Browsing by Author "George, Ivan"
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Item Communication Cost of Single-user Gesturing Tool in Laparoscopic Surgical Training(Association for Computing Machinery, 2019-05-04) Feng, Yuanyuan; Li, Katie; Semsar, Azin; McGowan, Hannah; Mun, Jacqueline; Zahiri, H. Reza; George, Ivan; Park, Adrian; Kleinsmith, Andrea; Mentis, Helena M.Multi-user input over a shared display has been shown to support group process and improve performance. However, current gesturing systems for instructional collaborative tasks limit the input to experts and overlook the needs of novices in making references on a shared display. In this paper, we investigate the effects of a single-user gesturing tool on the communication between trainer and trainees in a laparoscopic surgical training. By comparing the communication structure and content between the trainings with and without the gesturing tool, we show that the communication becomes more imbalanced and the trainees become less active when using the single-user gesturing tool. Our findings highlight the needs to grant all parties the same level of access to a shared display and suggest further directions in designing a shared display for instructional collaborative tasks.Item A Ubiquitous Context-Aware Environment for Surgical Training(IEEE, 2007-08-06) Ordoñez, Patricia; Kodeswaran, Palanivel Andiappan; Korolev, Vladimir; Li, Wenjia; Walavalkar, Onkar; Joshi, Anupam; Finin, Tim; Yesha, Yelena; George, IvanThe age of technology has changed the way that surgeons are being trained. Traditional methodologies for training can include lecturing, shadowing, apprenticing, and developing skills within live clinical situations. Computerized tools which simulate surgical procedures and/or experiences can allow for “virtual” experiences to enhance the traditional training procedures that can dramatically improve upon the older methods. However, such systems do not to adapt to the training context. We describe a ubiquitous computing system that tracks low-level events in the surgical training room (e.g. student locations, lessons completed, learning tasks assigned, and performance metrics) and from these derive the training context. This can be used to create an adaptive training system.