Back

Unraveling the effects of virtual reality overground walking on dynamic balance and postural control

Horsak, B.; Simonlehner, M.; Dumphart, B.; Siragy, T.

2022-12-14 bioengineering
10.1101/2022.12.12.519831 bioRxiv
Show abstract

This study analyzed the effects of walking freely in Virtual Reality (VR) compared to walking in the real-world on dynamic balance and postural control. For this purpose nine male and twelve female healthy participants underwent standard 3D gait analysis while walking randomly in a real laboratory and in a room-scale overground VR environment resembling the real laboratory. The VR was delivered to participants by a head-mounted-display which was operated wirelessly and calibrated to the real-world. Dynamic balance was assessed with three outcomes: the Margin of Stability (MOS) in the anteroposterior (AP-MOS) and mediolateral (ML-MOS) directions at initial-contact, the relationship between the mediolateral Center of Mass (COM) position and acceleration at mid-stance with subsequent step width, and trunk kinematics during the entire gait cycle. We observed increased mediolateral (ML) trunk linear velocity variability, an increased coupling of the COM position and acceleration with subsequent step width, and a decrease in AP-MOS while walking in VR, but no change in ML-MOS when walking in VR. We conclude that walking in VR results in a less reliable optical flow, indicated by increased mediolateral trunk kinematic variability, which seems to be compensated by the participants by slightly reweighing sensorimotor input and thereby consciously tightening the coupling between the COM and foot placement to avoid a loss of balance. Our results are particularly valuable for future developers who want to use VR to support gait analysis and rehabilitation.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.