K. Wilcocks, Argyrios Perivolaris, B. Kapralos, A. Quevedo, Michael Jenkin, K. Kanev, Hidenori Mimura, Makoto Hosoda, F. Alam, A. Dubrowski
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Work-in-Progress: A Novel Data Glove for Psychomotor-Based Virtual Medical Training
Despite its importance in the real-world, manual (hand) dexterity is often ignored in medical-based virtual training environments that have traditionally focused on cognitive and affective skills development. Psychomotor (technical) skills, particularly those related to manual dexterity, are fundamental to various medical procedures and ignoring them in virtual based training tools can lead to a sub-optimal training experience. Here, we present a novel, consumer-level data glove that provides accurate user interactions involving the proximal and medial phalanges, interactions that are relevant in many manual dexterity tasks. We also outline how this novel data glove is being incorporated into an existing serious gaming platform for anesthesia training that currently focuses on cognitive and affective skills development only. The addition of psychomotor skills development through the adoption of simulated tactile feedback will provide a more complete serious gaming training platform.