Automated depression screening and diagnosis is a highly relevant problem
today. There are a number of limitations of the traditional depression
detection methods, namely, high dependence on clinicians and biased
self-reporting. In recent years, research has suggested strong potential in
machine learning (ML) based methods that make use of the user's passive data
collected via wearable devices. However, ML is data hungry. Especially in the
healthcare domain primary data collection is challenging. In this work, we
present an approach based on transfer learning, from a model trained on a
secondary dataset, for the real time deployment of the depression screening
tool based on the actigraphy data of users. This approach enables machine
learning modelling even with limited primary data samples. A modified version
of leave one out cross validation approach performed on the primary set
resulted in mean accuracy of 0.96, where in each iteration one subject's data
from the primary set was set aside for testing.Comment: 5 pages, 4 figures, conference, to be published in UKSIM2