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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Veröffentlicht in:	PNAS nexus 2024-05, Vol.3 (5), p.pgae156-pgae156
Hauptverfasser:	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:	Artikel
Sprache:	eng
Schlagworte:	Food habits Machine learning Methods Observations Patient monitoring Physical Sciences and Engineering Sensors
Online-Zugang:	Volltext
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