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

TL;DR

This paper presents a novel gait assessment system using monocular video and neural networks, achieving validity and reliability comparable to high-end laboratory systems, enabling accessible gait analysis.

Contribution

The study introduces a cost-effective, easy-to-use gait measurement method based on monocular videos and deep learning, validated against a standard pressure-sensitive walkway.

Findings

High validity of gait parameters compared to GAITRite system

Excellent test-retest reliability matching laboratory standards

Potential for broad, accessible gait analysis applications

Abstract

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.