TY - JOUR
AU1 - Jiang, Zhihan
AU2 - Zhao, Running
AU3 - Lin, Lin
AU4 - Yu, Yue
AU5 - Chen, Handi
AU6 - Zhang, Xinchen
AU7 - Xu, Xuhai
AU8 - Wang, Yifang
AU9 - Ma, Xiaojuan
AU1 - Ngai, Edith C. H.
AB - Abstract:Growing awareness of wellness has prompted people to consider whether their dietary patterns align with their health and fitness goals. In response, researchers have introduced various wearable dietary monitoring systems and dietary assessment approaches. However, these solutions are either limited to identifying foods with simple ingredients or insufficient in providing analysis of individual dietary behaviors with domain-specific knowledge. In this paper, we present DietGlance, a system that automatically monitors dietary in daily routines and delivers personalized analysis from knowledge sources. DietGlance first detects ingestive episodes from multimodal inputs using eyeglasses, capturing privacy-preserving meal images of various dishes being consumed. Based on the inferred food items and consumed quantities from these images, DietGlance further provides nutritional analysis and personalized dietary suggestions, empowered by the retrieval augmentation generation module on a reliable nutrition library. A short-term user study (N=33) and a four-week longitudinal study (N=16) demonstrate the usability and effectiveness of DietGlance, offering insights and implications for future AI-assisted dietary monitoring and personalized healthcare intervention systems using eyewear.
TI - DietGlance: Dietary Monitoring and Personalized Analysis at a Glance with Knowledge-Empowered AI Assistant
JF - Computing Research Repository
DO - 10.48550/arxiv.2502.01317
DA - 2025-03-17
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/dietglance-dietary-monitoring-and-personalized-analysis-at-a-glance-6R2aDEmhHY
VL - 2025
IS - 2502
DP - DeepDyve
ER -