A machine-learning-enabled smart neckband for monitoring dietary intake.
Taewoong ParkTalha Ibn MahmudJunsang LeeSeokkyoon HongJae Young ParkYuhyun JiTaehoo ChangJonghun YiMin Ku KimRita R PatelDong Rip KimYoung L KimHyowon Hugh LeeFengqing ZhuChi-Hwan LeePublished in: PNAS nexus (2024)
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.
Keyphrases
- machine learning
- healthcare
- public health
- type diabetes
- weight loss
- resting state
- artificial intelligence
- mental health
- physical activity
- deep learning
- health information
- functional connectivity
- white matter
- metabolic syndrome
- big data
- weight gain
- insulin resistance
- cardiovascular disease
- human health
- climate change
- heart rate
- low cost
- health promotion
- glycemic control
- skeletal muscle
- social media