Target recognition based on motion in depth under simulated prosthesis vision

Vision is a crucial means for humans to perceive external information. As an artificial device utilizing functional electrical stimulation, visual prosthesis can induce phosphenes through electrical stimulation of the retina, optic nerve, or visual cortex to assist implanters in restoring part of their visual perception. In order to investigate object recognition among prosthesis wearers based on deep motion, this experiment employed virtual reality technology in the prosthesis vision experiment, constructed a virtual simulation of the prosthesis vision scene in Unity, and analyzed the impact of deletion, resolution, and dot size on target recognition under prosthesis vision. The experimental findings revealed that subjects had a lower recognition rate for scenes with 50% missing compared to scenes with 30% missing and standard scenes. Additionally, scenes with a resolution of 128×128 exhibited higher recognition rates than those with resolutions of 64×64 and 48×48. Significant differences were observed between small and standard scenes as well as large scenes. However, there was no significant difference between standard and large scenes. Moreover, the recognition rate for larger object shapes was higher. This study can provide ideas for virtual reality research on simulated prosthetic vision, and provide a theoretical basis for the improvement of prosthesis wearers' training and life ability in the future.