# The Effect of Training in Virtual Reality on the Precision of Hand Movements
## 5 In-Depth Statistical Evaluation
### VR Experiment
movements using a specific type of VR simulation that would allow users/participants to perform similar movement tasks over a prolonged period of time (approximately one month). As VR is said to be a very effective technology in this sphere, we created a VR simulation using the Unity3D game engine for this experiment. This engine proved itself to be very powerful and easy to use, as it had already been used for other types of simulations prior to this. The simulation for this work took several months to prepare and conduct. In addition to VR tests, RW experiments also took place. VR tests took place in seven sessions, whereas there were only two RW tests, on the start and end dates of the experiment. This was done to analyse the effects of training not only in VR itself but also the transfer of skills to the RW. Once the experiment was done, the data were collected. VR data was gathered inside the Unity3D game engine during the test process itself. Then the data about precision (in Json format) were exported to Excel, where they were processed in pre-prepared macro templates. RW data on performing the line movements (line, circle, sine wave) was processed at the end of the experiment. Lastly, RW data was exported to Excel templates, and the precision results were calculated and analysed using specific macros. The results suggest very positive outcomes for VR training in particular. As the participants had to perform on each of seven sessions four attempts of each type of simulation (six overall: one hand/two hands, line/circle/sine wave); in the end, the results improved drastically, and they actually improved from the start to the end. It was pointed out that possibly only four training sessions for this type of training could be sufficient as, after the fourth attempt, although there were improvements, they were not that impressive. When it comes to the RW tests, the results suggest that the transfer of skill from VR to RW was only partial. A study suggested similar results, wherein the training in VR produced positive results in that virtual scenario, but when it came to transferring the training to the RW scenario, no significant or no benefits were found [41]. Only the time value improved from the first attempt compared to the second one. Multiple reasons were suggested for this, such as participant forgetfulness, noninterest, tiredness, etc. It is recommended to perform such an experiment with a much bigger pool of participants, in-depth questionnaires, and possibly perform RW tests more frequently between the VR tests to remind the participants what they are training for. Additionally, it could also be the type of simulation that resulted in these outcomes. In order to be sure about some factors that could cause these non-beneficial effects, more research and experimentation should be performed on top of this work. As this experiment and training look very similar to welding training simulations, it is interesting to point out a couple of studies that delved into training participants in this field and evaluated the transfer of that training to practical cases. A study [42] suggests that VR welding simulations are good not only for novice welders but also for experienced ones as well. The training in VR welding is then used in practice in RW cases. As the results show some difference in quality between novice and experienced welders, data also suggests that even experienced welders can struggle with higher difficulty welds in VR, which can in turn allow them to train and become better at new levels. Thus, suggesting that there is a positive correlation and transfer of skills from VR training to the real world depending on the simulation type/difficulty. On the other hand, research [43] on different types of welding difficulty tasks shows that, the higher the difficulty of the welding task in VR, the less effective VR training becomes, thus requiring supplementary RW training.