  Despite its paramount importance for manifold use cases (e.g., in the health
care industry, sports, rehabilitation and fitness assessment), sufficiently
valid and reliable gait parameter measurement is still limited to high-tech
gait laboratories mostly. Here, we demonstrate the excellent validity and
test-retest repeatability of a novel gait assessment system which is built upon
modern convolutional neural networks to extract three-dimensional skeleton
joints from monocular frontal-view videos of walking humans. The validity study
is based on a comparison to the GAITRite pressure-sensitive walkway system. All
measured gait parameters (gait speed, cadence, step length and step time)
showed excellent concurrent validity for multiple walk trials at normal and
fast gait speeds. The test-retest-repeatability is on the same level as the
GAITRite system. In conclusion, we are convinced that our results can pave the
way for cost, space and operationally effective gait analysis in broad
mainstream applications. Most sensor-based systems are costly, must be operated
by extensively trained personnel (e.g., motion capture systems) or - even if
not quite as costly - still possess considerable complexity (e.g., wearable
sensors). In contrast, a video sufficient for the assessment method presented
here can be obtained by anyone, without much training, via a smartphone camera.
