Topic Models For Automated Motor Analysis In Schizophrenia Patients

Wearable devices fitted with various sensors are increasingly being used for the automatic and continuous tracking and monitoring of patients. Only first steps have been taken in the field of psychiatric care, where long term tracking of patient behavior holds the promise to help practitioners to better understand both individual patients, and the disorders in general. In this paper we use topic models for unsupervised analysis of movement activity of schizophrenia patients in a closed ward setting. Results demonstrate that features computed on the basis of this analysis differentially characterize interesting sub-populations of schizophrenia patients. Positive-signs schizophrenia sub-population was found to have high motor richness and low typicallity, while negative-signs patients had low motor richness and lower typicality. In addition we design a classifier which correctly classified up to 80% of the clinical sub-population (f-score=0.774) based on motor features.