Physical activity based classification of serious mental illness group participants in the UK Biobank using ensemble dense neural networks

Tahmina Zebin, Niels Peek, Alexander J. Casson

Research output: Contribution to conferencePaperpeer-review

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Abstract

Serious Mental Illnesses (SMIs) including schizophrenia and bipolar disorder are long term conditions which place major burdens on health and social care services. Locomotor activity is altered in many cases of SMI, and so in the long term wearable activity trackers could potentially aid in the early detection of SMI relapse, allowing early and targeted intervention. To move towards this goal, in this paper we use accelerometer activity tracking data collected from the UK Biobank to classify people as being either in a self-reported SMI group or an age and gender matched control group. Using an ensemble dense neural network algorithm we exploited hourly and average derived features from the wearable activity data and the created model obtained an accuracy of 91.3%.
Original languageEnglish
Number of pages4
DOIs
Publication statusPublished - 7 Oct 2019
EventInternational Conference of the IEEE Engineering in Medicine and Biology Society - City Cube Berlin, Germany
Duration: 23 Jul 201927 Jul 2019
Conference number: 41
https://embc.embs.org/2019/

Conference

ConferenceInternational Conference of the IEEE Engineering in Medicine and Biology Society
Abbreviated titleEMBC
Country/TerritoryGermany
Period23/07/1927/07/19
Internet address

Keywords

  • Serious Mental Illnesses
  • machine learning
  • UK biobank

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