Conference Proceeding Article
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).
False positive and false negatives, Food imageIn-buildings, Inertial sensor, Real-world
Intelligent Systems and Decision Analytics
WPA '17 Proceedings of the 4th International on Workshop on Physical Analytics, New York, USA, 2017 June 19
Association for Computing Machinery, Inc
City or Country
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. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3719
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