The master’s thesis proposes an approach of predicting meals and bites. The data was obtained in the wild, in an experiment in which 12 participants measured the movements of their wrists via a gyroscope and an accelerometer installed in a smartwatch. The data includes 169 meals and has the total duration of 570 hours. The main part of the algorithm for the predictions of meals and bites is based on deep
learning. The results obtained with a cross-validation show that the F1 score of the predictions of meals equals 0.91, while the F1 score of the predictions of bites equals 0.83. This shows that the algorithm relatively successfully learned to recognize meals in the wild, while the predictions of bites proved to be more challenging.
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