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Predicting operator's cognitive and motion skills from joystick inputs

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationIECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE
Pages5935-5940
Number of pages6
ISBN (Electronic)978-1-5090-3474-1
DOIs
Publication statusPublished - 22 Dec 2016
Publication typeA4 Article in a conference publication
EventAnnual Conference of the IEEE Industrial Electronics Society -
Duration: 1 Jan 1900 → …

Publication series

Name
ISSN (Print)1553-572X

Conference

ConferenceAnnual Conference of the IEEE Industrial Electronics Society
Period1/01/00 → …

Abstract

The skill level of a human operator is crucial in operating a complicated process. In this paper, we pay particular attention to operating a forest harvester. A simple computer game simulates the operation of a harvester as well as collects input data from the player and output data from the simulation model. First, we study the nature of the input and output data and illustrate them using PCA. Then, we proceed to using only input data and train a neural network model from operator inputs to skill level. Results show that the skill can be predicted reasonably well. The model itself is static, but dynamics are captured using specific indicators. Using bare input data simplifies data collection and makes the prediction faster. We do not have to use data that depend on the machine or environment, and the skill level can be predicted soon after the operator grabs the controls. The next phase will be using the skill information for operation support.

Keywords

  • Artificial neural networks, Size measurement

Publication forum classification

Field of science, Statistics Finland