Booth A T C, van der Krogt M M, Buizer A I, Steenbrink F, Harlaar J
Department of Rehabilitation Medicine, Amsterdam UMC, VU University Medical Center, Amsterdam Movement Sciences, Netherlands; Department of Clinical Applications and Research, Motek Medical B.V., Amsterdam, Netherlands.
Department of Rehabilitation Medicine, Amsterdam UMC, VU University Medical Center, Amsterdam Movement Sciences, Netherlands.
Clin Biomech (Bristol). 2019 Dec;70:146-152. doi: 10.1016/j.clinbiomech.2019.08.013. Epub 2019 Aug 24.
Virtual reality presents a platform for therapeutic gaming, and incorporation of immersive biofeedback on gait may enhance outcomes in rehabilitation. Time is limited in therapeutic practice, therefore any potential gait training tool requires a short set up time, while maintaining clinical relevance and accuracy. The aim of this study was to develop, validate, and establish the usability of an avatar-based application for biofeedback-enhanced gait training with minimal set up time.
A simplified, eight marker model was developed using eight passive markers placed on anatomical landmarks. This allowed for visualisation of avatar-based biofeedback on pelvis kinematics, hip and knee sagittal angles in real-time. Retrospective gait analysis data from typically developing children (n = 41) and children with cerebral palsy (n = 25), were used to validate eight marker model. Gait outcomes were compared to the Human Body Model using statistical parametric mapping. Usability for use in clinical practice was tested in five clinical rehabilitation centers with the system usability score.
Gait outcomes of Human Body Model and eight marker model were comparable, with small differences in gait parameters. The discrepancies between models were <5°, except for knee extension where eight marker model showed significantly less knee extension, especially towards full extension. The application was considered of 'high marginal acceptability' (system usability score, mean 68 (SD 13)).
Gait biofeedback can be achieved, to acceptable accuracy for within-session gait training, using an eight marker model. The application may be considered usable and implemented for use in patient populations undergoing gait training.
虚拟现实为治疗性游戏提供了一个平台，将沉浸式生物反馈应用于步态训练可能会提高康复效果。治疗实践中的时间有限，因此任何潜在的步态训练工具都需要较短的设置时间，同时保持临床相关性和准确性。本研究的目的是开发、验证并确定一种基于虚拟形象的应用程序在最短设置时间下用于生物反馈增强步态训练的可用性。
使用放置在解剖标志点上的八个被动标记开发了一个简化的八标记模型。这使得能够实时可视化基于虚拟形象的骨盆运动学、髋关节和膝关节矢状角的生物反馈。来自正常发育儿童（n = 41）和脑瘫儿童（n = 25）的回顾性步态分析数据用于验证八标记模型。使用统计参数映射将步态结果与人体模型进行比较。在五个临床康复中心使用系统可用性评分对该应用程序在临床实践中的可用性进行了测试。
人体模型和八标记模型的步态结果具有可比性，步态参数存在微小差异。除膝关节伸展外，模型之间的差异<5°，其中八标记模型显示膝关节伸展明显较少，尤其是在接近完全伸展时。该应用程序被认为具有“高边际可接受性”（系统可用性评分，平均68（标准差13））。
使用八标记模型可以实现步态生物反馈，其准确性对于疗程内的步态训练是可接受的。该应用程序可被认为是可用的，并可用于接受步态训练的患者群体。
