AuthorsJ. I. Frogner, F. M. Noori, P. Halvorsen, S. Hicks, E. Garcia-Ceja, J. Torresen and M. Riegler
EditorsS. Boll, J. Lee, J. Meyer, N. Nag and N. E. O'Connor
TitleOne-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression
AfilliationMachine Learning
Project(s)Department of Holistic Systems
Publication TypeProceedings, refereed
Year of Publication2019
Conference NameProceedings of the 4th International Workshop on Multimedia for Personal Health & Health Care - HealthMedia '19
Date Published10/2019
PublisherACM Press
Place PublishedNew York, NY, USA
ISBN Number9781450369145

Nowadays, it has become possible to measure different human activities using wearable devices. Besides measuring the number of daily steps or calories burned, these datasets have much more potential since different activity levels are also collected. Such data would be helpful in the field of psychology because it can relate to various mental health issues such as changes in mood and stress. In this paper, we present a machine learning approach to detect depression using a dataset with motor activity recordings of one group of people with depression and one group without, i.e., the condition group includes 23 unipolar and bipolar persons, and the control group includes 32 persons without depression. We use convolutional neural networks to classify the depressed and nondepressed patients. Moreover, different levels of depression were classified. Finally, we trained a model that predicts MontgomeryÅsberg Depression Rating Scale scores. We achieved an average F1-score of 0.70 for detecting the control and condition groups. The mean squared error for score prediction was approximately 4.0.


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