A Predictive Model For Use Of An Assistive Robotic Manipulator: Human Factors Versus Performance In Pick-And-Place/Retrieval Tasks

Keywords

Assistive robotics; human factors; human-robot interaction; machine learning; modeling

Abstract

The goal of this study was to model the important individual differences to predict a user's performance when operating an assistive robotic manipulator for a general population. Prior research done led to the identification of ten potential human factors to be observed including dexterity (gross and fine), spatial abilities (orientation and visualization), visual acuity in each eye, visual perception, depth perception, reaction time, and working memory. Eighty-nine individuals completed a test battery of potential human factors and, then, completed several tasks using a robotic manipulator designed to simulate find-and-fetch/pick-and-place tasks. During interaction with the robot, time on task, number of moves, and number of moves per minute were recorded. We successfully developed statistical models predicting performance that revealed several important human factors. Speed of information processing, spatial ability, dexterity, and working memory were all seen to be significant predictors of task performance. For time on task, linear and polynomial models showed roughly similar predictive performance on unseen test data achieving root-mean-square percentage error of about 7.3%; for number of moves per minute, a polynomial model was best with 9.1% error; and for number of moves, a linear model was best with 12.8% error.

Publication Date

12-1-2016

Publication Title

IEEE Transactions on Human-Machine Systems

Volume

46

Issue

6

Number of Pages

846-858

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/THMS.2016.2604366

Socpus ID

84988355072 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84988355072

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