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

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 a

SUBMITTER: Badawy R 

PROVIDER: S-EPMC5948536 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature