This paper is in the following e-collection/theme issue:

Falls and Emergency Detection with Sensors and other Technologies (37) Sensors at Home and Domotics (59) Dementia and Cognitive Decline (502) Ubiquitous Health (uHealth) (125) Telehealth and Telemonitoring (1713)

Published on in Vol 5, No 3 (2022): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38211, first published .
An Unsupervised Data-Driven Anomaly Detection Approach for Adverse Health Conditions in People Living With Dementia: Cohort Study

An Unsupervised Data-Driven Anomaly Detection Approach for Adverse Health Conditions in People Living With Dementia: Cohort Study

An Unsupervised Data-Driven Anomaly Detection Approach for Adverse Health Conditions in People Living With Dementia: Cohort Study

Authors of this article:

Nivedita Bijlani1 Author Orcid Image ;   Ramin Nilforooshan2, 3, 4 Author Orcid Image ;   Samaneh Kouchaki1, 3 Author Orcid Image
Article Authors Cited by (11) Tweetations (2) Metrics

Nivedita Bijlani   1 , BEng, MS ;   Ramin Nilforooshan   2, 3, 4 , MD, MRCPsych ;   Samaneh Kouchaki   1, 3 , BSc, MSc, PhD

1 Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, United Kingdom

2 Surrey and Borders Partnership NHS Foundation Trust, Guildford, United Kingdom

3 Care Research and Technology Centre, UK Dementia Research Institute, Imperial College, London, United Kingdom

4 School of Psychology, University of Surrey, Guildford, United Kingdom

Corresponding Author:

  • Nivedita Bijlani, BEng, MS
  • Centre for Vision, Speech and Signal Processing
  • University of Surrey
  • 388 Stag Hill
  • Guildford, GU2 7XH
  • United Kingdom
  • Phone: 44 1483 300 800
  • Fax: 44 1483 300 803
  • Email: n.bijlani@surrey.ac.uk