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The master projects outlined below are a part of the research project INtroducing personalized TReatment Of Mental health problems using Adaptive Technology (INTROMAT) financed by the Research Council of Norway, 2016-2022, as one of three IKTPLUSS lighthouse projects, see (in Norwegian):
The main objective of the project is to increase access to mental health services for common mental health problems by developing technology which can guide patients with limited or no therapist support through a treatment process adapted to each user.
The specific goal of these Master projects is to contribute to developing a patient monitoring and support system that consists of collecting data from the patient for modelling behaviour and emotional state, to be used for prediction and adaptation of the treatment. The work would be building on the state-of-the-art knowledge in sensors, apps, behavioral models and machine learning.
Deep Learning techniques have in recent years allowed the automated learning of prediction abilities, such as predicting future observations in traffic with a dashboard camera, or imagining future scenarios in video games. Typically, these algorithms work by seeing many examples of videos and learn to predict how new videos would continue. Unlike these systems, when we humans make and use predictions, we don't imagine a "video" of future images, but rather make high-level guesses or estimates about the state of our surroundings. For instance, I may predict that my coffee cup would break if I throw it in the floor, but I would not imagine a full "picture" of the broken cup, including predicting exactly how many pieces it breaks into.