Browsing by Author "Aliza, Aliza"

Now showing 1 - 2 of 2

Citation Count: 0
Eye-Hand Coordination Training: a Systematic Comparison of 2d, Vr, and Ar Display Technologies and Task Instructions
(Ieee Computer Soc, 2024) Aliza, Aliza; Zaugg, Irene; Celik, Elif; Stuerzlinger, Wolfgang; Ortega, Francisco Raul; Batmaz, Anil Ufuk; Sarac, Mine
Previous studies on Eye-Hand Coordination Training (EHCT) focused on the comparison of user motor performance across different hardware with cross-sectional studies. In this paper, we compare user motor performance with an EHCT setup in Augmented Reality (AR), Virtual Reality (VR), and on a 2D touchscreen display in a longitudinal study. Through a ten-day user study, we thoroughly analyzed the motor performance of twenty participants with five task instructions focusing on speed, error rate, accuracy, precision, and none. As a novel evaluation criterion, we also analyzed the participants' performance in terms of effective throughput. The results showed that each task instruction has a different effect on one or more psychomotor characteristics of the trainee, which highlights the importance of personalized training programs. Regarding different display technologies, the majority of participants could see more improvement in VR than in 2D or AR. We also identified that effective throughput is a good candidate for monitoring overall motor performance progress in EHCT systems.
Citation Count: 1
On the Effectiveness of Virtual Eye-Hand Coordination Training With Head Mounted Displays
(IEEE Computer Soc, 2023) Mughrabi, Moaaz Hudhud; Kaya, Furkan; Batmaz, Anil Ufuk; Aliza, Aliza; Stuerzlinger, Wolfgang; Borazan, Baris; Tonyali, Emir
Eye-hand coordination training systems are used to train participants' motor skills and visual perception. Such systems have already been tested in Virtual Reality, and the results revealed that Head Mounted Display-based systems have the potential to improve the motor training. However. this was only investigated in an hour-long study. In the longitudinal study reported here, we analyzed the motor performance of three participants in ten sessions with three different assessment criteria, where participants were instructed to focus on speed, error rate, or complete the training freely (with no instructions). We also assessed the effective throughput performance of the participants. Our results indicate that effective throughput can be potentially used as an additional assessment criterion, We hope that our results will help practitioners and developers design efficient Virtual Reality training systems.