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Sensors 2018, 18(4), 1215; doi:10.3390/s18041215

Automated Quality Control for Sensor Based Symptom Measurement Performed Outside the Lab

1
School of Engineering and Applied Sciences, Aston University, Birmingham B4 7ET, UK
2
Institute for Computing and Information Sciences, Radboud University, 6525 EC Nijmegen, The Netherlands
3
Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, 6525 HR Nijmegen, The Netherlands
4
Radboud Institute for Health Sciences, Scientific Center for Quality of Healthcare, Radboud University Medical Center, 6525 EZ Nijmegen, The Netherlands
5
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
6
UCB Biopharma, B–1070 Brussels, Belgium
7
Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 24 February 2018 / Revised: 31 March 2018 / Accepted: 9 April 2018 / Published: 16 April 2018
(This article belongs to the Special Issue Sensors for Health Monitoring and Disease Diagnosis)
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Abstract

The use of wearable sensing technology for objective, non-invasive and remote clinimetric testing of symptoms has considerable potential. However, the accuracy achievable with such technology is highly reliant on separating the useful from irrelevant sensor data. Monitoring patient symptoms using digital sensors outside of controlled, clinical lab settings creates a variety of practical challenges, such as recording unexpected user behaviors. These behaviors often violate the assumptions of clinimetric testing protocols, where these protocols are designed to probe for specific symptoms. Such violations are frequent outside the lab and affect the accuracy of the subsequent data analysis and scientific conclusions. To address these problems, we report on a unified algorithmic framework for automated sensor data quality control, which can identify those parts of the sensor data that are sufficiently reliable for further analysis. Combining both parametric and nonparametric signal processing and machine learning techniques, we demonstrate that across 100 subjects and 300 clinimetric tests from three different types of behavioral clinimetric protocols, the system shows an average segmentation accuracy of around 90%. By extracting reliable sensor data, it is possible to strip the data of confounding factors in the environment that may threaten reproducibility and replicability. View Full-Text
Keywords: Bayesian nonparametrics; clinimetric tests; Parkinson’s disease; pattern recognition; quality control; remote monitoring; segmentation; wearable sensors Bayesian nonparametrics; clinimetric tests; Parkinson’s disease; pattern recognition; quality control; remote monitoring; segmentation; wearable sensors
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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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Badawy, R.; Raykov, Y.P.; Evers, L.J.W.; Bloem, B.R.; Faber, M.J.; Zhan, A.; Claes, K.; Little, M.A. Automated Quality Control for Sensor Based Symptom Measurement Performed Outside the Lab. Sensors 2018, 18, 1215.

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