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

Reham Badawy, Yordan Raykov, Luc J.W. Evers, Bastiaan R. Bloem, Marjan J. Faber, Andong Zhan, Kasper Claes, Max A Little

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number1215
Issue number4
Publication statusPublished - 16 Apr 2018

Bibliographical note

© 2018 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 (


  • Bayesian nonparametrics
  • clinimetric tests
  • Parkinson’s disease
  • pattern recognition
  • quality control
  • remote monitoring
  • segmentation
  • wearable sensors


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