A multimodal approach for event detection from lifelogs
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- Master theses 
This paper analyzes how personal lifelog data which contains biometric, visual, activity data, can be leveraged to detect points in time where the individual is partaking in an eating activity. To answer this question, three artificial neural network models were introduced. Firstly, a image object detection model trained to detect eating related objects using the YOLO framework. Secondly, a feed-forward neural network (FANN) and a Long-Short-Term-Memory (LSTM) neural network model which attempts to detect ‘eating moments’ in the lifelog data. The results show promise, with F1-score and AUC score of 0.489 and 0.796 for the FANN model, and F1-score of 0.74 and AUC score of 0.835 respectively. However, there are clear rooms for improvement on all models. The models and methods introduced can help individuals monitor their nutrition habits so they are empowered to make healthy lifestyle decisions. Additionally, several methods for streamlining event detection in lifelog data are introduced.