A machine-learning-enabled smart neckband for monitoring dietary intake

The increasing need for precise dietary monitoring across various health scenarios has led to innovations in wearable sensing technologies. However, continuously tracking food and fluid intake during daily activities can be complex. In this study, we present a machine-learning-powered smart neckband...

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Bibliographic Details
Published in:PNAS nexus 2024-05, Vol.3 (5), p.pgae156-pgae156
Main Authors: Park, Taewoong, Mahmud, Talha Ibn, Lee, Junsang, Hong, Seokkyoon, Park, Jae Young, Ji, Yuhyun, Chang, Taehoo, Yi, Jonghun, Kim, Min Ku, Patel, Rita R, Kim, Dong Rip, Kim, Young L, Lee, Hyowon, Zhu, Fengqing, Lee, Chi Hwan
Format: Article
Language:English
Subjects:
Food habits
Machine learning
Methods
Observations
Patient monitoring
Physical Sciences and Engineering
Sensors
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Summary:The increasing need for precise dietary monitoring across various health scenarios has led to innovations in wearable sensing technologies. However, continuously tracking food and fluid intake during daily activities can be complex. In this study, we present a machine-learning-powered smart neckband that features wireless connectivity and a comfortable, foldable design. Initially considered beneficial for managing conditions such as diabetes and obesity by facilitating dietary control, the device's utility extends beyond these applications. It has proved to be valuable for sports enthusiasts, individuals focused on diet control, and general health monitoring. Its wireless connectivity, ergonomic design, and advanced classification capabilities offer a promising solution for overcoming the limitations of traditional dietary tracking methods, highlighting its potential in personalized healthcare and wellness strategies.
ISSN:2752-6542
2752-6542
DOI:10.1093/pnasnexus/pgae156