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Download fileHuman response delay estimation and monitoring using gamma distribution analysis
conference contribution
posted on 2021-08-03, 15:18 authored by Eve ZhangEve Zhang, Miguel Martinez-GarciaMiguel Martinez-Garcia, Timothy GordonThe aim of this paper is to estimate and monitor the human response delay in manual control tasks. A probability distribution analysis is applied on the response delay, based on experimental data collected from human subjects controlling a dynamic system and responding to visually perceived errors via joystick or steering wheel. The distribution analysis includes firstly a sliding segment method, to extract the delay time for each slice of data. Then, probability distributions of the delay time are fitted by using a bootstrap based goodness-of-fit test. For both manual-control cases, with a joystick and a steering wheel respectively, the experimental data can be explained reasonably by a Gamma distribution. Consequently, the Gamma distribution parameters for different human subjects are compared. Based on these findings, an online monitoring method of the level of attention in the human-operator - or applied workload - is proposed, which could be of interest for relevant shared-control applications.
History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Aeronautical and Automotive Engineering
Published in
2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)Pages
807 - 812Source
2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)Publisher
IEEEVersion
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Publication date
2019-01-17Copyright date
2018ISBN
9781538666500eISSN
2577-1655Publisher version
Language
- en