Studying Relationships of Muscle Representations and Levels of Interactivity in a Canine Anatomy VR Environment Conference Paper uri icon


  • Springer Nature Switzerland AG 2019. Virtual Reality (VR) is at the forefront of modern technology; revolutionizing current methods for conducting activities such as gaming, training simulations, and education. When considering anatomy education specifically, students must learn form, function, and movement of various bones, muscles, muscle tendons, ligaments, and joints within the body. Historically, cadaver dissection is believed to be the most optimal method of study, but it is not always accessible. We created a VR canine thoracic limb application that allows students to learn about musculoskeletal movements, while dynamically interacting with anatomical visualization. We aimed at increasing memory retention in a more immersive and engaging way. In our study, three major factors were considered: (1) spatial visualization ability of learners, (2) visualization styles of muscles, and (3) levels of interactivity of the application. Participants of differing spatial abilities (high and low) studied a virtual thoracic limb in one of two visual conditions (realistic muscles or symbolic muscles) and one of two interactive conditions (interactive manipulation or non-interactive viewing). We tested these against each other to determine which method of muscle representation holds the most effective form of memory retention, and what role interactivity plays in this retention. Before the experiment, we gathered data pertaining to students spatial visualization ability via a mental rotation test to create a baseline. After the experiment, we interviewed the participants to gather qualitative data about the applications effectiveness and usability. We observed through 24 user studies that low spatial visualization users gained an advantage through dynamic visualization learning to almost perform as well as their high spatial visualization counterparts. Realistic muscles assisted participants with identifying anatomical views more efficiently, and therefore had a significantly better average compared to the symbolic representation.

published proceedings

  • Communications in Computer and Information Science

author list (cited authors)

  • Heymann, B., White, P., & Seo, J. H.

citation count

  • 0

complete list of authors

  • Heymann, Ben||White, Preston||Seo, Jinsil Hwaryoung

publication date

  • January 2019