The Effect of Training in Virtual Reality on the Precision of Hand Movements

Abstract

The main point of the work was to use virtual reality to discover its benefits on training, specifically on the precision of hand movements in specific settings, and then evaluate its effects both for virtual reality and the transfer of the results to the real world. A virtual reality simulation was created using the Unity3D game engine and real-world experimental material was also prepared. A total of 16 participants took part in the training, which lasted for approximately one month. Once the data was gathered from both the virtual reality and real-world tests, we carried out in-depth statistical analysis. The results suggest positive outcomes in most aspects in virtual reality training productivity, but only partial transfer of the training benefits to the real world scenario. The possible reasons for this are described in the work and suggestions are given to duplicate the study with different variables to try to achieve different results.

State-of-the-Art Review on Immersive Virtual Reality Interventions for Colonoscopy-Induced Anxiety and Pain

“…Finally, physicians performing invasive or minimally invasive procedures such as colonoscopy training may benefit from iVR exposure training by lowering performance anxiety. The improvement of hand coordination [ 79 ], greater procedural confidence [ 80 ], facilitation of the acquisition of complex skills and also translating learned aspects into practice was reported to be even five times as efficient compared with conventional training methods [ 81 ]. To date, no colonoscopy simulators offer iVR compatible interfaces.…”

New Approach to Accelerated Image Annotation by Leveraging Virtual Reality and Cloud Computing

“…With VR, the perception of 3D structures in complex data (e.g., EM images) is enhanced, and measurements are performed quicker than in standard 2D stack viewers. Examples of advantages in 3D perception of VR are found for histological sample examination ( Lobachev et al, 2021 ), surgery simulation or planning ( Seymour, 2008 ; Guerriero et al, 2018 ; Thomsen et al, 2017 ; Chen et al, 2020 ), motion or gaze precision ( Martirosov et al, 2021 ; Pastel et al, 2021 ), and 3D data labeling prior to machine learning training ( Ramirez et al, 2020 ). Medical experts and undergraduate students have reported better visualization of 3D anatomical structures in VR using DIVA, when compared with typical 3D renderings, see Table 1 in Bouaoud et al (2020) and Raimondi et al (2021) .…”