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Performance metrics for guidance active constraints in surgical robotics
Author(s) -
Enayati Nima,
Ferrigno Giancarlo,
De Momi Elena
Publication year - 2018
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.1873
Subject(s) - computer science , interactivity , set (abstract data type) , human–computer interaction , robotics , adaptability , artificial intelligence , human in the loop , robot , machine learning , multimedia , ecology , biology , programming language
Active constraint (AC)/virtual fixture (VF) is among the most popular approaches towards the shared execution of subtasks by the surgeon and robotic systems. As more possibilities appear for the implementation of ACs in surgical scenarios, the need to introduce methods that guarantee a safe and intuitive user‐interaction increases. The presence of the human in the loop adds a layer of interactivity and adaptability that renders the assessment of such methods non‐trivial. In most works, guidance ACs have been evaluated mainly in terms of enhancement of accuracy and completion time with little regard for other aspects such as human factors, even though the continuous engagement of these methods can considerably degrade the user experience. This paper proposes a set of performance metrics and considerations that can help evaluate guidance ACs with reference to accuracy enhancement, force characteristics and subjective aspects. The use of these metrics is demonstrated through two sets of experiments on 12 surgeons and 6 inexperienced users.