title: Inferring Meal Eating Activities in Real World Settings from Ambient Sounds: A Feasibility Study.
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.
authors: Edison
authors: E
authors: Thomaz
authors: Cheng
authors: C
authors: Zhang
authors: Irfan
authors: I
authors: Essa
authors: Gregory D
authors: GD
authors: Abowd
keywords: Acoustic sensor; Activity recognition; Ambient sound; Automated dietary assessment; Dietary intake; Food journaling; H.5.m. Information Interfaces and Presentation (e.g. HCI): Miscellaneous; Machine learning; Sound classification