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• W3127269508 abstract "Many works in the field of automated dietary monitoring (ADM) have analyzed small data sets consisting of <; 10 subjects and <; 20 meals. This is often the first step in researching new sensors or body positions for detecting consumption. Metrics tend to focus on within-meal accuracy by quantifying physiological event detection (bites, chews, swallows). As analysis shifts to larger datasets containing many days of data from everyday life and researchers build methods that can be used in everyday life, it becomes equally important to quantify the accuracy of how many meals are detected. In small data sets most meals can be detected at least partially. In larger datasets, some meals are missed and false positives occur. In this work we discuss the pros and cons of time-based metrics and episode level metrics. We demonstrate how class imbalance affects some of the commonly used time metrics, and discuss why episode level metrics need to be reported as the field transitions from small data sets to big data sets." @default.
• W3127269508 created "2021-02-15" @default.
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• W3127269508 date "2020-12-16" @default.
• W3127269508 modified "2023-10-18" @default.
• W3127269508 title "The Challenge of Metrics in Automated Dietary Monitoring as Analysis Transitions from Small Data to Big Data" @default.
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• W3127269508 doi "https://doi.org/10.1109/bibm49941.2020.9313465" @default.
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