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

Taewoong Park; Talha Ibn Mahmud; Junsang Lee; Seokkyoon Hong; Jae Young Park; Yuhyun Ji; Taehoo Chang; Jonghun Yi; Min Ku Kim; Rita R Patel; Dong Rip Kim; Young L Kim; Hyowon Lee; Fengqing Zhu; Chi Hwan Lee

doi:10.1093/pnasnexus/pgae156

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

PNAS Nexus. 2024 May 7;3(5):pgae156. doi: 10.1093/pnasnexus/pgae156. eCollection 2024 May.

Authors

Taewoong Park¹, Talha Ibn Mahmud², Junsang Lee¹, Seokkyoon Hong¹, Jae Young Park¹, Yuhyun Ji¹, Taehoo Chang³, Jonghun Yi⁴, Min Ku Kim¹, Rita R Patel⁵, Dong Rip Kim⁴, Young L Kim¹, Hyowon Lee^{1

6}, Fengqing Zhu², Chi Hwan Lee^{1

2

3

6

7}

Affiliations

¹ Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA.
² Elmore Family School of Electrical and Computer Engineering, West Lafayette, IN 47907, USA.
³ School of Materials Engineering, Purdue University, West Lafayette, IN 47907, USA.
⁴ School of Mechanical Engineering, Hanyang University, Seoul 04763, Republic of Korea.
⁵ Department of Speech, Language and Hearing Sciences, Indiana University, Bloomington, IN 47408, USA.
⁶ Center for Implantable Devices, Purdue University, West Lafayette, IN 47907, USA.
⁷ School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.

Abstract

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.

Keywords: bioelectronics; dietary intake; machine learning; smart neckband; wearable.