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.

翻译：目前,自动抑郁症筛查和诊断是一个高度相关的问题,传统抑郁症检测方法存在一些局限性,即高度依赖临床医生和有偏见的自我报告;近年来,研究表明,利用用户通过可磨损装置收集的被动数据进行机器学习(ML)方法有很大潜力;然而,ML是数据饥饿。特别是在保健领域,初级数据收集具有挑战性。在这项工作中,我们提出了一个基于转移学习的方法,即从受过二级数据集培训的模型中进行转移学习,以便根据用户行为学数据实时部署抑郁症筛查工具。这一方法使机器学习建模,即使使用有限的初级数据样本也是如此。在初级数据集上实施的经修改的留置一流验证方法得出了0.96的平均值,在每次循环中,一个主数据集的数据被留作测试。</s>