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Sensors 2017, 17(9), 2113; https://doi.org/10.3390/s17092113

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

1
The Hamlyn Centre, Imperial College London, London SW7 2AZ, UK
2
School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
3
Clinical Innovation & Digital Platforms; Projects, Clinical Platforms & Sciences, GSK, Stevenage SG1 2NY, UK
4
Emerging Platforms, Platform Technology & Science, GSK, Stevenage SG1 2NY, UK
5
Tessella, Altran’s World Class Center for Analytics, Stevenage SG1 3QP, UK
*
Author to whom correspondence should be addressed.
Received: 20 July 2017 / Revised: 24 August 2017 / Accepted: 24 August 2017 / Published: 14 September 2017
(This article belongs to the Special Issue Advances in Body Sensor Networks: Sensors, Systems, and Applications)
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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. View Full-Text
Keywords: rheumatoid arthritis; actigraphy; continuous monitoring; machine learning rheumatoid arthritis; actigraphy; continuous monitoring; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Andreu-Perez, J.; Garcia-Gancedo, L.; McKinnell, J.; Van der Drift, A.; Powell, A.; Hamy, V.; Keller, T.; Yang, G.-Z. Developing Fine-Grained Actigraphies for Rheumatoid Arthritis Patients from a Single Accelerometer Using Machine Learning. Sensors 2017, 17, 2113.

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