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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.
Highlights
Rheumatoid arthritis (RA) is a chronic systemic disease that typically affects adults between the ages of 30 and 60 [1], leading to a life disability in which patients describe increasing pain in several joints over time
In [22], a study with a cohort of 98 RA patients used an accelerometer sensor attached to the arm to determine an actigraphy based on the detection of four different levels of activity based on a scale related to energy expenditure
random forest (RF)-based methods can be trained with dimensionality reduced inputs from either deep learning (DL) or metric learning (Metric), and the same is applicable to the SVM-based classifiers
Summary
Rheumatoid arthritis (RA) is a chronic systemic disease that typically affects adults between the ages of 30 and 60 [1], leading to a life disability in which patients describe increasing pain in several joints over time. This disease has a prevalence of 0.24% that varies between 0.3% and 1% in the developed countries [2,3]. In [22], a study with a cohort of 98 RA patients used an accelerometer sensor attached to the arm to determine an actigraphy based on the detection of four different levels of activity (sedentary, very light, light and moderate) based on a scale related to energy expenditure.
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