
RESULTS: On the basis of this data collection, we developed deep learning models leveraging spatial and time augmentation and inferring eating at an area under the curve (AUC) of 0.825 within 5 minutes in the general population. In addition, the longitudinal follow-up of the study design encouraged us to develop personalized models that detect eating behavior at an AUC of 0.872. When aggregated to individual meals, the AUC is 0.951. We then prospectively collected an independent validation cohort in a different season of the year and validated the robustness of the models (0.941 for meal-level aggregation).
CONCLUSIONS: The accuracy of this model and the data streaming platform promises immediate deployment for monitoring eating in applications such as diabetic integrative care.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
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| Enthalten in: |
Journal of medical Internet research - 24(2022), 3 vom: 01. März, Seite e27934 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Bo [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 31.03.2022 Date Revised 16.07.2022 published: Electronic Citation Status MEDLINE |
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| doi: |
10.2196/27934 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM337616043 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a ©Bo Zhang, Kaiwen Deng, Jie Shen, Lingrui Cai, Bohdana Ratitch, Haoda Fu, Yuanfang Guan. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 01.03.2022. | ||
| 520 | |a BACKGROUND: Monitoring eating is central to the care of many conditions such as diabetes, eating disorders, heart diseases, and dementia. However, automatic tracking of eating in a free-living environment remains a challenge because of the lack of a mature system and large-scale, reliable training set | ||
| 520 | |a OBJECTIVE: This study aims to fill in this gap by an integrative engineering and machine learning effort and conducting a large-scale study in terms of monitoring hours on wearable-based eating detection | ||
| 520 | |a METHODS: This prospective, longitudinal, passively collected study, covering 3828 hours of records, was made possible by programming a digital system that streams diary, accelerometer, and gyroscope data from Apple Watches to iPhones and then transfers the data to the cloud | ||
| 520 | |a RESULTS: On the basis of this data collection, we developed deep learning models leveraging spatial and time augmentation and inferring eating at an area under the curve (AUC) of 0.825 within 5 minutes in the general population. In addition, the longitudinal follow-up of the study design encouraged us to develop personalized models that detect eating behavior at an AUC of 0.872. When aggregated to individual meals, the AUC is 0.951. We then prospectively collected an independent validation cohort in a different season of the year and validated the robustness of the models (0.941 for meal-level aggregation) | ||
| 520 | |a CONCLUSIONS: The accuracy of this model and the data streaming platform promises immediate deployment for monitoring eating in applications such as diabetic integrative care | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a digital watch | |
| 650 | 4 | |a eating | |
| 700 | 1 | |a Deng, Kaiwen |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Cai, Lingrui |e verfasserin |4 aut | |
| 700 | 1 | |a Ratitch, Bohdana |e verfasserin |4 aut | |
| 700 | 1 | |a Fu, Haoda |e verfasserin |4 aut | |
| 700 | 1 | |a Guan, Yuanfang |e verfasserin |4 aut | |
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