基于单目视频的算法提供高度有效且可靠的步态参数
Algorithm Based on One Monocular Video Delivers Highly Valid and Reliable Gait Parameters
AI总结：
本文提出一种基于卷积神经网络从单目正面视频提取三维骨骼关节的步态评估系统，经验证其步态参数有效性和重测信度与GAITRite系统相当，且成本低、操作简便，可推动步态分析在主流场景中的广泛应用。
摘要：
尽管步态参数测量对众多应用场景（如医疗保健行业、体育运动、康复和体能评估）至关重要，但足够有效且可靠的步态参数测量在很大程度上仍局限于高科技步态实验室。在此，我们展示了一种新型步态评估系统的出色有效性和重测信度，该系统基于现代卷积神经网络，从行走人体的单目正面视角视频中提取三维骨骼关节。有效性研究基于与GAITRite压敏步道系统的比较。所有测量的步态参数（步速、步频、步长和步态周期时间）在正常步速和快速步速下的多次行走试验中均表现出出色的同时效度。重测信度与GAITRite系统处于同一水平。总之，我们相信我们的结果能够为在广泛的主流应用中实现成本、空间和操作上高效的步态分析铺平道路。大多数基于传感器的系统成本高昂，必须由经过广泛培训的人员操作（例如运动捕捉系统），或者——即使成本不那么高——仍然具有相当大的复杂性（例如可穿戴传感器）。相比之下，本文所提出的评估方法所需的视频，任何人都可以通过智能手机摄像头获取，无需太多培训。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
