The purpose of this research is to first: identify the important human factors to performance when operating an assistive robotic manipulator, second: develop a predictive model that will be able to determine a user's performance based on their known human factors, and third: develop compensators based on the determined important human factors that will help improve user performance and satisfaction. An extensive literature search led to the selection of ten potential human factors to be analyzed including reaction time, spatial abilities (orientation and visualization), working memory, visual perception, dexterity (gross and fine), depth perception, and visual acuity of both eyes (classified as strongest and weakest). 93 participants were recruited to perform six different pick-and-place and retrieval tasks using an assistive robotic device. During this time, a participants Time-on-Task, Number-of-Moves, and Number-of-Moves per minute were recorded. From this it was determined that all the human factors except visual perception were considered important to at least one aspect of a user's performance. Predictive models were then developed using random forest, linear models, and polynomial models. To compensate for deficiencies in certain human factors, the GUI was redesigned based on a heuristic analysis and user feedback. Multimodal feedback as well as adjustments in the sensitivity of the input device and reduction in the robot's speed of movement were also implemented. From a user study using 15 participants it was found that certain compensation did improve satisfaction of the users, particularly the multimodal feedback and sensitivity adjustment. The reduction of speed was met with mixed reviews from the participants.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Electrical Engineering and Computer Engineering
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Paperno, Nicholas, "Modeling and Compensation for Efficient Human Robot Interaction" (2016). Electronic Theses and Dissertations, 2004-2019. 5202.