Virtual Reality-Based Navigation Task to Reveal Obstacle Avoidance Performance in Individuals With Visuospatial Neglect PROJECT TITLE:Virtual Reality-Based Navigation Task to Reveal Obstacle Avoidance Performance in Individuals With Visuospatial NeglectABSTRACT:Persons with post-stroke visuospatial neglect (VSN) often collide with moving obstacles while walking. It is not well understood whether or not the collisions occur as a result of attentional-perceptual deficits caused by VSN or due to post-stroke locomotor deficits. We assessed people with VSN on a seated, joystick-driven obstacle avoidance task, thus eliminating the influence of locomotion. Twelve participants with VSN were tested on obstacle detection and obstacle avoidance tasks in a virtual setting that included three obstacles approaching head-on or thirty$^circ$ contralesionally/ipsilesionally. Our results indicate that within the detection task, the contralesional and head-on obstacles were detected at closer proximities compared to the ipsilesional obstacle. For the avoidance task collisions were observed only for the contralesional and head-on obstacle approaches. For the contralesional obstacle approach, participants initiated their avoidance ways at smaller distances from the obstacle and maintained smaller minimum distances from the obstacles. The gap at detection showed a negative association with the distance at the onset of avoidance strategy for all three obstacle approaches. We conclusion the observation of collisions with contralesional and head-on obstacles, within the absence of locomotor burden, provides evidence that attentional-perceptual deficits because of VSN, independent of post-stroke locomotor deficits, alter obstacle avoidance skills. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest An Optimizer's Approach to Stochastic Control Problems With Nonclassical Information Structures PCR-CTPP Design for Enzyme-Free SNP Genotyping Using Memetic Algorithm