Abstract
Technological advances have enabled new paradigms for skill training using virtual reality and robotics. We present three recent research advances in the field of virtual reality and human–robot interaction (HRI) for training. First, skill assessment in these systems is discussed, with an emphasis on the derivation of meaningful and objective quantitative performance metrics from motion data acquired through sensors on the robotic devices. We show how such quantitative measures derived for the robotic stroke rehabilitation domain correlate strongly with clinical measures of motor impairment. For virtual reality-based task training, we present task analysis and motion-based performance metrics for a manual control task. Lastly, we describe specific challenges in the surgical domain, with a focus on the development of tasks for skills assessment in surgical robotics.
An erratum to this chapter is available at http://dx.doi.org/10.1007/978-1-4614-8648-0_26
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-1-4614-8648-0_26
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Acknowledgments
Portions of this work have been support in part by grants from the National Science Foundation (IIS-0448341 and IIS-0812569) and Mission Connect, a project of the TIRR Foundation.
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O’Malley, M.K. et al. (2014). Robotics as a Tool for Training and Assessment of Surgical Skill. In: Garbey, M., Bass, B., Berceli, S., Collet, C., Cerveri, P. (eds) Computational Surgery and Dual Training. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8648-0_24
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DOI: https://doi.org/10.1007/978-1-4614-8648-0_24
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