Inferring Meal Eating Activities in Real World Settings from Ambient Sounds: A Feasibility Study.
IUI'15: IUI'15 20th International Conference on Intelligent User Interfaces Atlanta Georgia USA March, 2015(2015)
摘要
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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关键词
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
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