English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Despite its paramount importance for manifold use cases (e.g., in the healthcare industry, sports, rehabilitation and fitness assessment), sufficientlyvalid and reliable gait parameter measurement is still limited to high-techgait laboratories mostly. Here, we demonstrate the excellent validity andtest-retest repeatability of a novel gait assessment system which is built uponmodern convolutional neural networks to extract three-dimensional skeletonjoints from monocular frontal-view videos of walking humans. The validity studyis based on a comparison to the GAITRite pressure-sensitive walkway system. Allmeasured gait parameters (gait speed, cadence, step length and step time)showed excellent concurrent validity for multiple walk trials at normal andfast gait speeds. The test-retest-repeatability is on the same level as theGAITRite system. In conclusion, we are convinced that our results can pave theway for cost, space and operationally effective gait analysis in broadmainstream applications. Most sensor-based systems are costly, must be operatedby extensively trained personnel (e.g., motion capture systems) or - even ifnot quite as costly - still possess considerable complexity (e.g., wearablesensors). In contrast, a video sufficient for the assessment method presentedhere can be obtained by anyone, without much training, via a smartphone camera.

Algorithm Based on One Monocular Video Delivers Highly Valid and Reliable Gait Parameters