Algorithm Based on One Monocular Video Delivers Highly Valid and Reliable Gait Parameters
This enables cost-effective and accessible gait analysis for health care, sports, and rehabilitation, using only a smartphone camera, though it is incremental as it applies existing methods to a new application.
The researchers tackled the problem of measuring gait parameters by developing a system that uses convolutional neural networks to extract 3D skeleton joints from monocular frontal-view videos, achieving excellent validity and test-retest repeatability comparable to the GAITRite system for parameters like gait speed, cadence, step length, and step time.
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.