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PSYKOSE: A motor activity database of patients with schizophrenia

Abstract

Using sensor data from devices such as smart-watches or mobile phones is very popular in both computer science and medical research. Such movement data can predict certain health states or performance outcomes. However, in order to increase reliability and replication of the research it is important to share data and results openly. In medicine, this is often difficult due to legal restrictions or to the fact that data collected from clinical trials is seen as very valuable and something that should be kept "in-house". In this paper, we therefore present PSYKOSE, a publicly shared dataset consisting of motor activity data collected from body sensors. The dataset contains data collected from patients with schizophrenia. Schizophrenia is a severe mental disorder characterized by psychotic symptoms like hallucinations and delusions, as well as symptoms of cognitive dysfunction and diminished motivation. In total, we have data from 22 patients with schizophrenia and 32 healthy control persons. For each person in the dataset, we provide sensor data collected over several days in a row. In addition to the sensor data, we also provide some demographic data and medical assessments during the observation period. The patients were assessed by medical experts from Haukeland University hospital. In addition to the data, we provide a baseline analysis and possible use-cases of the dataset.

Category

Academic article

Language

English

Author(s)

  • Petter Jakobsen
  • Enrique Garcia-Ceja
  • Lena Antonsen Stabell
  • Ketil Joachim Ødegaard
  • Jan Øystein Berle
  • Vajira Lasantha Bandara Thambawita
  • Steven Hicks
  • Pål Halvorsen
  • Ole Bernt Fasmer
  • Michael Riegler

Affiliation

  • SINTEF Digital / Sustainable Communication Technologies
  • University of Bergen
  • Bergen Hospital Trust - Haukeland University Hospital
  • Simula Metropolitan Center for Digital Engineering

Year

2020

Published in

IEEE International Symposium on Computer-Based Medical Systems

Page(s)

303 - 308

View this publication at Norwegian Research Information Repository