Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2017
Abstract
In this paper, we describe the progressive design of the gesture recognition module of an automated food journaling system - Annapurna. Annapurna runs on a smartwatch and utilises data from the inertial sensors to first identify eating gestures, and then captures food images which are presented to the user in the form of a food journal. We detail the lessons we learnt from multiple in-the-wild studies, and show how eating recognizer is refined to tackle challenges such as (i) high gestural diversity, and (ii) non-eating activities with similar gestural signatures. Annapurna is finally robust (identifying eating across a wide diversity in food content, eating styles and environments) and accurate (false-positive and false-negative rates of 6.5% and 3.3% respectively).
Keywords
False positive and false negatives, Food imageIn-buildings, Inertial sensor, Real-world
Discipline
Software Engineering
Publication
WPA '17 Proceedings of the 4th International on Workshop on Physical Analytics, New York, USA, 2017 June 19
First Page
7
Last Page
12
ISBN
9781450349581
Identifier
10.1145/3092305.3092306
Publisher
Association for Computing Machinery, Inc
City or Country
Niagara Falls
Citation
SEN, Sougata; SUBBARAJU, Vigneshwaran; MISRA, Archan; BALAN, Rajesh Krishna; and LEE, Youngki.
Experiences in building a real-world eating recogniser. (2017). WPA '17 Proceedings of the 4th International on Workshop on Physical Analytics, New York, USA, 2017 June 19. 7-12.
Available at: https://ink.library.smu.edu.sg/sis_research/3719
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org./10.1145/3092305.3092306