Evaluating the Accuracy of Virtual Reality Trackers for Computing Spatiotemporal Gait Parameters

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.

Medienart:

E-Artikel

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

Zur Gesamtaufnahme - volume:21

Enthalten in:

Sensors (Basel, Switzerland) - 21(2021), 10 vom: 11. Mai

Sprache:

Englisch

Beteiligte Personen:

Guaitolini, Michelangelo [VerfasserIn]
Petros, Fitsum E [VerfasserIn]
Prado, Antonio [VerfasserIn]
Sabatini, Angelo M [VerfasserIn]
Agrawal, Sunil K [VerfasserIn]

Links:

Volltext

Themen:

Gait analysis
Gait event detection
Gait features
Journal Article
Motion analysis
Virtual reality

Anmerkungen:

Date Completed 04.06.2021

Date Revised 05.06.2021

published: Electronic

Citation Status MEDLINE

doi:

10.3390/s21103325

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM326133003