Evaluating upper limb function after stroke using the free-living accelerometer data. (November 2020)

• Record Type: Journal Article
• Authors: Tang, Lin; Halloran, Shane; Shi, Jian Qing; Guan, Yu; Cao, Chunzheng; Eyre, Janet
• Is Part Of: Statistical methods in medical research. Volume 29:Number 11(2020)
• Page Start: 3249
• Page End: 3264
• Publication Date: 2020-11
• Subjects: Accelerometer data -- clustering -- Gaussian mixture model -- Gaussian process prior -- nonlinear mixed effects model -- stroke; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280220922259
• Languages: English
• ISSNs: 0962-2802

Abstract

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.