Virtual reality (VR) is an emerging technology offering tremendous opportunities to aid gait rehabilitation. To this date, real walking with users immersed in virtual environments with head-mounted displays (HMDs) is either possible with treadmills or room-scale (overground) VR setups. Especially for the latter, there is a growing interest in applications for interactive gait training as they could allow for more self-paced and natural walking. This study investigated if walking in an overground VR environment has relevant effects on 3D gait biomechanics. A convenience sample of 21 healthy individuals underwent standard 3D gait analysis during four randomly assigned walking conditions: the real laboratory (RLab), a virtual laboratory resembling the real world (VRLab), a small version of the VRlab (VRLab−), and a version which is twice as long as the VRlab (VRLab+). To immerse the participants in the virtual environment we used a VR-HMD, which was operated wireless and calibrated in a way that the virtual labs would match the real-world. Walking speed and a single measure of gait kinematic variability (GaitSD) served as primary outcomes next to standard spatio-temporal parameters, their coefficients of variant (CV%), kinematics, and kinetics. Briefly described, participants demonstrated a slower walking pattern (−0.09 ± 0.06 m/s) and small accompanying kinematic and kinetic changes. Participants also showed a markedly increased gait variability in lower extremity gait kinematics and spatio-temporal parameters. No differences were found between walking in VRLab+ vs. VRLab−. Most of the kinematic and kinetic differences were too small to be regarded as relevant, but increased kinematic variability (+57%) along with increased percent double support time (+4%), and increased step width variability (+38%) indicate gait adaptions toward a more conservative or cautious gait due to instability induced by the VR environment. We suggest considering these effects in the design of VR-based overground training devices. Our study lays the foundation for upcoming developments in the field of VR-assisted gait rehabilitation as it describes how VR in overground walking scenarios impacts our gait pattern. This information is of high relevance when one wants to develop purposeful rehabilitation tools.

No abstract

This paper describes the development, implementation and evaluation of a speech/music detector. We aim at audio from different sources with different qualities -i.e. audio from "the wild". We examine existing approaches for audio classification and select a recent feature. We modify the feature and evaluate the classification accuracy on a random test set of more than 60 hours of audio material against a standard speech/music detection approach. With our approach, we reach a classification accuracy of 96,6%. We provide a performant open source implementation of our detector.

No abstract

No abstract