Automated Quality Control for Sensor Based Symptom Measurement Performed Outside the Lab
Name
sensors-18-01215.pdf
Size
2.09 MB
Format
Adobe PDF
Checksum (MD5)
87b5800e30cfc1f892c99b7288186907
Author(s)
Badawy, Reham
Raykov, Yordan P.
Evers, Luc J. W.
Bloem, Bastiaan R.
Faber, Marjan J.
Zhan, Andong
Claes, Kasper
Raykov, Yordan
Evers, Luc
Bloem, Bastiaan
Date Issued
April 2018
Journal
Sensors
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Citation
Badawy, Reham et al. "Automated Quality Control for Sensor Based Symptom Measurement Performed Outside the Lab." Sensors 18, 4 (April 2018): 1215 © 2018 The Authors
Version
Final published version
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. Keywords: Bayesian nonparametrics; clinimetric tests; Parkinson’s disease; pattern recognition; quality control; remote monitoring; segmentation; wearable sensors
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/s18041215
