The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127 Pisa, Italy.
Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy.
Sensors (Basel). 2021 May 11;21(10):3325. doi: 10.3390/s21103325.
Ageing, disease, and injuries result in movement defects that affect daily life. Gait analysis is a vital tool for understanding and evaluating these movement dysfunctions. In recent years, the use of virtual reality (VR) to observe motion and offer augmented clinical care has increased. Although VR-based methodologies have shown benefits in improving gait functions, their validity against more traditional methods (e.g., cameras or instrumented walkways) is yet to be established. In this work, we propose a procedure aimed at testing the accuracy and viability of a VIVE Virtual Reality system for gait analysis. Seven young healthy subjects were asked to walk along an instrumented walkway while wearing VR trackers. Heel strike (HS) and toe off (TO) events were assessed using the VIVE system and the instrumented walkway, along with stride length (SL), stride time (ST), stride width (SW), stride velocity (SV), and stance/swing percentage (STC, SWC%). Results from the VR were compared with the instrumented walkway in terms of detection offset for time events and root mean square error (RMSE) for gait features. An absolute offset between VR- and walkway-based data of (15.3 ± 12.8) ms for HS, (17.6 ± 14.8) ms for TOs and an RMSE of 2.6 cm for SW, 2.0 cm for SL, 17.4 ms for ST, 2.2 m/s for SV, and 2.1% for stance and swing percentage were obtained. Our findings show VR-based systems can accurately monitor gait while also offering new perspectives for VR augmented analysis.
衰老、疾病和损伤导致运动缺陷,影响日常生活。步态分析是理解和评估这些运动功能障碍的重要工具。近年来,虚拟现实 (VR) 在观察运动和提供增强的临床护理方面的应用越来越多。尽管基于 VR 的方法已显示出改善步态功能的益处,但它们与更传统方法(例如相机或仪器化步道)的有效性仍有待确定。在这项工作中,我们提出了一种程序,旨在测试 VIVE 虚拟现实系统进行步态分析的准确性和可行性。要求七名年轻健康的受试者在佩戴 VR 跟踪器的情况下沿着仪器化步道行走。使用 VIVE 系统和仪器化步道评估足跟触地 (HS) 和脚趾离地 (TO) 事件,以及步长 (SL)、步长时间 (ST)、步宽 (SW)、步长速度 (SV) 和站立/摆动百分比 (STC、SWC%)。根据时间事件的检测偏移和步态特征的均方根误差 (RMSE),比较了 VR 与仪器化步道的结果。HS 的 VR 和步道数据之间的绝对偏移为 (15.3 ± 12.8) ms,TO 的绝对偏移为 (17.6 ± 14.8) ms,SW 的 RMSE 为 2.6 cm,SL 的 RMSE 为 2.0 cm,ST 的 RMSE 为 17.4 ms,SV 的 RMSE 为 2.2 m/s,站立和摆动百分比的 RMSE 为 2.1%。我们的研究结果表明,基于 VR 的系统可以准确监测步态,同时为 VR 增强分析提供新的视角。