  The use of smartphone and wearable sensing technology for objective,
non-invasive and remote clinimetric testing of symptoms has considerable
potential. However, the clinimetric accuracy achievable with such technology is
highly reliant on separating the useful from irrelevant or confounded sensor
data. Monitoring patient symptoms using digital sensors outside of controlled,
clinical lab settings creates a variety of practical challenges, such as
unavoidable and unexpected user behaviours. These behaviours 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 can affect the accuracy of the subsequent data analysis and
scientific conclusions. At the same time, curating sensor data by hand after
the collection process is inherently subjective, laborious and error-prone. 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 which are sufficiently reliable for further analysis. Algorithms
which are special cases of this framework for different sensor data types (e.g.
accelerometer, digital audio) detect the extent to which the sensor data
adheres to the assumptions of the test protocol for a variety of clinimetric
tests. The approach is general enough to be applied to a large set of
clinimetric tests and we demonstrate its performance on walking, balance and
voice smartphone-based tests, designed to monitor the symptoms of Parkinson's
disease.
