Depression is one of the most prevalent health problems in Europe. The current standard for depression diagnosis is still based on subjective clinical rating scales derived from methods that were developed in the early 1960s. In this project, the candidate will exploit the potential of depression monitoring from the continuous measurement of physiological, behavioral and vocal characteristics of an individual. He will research algorithms based on the combination of aforementioned metrics which enable accurate and objective diagnosis.
The fellow will implement these algorithms on a lightweight mental health monitoring platform based on a smartphone and body sensor network which can be used during daily life activities. He will use novel methods of Affective Computing thus maintaining and enhancing the candidate’s position at the forefront of advances in this field.
This project will provide a new platform that helps to expand mental healthcare into the homecare domain where existing treatment methodologies have not yet demonstrated substantial effectiveness as compared to in-ward settings. Also the ability of long-term monitoring of mental health has the potential to help in the pre-clinical assessment, early diagnosis, and treatment prediction of other than depression severe disorders like bipolar disorder, where a patient exhibits extreme swings in mood related to manic and depressive episodes.
This fellowship will facilitate the transfer of the fellow back to academia from an industry-based research environment. The skills acquired during the fellowship and the experience and knowledge gained in industrial research labs would allow the fellow to apply for the tenure position in a European university which is an ultimate goal of his career.
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