Publications

@inproceedings{2015-Thomaz-IMEARWSFASFS,

title = {Inferring Meal Eating Activities in Real World Settings from Ambient Sounds: A Feasibility Study},

author = {Edison Thomaz and Cheng Zhang and Irfan Essa and Gregory Abowd},

url = {https://dl.acm.org/doi/10.1145/2678025.2701405},

doi = {10.1145/2678025.2701405},

year  = {2015},

date = {2015-05-01},

urldate = {2015-05-01},

booktitle = {ACM Conference on Intelligence User Interfaces (IUI)},

abstract = {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.},

keywords = {ACM, activity recognition, AI, awards, behavioral imaging, best paper award, computational health, IUI, machine learning},

pubstate = {published},

tppubtype = {inproceedings}

}