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

Additional URL

http://doi.org./10.1145/3092305.3092306

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