1-Page Extended Abstract (Poster)
18:15 - 20:15 | Mon 5 Mar | Caribbean ABC | MoPO
We spot eating using data recorded with Electromyogram-monitoring eyeglasses in free-living. We apply overlapping and non-overlapping sliding windows to the data and use one-class classification for spotting. We evaluate the spotting timing errors at eating start and eating end. Results show that best F1-score may not provide best timing performance. Timing errors for small windows, e.g. 5 s, were larger than 100 s.
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