Paper in ACM IUI15: “Inferring Meal Eating Activities in Real-World Settings from Ambient Sounds: A Feasibility Study”

Citation

  • E. Thomaz, C. Zhang, I. Essa, and G. D. Abowd (2015), “Inferring Meal Eating Activities in Real World Settings from Ambient Sounds: A Feasibility Study,” in ACM Conference on Intelligence User Interfaces (IUI), 2015. (Best Short Paper Award) [PDF] [DOI] [BIBTEX]
    @InProceedings{ 2015-Thomaz-IMEARWSFASFS,
    author  = {Edison Thomaz and Cheng Zhang and Irfan Essa and
    Gregory D. Abowd},
    awards  = {(Best Short Paper Award)},
    booktitle  = {{ACM Conference on Intelligence User Interfaces
    (IUI)}},
    doi = {10.1145/2678025.2701405},
    month = {May},
    pdf = {http://www.cc.gatech.edu/~irfan/p/2015-Thomaz-IMEARWSFASFS.pdf},
    title = {Inferring Meal Eating Activities in Real World
    Settings from Ambient Sounds: A Feasibility Study},
    year = {2015}
    }

Abstract

2015-04-IUI-Award

Dietary self-monitoring has been shown to be an effective method for weight-loss, but it remains an onerous task despite recent advances in food journaling systems. Semi-automated food journaling can reduce the effort of logging, but often requires that eating activities be detected automatically. In this work we describe results from a feasibility study conducted in-the-wild where eating activities were inferred from ambient sounds captured with a wrist-mounted device; twenty participants wore the device during one day for an average of 5 hours while performing normal everyday activities. Our system was able to identify meal eating with an F-score of 79.8% in a person-dependent evaluation, and with 86.6% accuracy in a person-independent evaluation. Our approach is intended to be practical, leveraging off-the-shelf devices with audio sensing capabilities in contrast to systems for automated dietary assessment based on specialized sensors.

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