Publication Type

Conference Proceeding Article

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

Research Areas

Intelligent Systems and Decision Analytics

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

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

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

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