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Evaluating upper limb function after stroke using the free-living accelerometer data

Lookup NU author(s): Dr Lin Tang, Shane Halloran, Dr Jian Shi, Dr Yu GuanORCiD, Dr Chunzheng Cao, Professor Janet Eyre


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© The Author(s) 2020. Accelerometer devices are becoming efficient tools in clinical studies for automatically measuring the activities of daily living. Such data provides a time series describing activity level at every second and displays a subject’s activity pattern throughout a day. However, the analysis of such data is very challenging due to the large number of observations produced each second and the variability among subjects. The purpose of this study is to develop efficient statistical analysis techniques for predicting the recovery level of the upper limb function after stroke based on the free-living accelerometer data. We propose to use a Gaussian Mixture Model (GMM)-based method for clustering and extracting new features to capture the information contained in the raw data. A nonlinear mixed effects model with Gaussian Process prior for the random effects is developed as the predictive model for evaluating the recovery level of the upper limb function. Results of applying to the accelerometer data for patients after stroke are presented.

Publication metadata

Author(s): Tang L, Halloran S, Shi JQ, Guan Y, Cao C, Eyre J

Publication type: Article

Publication status: Published

Journal: Statistical Methods in Medical Research

Year: 2020

Volume: 29

Issue: 11

Pages: 3249-3264

Print publication date: 01/11/2020

Online publication date: 22/05/2020

Acceptance date: 02/04/2018

ISSN (print): 0962-2802

ISSN (electronic): 1477-0334

Publisher: Sage Publications Ltd


DOI: 10.1177/0962280220922259

PubMed id: 32441206


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