Audio-based detection and evaluation of eating behavior using the smartwatch platform
Open accessAbstract
In recent years, smartwatches have emerged as a viable platform for a variety of medical and health-related applications. In addition to the benefits of a stable hardware platform, these devices have a significant advantage over other wrist-worn devices, in that user acceptance of watches is higher than other custom hardware solutions. In this paper, we describe signal-processing techniques for identification of chews and swallows using a smartwatch device׳s built-in microphone. Moreover, we conduct a survey to evaluate the potential of the smartwatch as a platform for monitoring nutrition. The focus of this paper is to analyze the overall applicability of a smartwatch-based system for food-intake monitoring. Evaluation results confirm the efficacy of our technique; classification was performed between apple and potato chip bites, water swallows, talking, and ambient noise, with an F-measure of 94.5% based on 250 collected samples.
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Eating behaviours are analysed using audio collected from smartwatches and earables.
“Eating behaviours are also analysed using audio collected from smartwatches and earables [47,51,85].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [85]
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References 19 · Cited by 85
- Authors
- Kalantarian, H., & Sarrafzadeh, M.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Kalantarian, Haik
- Sarrafzadeh, Majid
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2015-10
- Volume
- 65
- Pages
- 1-9
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W1934290066
- Cited by
- 85
- References
- 19
- Citations by year
- 2025: 5 · 2024: 8 · 2023: 9 · 2022: 9 · 2021: 8 · 2020: 7 · 2019: 6 · 2018: 11 · 2017: 7 · 2016: 15
- FWCI
- 9.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computers in Biology and Medicine (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (1312310)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- smartwatches, dietary habits, dietary monitoring, nutritional monitoring, user acceptance
- MeSH terms
- Acoustics, Eating, Feeding Behavior, Humans, Monitoring, Physiologic, Computers, Handheld
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