Evaluation of properties and usability of virtual reality interaction techniques in craniomaxillofacial computer-assisted surgical simulation
Authors: 
Helena Mehtonen, Jorma Järnstedt, Jari Kangas, Sriram Kumar, Irina Rinta-Kiikka, Roope RaisamoAuthors Info & Claims
Helena Mehtonen, Jorma Järnstedt, Jari Kangas, Sriram Kumar, Irina Rinta-Kiikka, Roope RaisamoAuthors Info & ClaimsPages 306 - 315
Abstract
Although there are promising results from the use of virtual reality (VR) in the craniomaxillofacial field, there is still a need to validate the usability and properties of the VR environment and interaction techniques. The aim of this study was to evaluate the suitability of VR interaction methods for craniomaxillofacial computer-assisted surgical simulation (CASS) and to identify possible areas for improvement.
Four VR interaction conditions were compared quantitatively and qualitatively: Hand, Mouse, Pen and, Controller. Four oral and maxillofacial radiologists performed a VR marking task on skull stereolithography models. Quantitative measures included accuracy, completion time, number of grasps and development. Qualitative attributes were easiness, efficiency, physical effort, accuracy, and naturalness.
Mouse (1,51 mm) and Controller (1,73 mm) were the most accurate, Pen was the close third (2,06 mm), while Hand (4,52 mm) scored poorly. Mouse was slower and more burdensome than the other conditions. The accuracy of Pen and the completion times of Hand, Pen and Mouse improved over time. The usability of Controller (1,50) was rated best in Likert scale (1-5), with Pen (1,75) the close second. Mouse (3,00) and Hand (3,57) were inferior, and overall, Hand was the least preferred.
Controller and Mouse achieved acceptable accuracy for craniomaxillofacial CASS. The usability of Controller was also rated highest, and it was the preferred choice of the radiologists. The combination of Mouse and VR was unnatural and cumbersome. To achieve an acceptable level of accuracy for Hand, hand tracking technology needs to be significantly improved.
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December 2023
733 pages
ISBN:9798400717079
DOI:10.1145/3638380
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Association for Computing Machinery
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Published: 10 May 2024
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