  Video recording is a widely used method for documenting infant and child
behaviours in research and clinical practice. Video data has rarely been shared
due to ethical concerns of confidentiality, although the need of shared
large-scaled datasets remains increasing. This demand is even more imperative
when data-driven computer-based approaches are involved, such as screening
tools to complement clinical assessments. To share data while abiding by
privacy protection rules, a critical question arises whether efforts at data
de-identification reduce data utility? We addressed this question by showcasing
the Prechtl's general movements assessment (GMA), an established and globally
practised video-based diagnostic tool in early infancy for detecting
neurological deficits, such as cerebral palsy. To date, no shared
expert-annotated large data repositories for infant movement analyses exist.
Such datasets would massively benefit training and recalibration of human
assessors and the development of computer-based approaches. In the current
study, sequences from a prospective longitudinal infant cohort with a total of
19451 available general movements video snippets were randomly selected for
human clinical reasoning and computer-based analysis. We demonstrated for the
first time that pseudonymisation by face-blurring video recordings is a viable
approach. The video redaction did not affect classification accuracy for either
human assessors or computer vision methods, suggesting an adequate and
easy-to-apply solution for sharing movement video data. We call for further
explorations into efficient and privacy rule-conforming approaches for
deidentifying video data in scientific and clinical fields beyond movement
assessments. These approaches shall enable sharing and merging stand-alone
video datasets into large data pools to advance science and public health.
