Paper Info
Title
Annapurna: An automated smartwatch-based eating detection and food journaling system
Abstract
AbstractAbstractMaintaining a food journal can allow an individual to monitor eating habits, including unhealthy eating sessions, food items causing severe reactions, or portion size related information. However, manually maintaining a food journal can be burdensome. In this paper, we explore the vision of a pervasive, automated, completely unobtrusive, food journaling system using a commodity smartwatch. We present a prototype system — Annapurna— which is composed of three key components: (a) a smartwatch-based gesture recognizer that can robustly identify eating-specific gestures occurring anywhere, (b) a smartwatch-based image captor that obtains a small set of relevant images (containing views of the food being consumed) with a low energy overhead, and (c) a server-based image filtering engine that removes irrelevant uploaded images. Through lessons learnt from multiple user studies, we refine Annapurna progressively and show that our vision is indeed achievable: Annapurna can identify eating episodes and capture food images (involving a very wide diversity in food content, eating styles and environments) in over 95% of all free-living eating episodes.
Year
DOI
Venue
2020
10.1016/j.pmcj.2020.101259
Periodicals
Keywords
DocType
Volume
Wearable Sensing,Mobile computing,Food journaling,Automated eating tracking system,IMU and camera data processing
Journal
68
Issue
ISSN
Citations 
C
1574-1192
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Sougata Sen1215.69
Subbaraju, V.215715.53
Archan Misra31688149.25
Rajesh Krishna Balan4105680.30
Youngki Lee583270.33