Predicting operator's cognitive and motion skills from joystick inputs

Mikko Laurikkala, Satoshi Suzuki, Matti Vilkko

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

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of the IECON 2016 - 42nd Annual Conference of the Industrial Electronics Society
PublisherIEEE Institute of Electrical and Electronic Engineers
ISBN (Electronic)978-1-5090-3474-1
ISBN (Print)978-1-5090-3475-8
Publication statusPublished - 21 Dec 2016
MoE publication typeA4 Article in a conference publication
Event42nd Conference of the Industrial Electronics Society, IECON 2016 - Florence, Italy
Duration: 24 Oct 201627 Oct 2016


Conference42nd Conference of the Industrial Electronics Society, IECON 2016


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