Abstract：
BACKGROUND: Over the past decades, tremendous technological advances have emerged in human motion analysis (HMA).
OBJECTIVE: How has technology for analysing human motion evolved over the past decades, and what clinical applications has it enabled?
METHODS: The literature on HMA has been extensively reviewed, focusing on three main approaches: Fully-Instrumented Gait Analysis (FGA), Wearable Sensor Analysis (WSA), and Deep-Learning Video Analysis (DVA), considering both technical and clinical aspects.
RESULTS: FGA techniques relying on data collected using stereophotogrammetric systems, force plates, and electromyographic sensors have been dramatically improved providing highly accurate estimates of the biomechanics of motion. WSA techniques have been developed with the advances in data collection at home and in community settings. DVA techniques have emerged through artificial intelligence, which has marked the last decade. Some authors have considered WSA and DVA techniques as alternatives to \"traditional\" HMA techniques. They have suggested that WSA and DVA techniques are destined to replace FGA.
CONCLUSIONS: We argue that FGA, WSA, and DVA complement each other and hence should be accounted as \"synergistic\" in the context of modern HMA and its clinical applications. We point out that DVA techniques are especially attractive as screening techniques, WSA methods enable data collection in the home and community for extensive periods of time, and FGA does maintain superior accuracy and should be the preferred technique when a complete and highly accurate biomechanical data is required. Accordingly, we envision that future clinical applications of HMA would favour screening patients using DVA in the outpatient setting. If deemed clinically appropriate, then WSA would be used to collect data in the home and community to derive relevant information. If accurate kinetic data is needed, then patients should be referred to specialized centres where an FGA system is available, together with medical imaging and thorough clinical assessments.
OBJECTIVE: How has technology for analysing human motion evolved over the past decades, and what clinical applications has it enabled?
METHODS: The literature on HMA has been extensively reviewed, focusing on three main approaches: Fully-Instrumented Gait Analysis (FGA), Wearable Sensor Analysis (WSA), and Deep-Learning Video Analysis (DVA), considering both technical and clinical aspects.
RESULTS: FGA techniques relying on data collected using stereophotogrammetric systems, force plates, and electromyographic sensors have been dramatically improved providing highly accurate estimates of the biomechanics of motion. WSA techniques have been developed with the advances in data collection at home and in community settings. DVA techniques have emerged through artificial intelligence, which has marked the last decade. Some authors have considered WSA and DVA techniques as alternatives to \"traditional\" HMA techniques. They have suggested that WSA and DVA techniques are destined to replace FGA.
CONCLUSIONS: We argue that FGA, WSA, and DVA complement each other and hence should be accounted as \"synergistic\" in the context of modern HMA and its clinical applications. We point out that DVA techniques are especially attractive as screening techniques, WSA methods enable data collection in the home and community for extensive periods of time, and FGA does maintain superior accuracy and should be the preferred technique when a complete and highly accurate biomechanical data is required. Accordingly, we envision that future clinical applications of HMA would favour screening patients using DVA in the outpatient setting. If deemed clinically appropriate, then WSA would be used to collect data in the home and community to derive relevant information. If accurate kinetic data is needed, then patients should be referred to specialized centres where an FGA system is available, together with medical imaging and thorough clinical assessments.
摘要：
背景：在过去的几十年里，人类运动分析(HMA)领域出现了巨大的技术进步。
目标：在过去的几十年中，分析人类运动的技术是如何发展的，以及它有哪些临床应用？
方法：关于HMA的文献已被广泛综述，专注于三种主要方法：全仪表化步态分析(FGA)，可穿戴式传感器分析(WSA),和深度学习视频分析(DVA)，同时考虑技术和临床方面。
结果：FGA技术依赖于使用立体摄影测量系统收集的数据，力板,和肌电图传感器已经得到了极大的改进，为运动的生物力学提供了高度准确的估计。随着家庭和社区环境中数据收集的进步，WSA技术得到了发展。DVA技术已经通过人工智能出现，这标志着过去的十年。一些作者认为WSA和DVA技术可以替代“传统”HMA技术。他们建议WSA和DVA技术注定要取代FGA。
结论：我们认为FGA，WSA,和DVA相互补充，因此在现代HMA及其临床应用的背景下，应被视为“协同”。我们指出，DVA技术作为筛选技术特别有吸引力，WSA方法可以在家庭和社区中广泛收集数据，和FGA确实保持较高的准确性，当需要完整和高度准确的生物力学数据时，应该是首选技术。因此，我们预计HMA的未来临床应用将有利于在门诊使用DVA筛查患者.如果临床上认为合适，然后，WSA将用于收集家庭和社区的数据，以获取相关信息。如果需要准确的动力学数据，然后患者应转诊到有FGA系统的专业中心，连同医学成像和全面的临床评估。
目标：在过去的几十年中，分析人类运动的技术是如何发展的，以及它有哪些临床应用？
方法：关于HMA的文献已被广泛综述，专注于三种主要方法：全仪表化步态分析(FGA)，可穿戴式传感器分析(WSA),和深度学习视频分析(DVA)，同时考虑技术和临床方面。
结果：FGA技术依赖于使用立体摄影测量系统收集的数据，力板,和肌电图传感器已经得到了极大的改进，为运动的生物力学提供了高度准确的估计。随着家庭和社区环境中数据收集的进步，WSA技术得到了发展。DVA技术已经通过人工智能出现，这标志着过去的十年。一些作者认为WSA和DVA技术可以替代“传统”HMA技术。他们建议WSA和DVA技术注定要取代FGA。
结论：我们认为FGA，WSA,和DVA相互补充，因此在现代HMA及其临床应用的背景下，应被视为“协同”。我们指出，DVA技术作为筛选技术特别有吸引力，WSA方法可以在家庭和社区中广泛收集数据，和FGA确实保持较高的准确性，当需要完整和高度准确的生物力学数据时，应该是首选技术。因此，我们预计HMA的未来临床应用将有利于在门诊使用DVA筛查患者.如果临床上认为合适，然后，WSA将用于收集家庭和社区的数据，以获取相关信息。如果需要准确的动力学数据，然后患者应转诊到有FGA系统的专业中心，连同医学成像和全面的临床评估。
