Evaluating the Accuracy of Virtual Reality Trackers for Computing Spatiotemporal Gait Parameters
| Autor: | Angelo Maria Sabatini, M. Guaitolini, Sunil K. Agrawal, Fitsum E. Petros, Antonio Prado |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
030506 rehabilitation
medicine.medical_specialty Motion analysis Computer science BitTorrent tracker STRIDE Walking TP1-1185 Virtual reality Biochemistry Article Analytical Chemistry User-Computer Interface 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Gait (human) medicine Humans gait features Electrical and Electronic Engineering Gait Instrumentation motion analysis Chemical technology Work (physics) Swing Atomic and Molecular Physics and Optics Gait analysis gait analysis gait event detection virtual reality 0305 other medical science 030217 neurology & neurosurgery |
| Zdroj: | Sensors, Vol 21, Iss 3325, p 3325 (2021) Sensors (Basel, Switzerland) Sensors Volume 21 Issue 10 |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|