|Authors||E. Garcia-Ceja, M. Riegler, P. Jakobsen, J. Tørresen, T. Nordgreen, K. J. Oedegaard and O. B. Fasmer|
|Editors||P. Cesar, M. Zink and N. Murray|
|Afilliation||Communication Systems, Machine Learning|
|Publication Type||Proceedings, refereed|
|Year of Publication||2018|
|Conference Name||the 9th ACM Multimedia Systems ConferenceProceedings of the 9th ACM Multimedia Systems Conference on - MMSys '18|
|Place Published||Amsterdam, NetherlandsNew York, New York, USA|
Wearable sensors measuring different parts of people's activity are a common technology nowadays. In research, data collected using these devices also draws attention. Nevertheless, datasets containing sensor data in the field of medicine are rare. Often, data is non-public and only results are published. This makes it hard for other researchers to reproduce and compare results or even collaborate. In this paper we present a unique dataset containing sensor data collected from patients suffering from depression. The dataset contains motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls. For each patient we provide sensor data over several days of continuous measuring and also some demographic data. The severity of the patients' depressive state was labeled using ratings done by medical experts on the Montgomery-Asberg Depression Rating Scale (MADRS). In this respect, the here presented dataset can be useful to explore and understand the association between depression and motor activity better. By making this dataset available, we invite and enable interested researchers the possibility to tackle this challenging and important societal problem.