Age and gender differences in performance for operating a robotic manipulator

Nicholas Paperno, Michael A. Rupp, Eva L. Parkhurst, Edgard M. Maboudou-Tchao, Janan Al Awar Smither, John Bricout, Aman Behal

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This paper examines the performance differences across gender and age when operating a robotic manipulator arm and also seeks to determine which human factors are considered important predictors of performance for each group. To examine these differences, 93 participants were recruited and divided up into both male (46) and female (47) as well as young (46) and old (47). While men and women had nearly identical human factors, except for women exhibiting better dexterity, different navigation strategies were utilized by the genders leading men to perform the tasks quicker and with less overall moves than women. While task completion speed was affected most by working memory (WM) and spatial abilities for men, it was seen to be mostly dependent on physical abilities for women. Substantial differences were seen between the age cohorts in WM and dexterity which resulted in the younger cohort completing tasks quicker and with a higher rate of commands than the older cohort; no difference was observed in the total number of moves which provided evidence of a similar navigation strategy across the age groups. To improve task speed performance, older adults used all facets of their information processing and spatial abilities as compared to the younger group who used a narrower subset. To compensate for the aforementioned variations in important human factors, human-computer interface design considerations are suggested.

Original languageEnglish (US)
Article number8657750
Pages (from-to)137-149
Number of pages13
JournalIEEE Transactions on Human-Machine Systems
Volume49
Issue number2
DOIs
StatePublished - Apr 2019

Bibliographical note

Funding Information:
Manuscript received July 13, 2017; revised February 16, 2018 and September 6, 2018; accepted November 17, 2018. Date of publication March 1, 2019; date of current version March 13, 2019. This work was supported in part by the NIDILRR Grant H133G120275 and in part by the NSF Grant IIS-1409823 and Grant IIS-1527794. This paper was recommended by Associate Editor J. Yang. (Corresponding author: Aman Behal.) N. Paperno is with the U.S. Patent and Trademark Office, Alexandria, VA 22314 USA (e-mail:,nickpaper4828@gmail.com).

Keywords

  • Bayesian analysis
  • human factors
  • human-robot interaction
  • machine learning

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