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Developing Fine-Grained Actigraphies for Rheumatoid Arthritis Patients from a Single Accelerometer Using Machine Learning.

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Title: Developing Fine-Grained Actigraphies for Rheumatoid Arthritis Patients from a Single Accelerometer Using Machine Learning.
Authors: Andreu-Perez, J
Garcia-Gancedo, L
McKinnell, J
Van der Drift, A
Powell, A
Hamy, V
Keller, T
Yang, G-Z
Item Type: Journal Article
Abstract: In addition to routine clinical examination, unobtrusive and physical monitoring of Rheumatoid Arthritis (RA) patients provides an important source of information to enable understanding the impact of the disease on quality of life. Besides an increase in sedentary behaviour, pain in RA can negatively impact simple physical activities such as getting out of bed and standing up from a chair. The objective of this work is to develop a method that can generate fine-grained actigraphies to capture the impact of the disease on the daily activities of patients. A processing methodology is presented to automatically tag activity accelerometer data from a cohort of moderate-to-severe RA patients. A study of procesing methods based on machine learning and deep learning is provided. Thirty subjects, 10 RA patients and 20 healthy control subjects, were recruited in the study. A single tri-axial accelerometer was attached to the position of the fifth lumbar vertebra (L5) of each subject with a tag prediction granularity of 3 s. The proposed method is capable of handling unbalanced datasets from tagged data while accounting for long-duration activities such as sitting and lying, as well as short transitions such as sit-to-stand or lying-to-sit. The methodology also includes a novel mechanism for automatically applying a threshold to predictions by their confidence levels, in addition to a logical filter to correct for infeasible sequences of activities. Performance tests showed that the method was able to achieve around 95% accuracy and 81% F-score. The produced actigraphies can be helpful to generate objective RA disease-specific markers of patient mobility in-between clinical site visits.
Issue Date: 14-Sep-2017
Date of Acceptance: 24-Aug-2017
URI: http://hdl.handle.net/10044/1/50957
DOI: https://dx.doi.org/10.3390/s17092113
ISSN: 1424-2818
Publisher: MDPI AG
Journal / Book Title: Sensors
Volume: 17
Issue: 9
Copyright Statement: © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Keywords: actigraphy
continuous monitoring
machine learning
rheumatoid arthritis
0301 Analytical Chemistry
0906 Electrical And Electronic Engineering
Analytical Chemistry
Publication Status: Published online
Article Number: 2113
Appears in Collections:Faculty of Engineering
Division of Surgery
Computing
Faculty of Medicine



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