Human-Centric Predictive Model of Task Difficulty for Human-In-The-Loop Control Tasks

Simple item page
dc.contributor.authorWang, Ziheng
dc.contributor.authorFey, Ann Majewicz
dc.contributor.utdAuthorWang, Ziheng
dc.contributor.utdAuthorFey, Ann Majewicz
dc.date.accessioned2019-06-28T21:42:48Z
dc.date.available2019-06-28T21:42:48Z
dc.date.created2018-04-05
dc.description.abstractQuantitatively measuring the difficulty of a manipulation task in human-in-the-loop control systems is ill-defined. Currently, systems are typically evaluated through task-specific performance measures and post-experiment user surveys; however, these methods do not capture the real-time experience of human users. In this study, we propose to analyze and predict the difficulty of a bivariate pointing task, with a haptic device interface, using human-centric measurement data in terms of cognition, physical effort, and motion kinematics. Noninvasive sensors were used to record the multimodal response of human user for 14 subjects performing the task. A data-driven approach for predicting task difficulty was implemented based on several task-independent metrics. We compare four possible models for predicting task difficulty to evaluated the roles of the various types of metrics, including: (I) a movement time model, (II) a fusion model using both physiological and kinematic metrics, (III) a model only with kinematic metrics, and (IV) a model only with physiological metrics. The results show significant correlation between task difficulty and the user sensorimotor response. The fusion model, integrating user physiology and motion kinematics, provided the best estimate of task difficulty (R² = 0.927), followed by a model using only kinematic metrics (R² = 0.921). Both models were better predictors of task difficulty than the movement time model (R² = 0.847), derived from Fitt’s law, a well studied difficulty model for human psychomotor control.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.description.sponsorshipNational Center for Advancing Translational Sciences of the National Institutes of Health under award Number UL1TR001105
dc.identifier.bibliographicCitationWang, Z., and A. M. Fey. 2018. "Human-centric predictive model of task difficulty for human-in-the-loop control tasks." PLOS One 13(4), doi:10.1371/journal.pone.0195053
dc.identifier.issue4
dc.identifier.urihttps://hdl.handle.net/10735.1/6666
dc.identifier.volume13
dc.language.isoen
dc.publisherPublic Library of Science
dc.relation.urihttp://dx.doi.org/10.1371/journal.pone.0195053
dc.rightsCC BY 4.0 (Attribution)
dc.rights©2018 The Authors
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.source.journalPLOS One
dc.subjectHuman-computer interaction
dc.subjectHaptic devices
dc.subjectMotor ability--Testing
dc.subjectPsychomotor Performance
dc.titleHuman-Centric Predictive Model of Task Difficulty for Human-In-The-Loop Control Tasks
dc.type.genrearticle

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
JECS-6509-9584.58.pdf
Size:
14.4 MB
Format:
Adobe Portable Document Format
Description:
Article
Download

Collections

Fey, Ann Majewicz