Adapting human-machine interfaces to user performance

Author

Danziger, Zachary ; Fishbach, Alon ; Mussa-Ivaldi, Ferdinando A.

Author_Institution

Northwestern University, Evanston, IL 60208 USA

fYear

2008

fDate

20-25 Aug. 2008

Firstpage

4486

Lastpage

4490

Abstract

The goal of this study was to create and examine machine learning algorithms that adapt in a controlled and cadenced way to foster a harmonious learning environment between the user of a human-machine interface and the controlled device. In this experiment, subjects´ high-dimensional finger motions remotely controlled the joint angles of a simulated planar 2-link arm, which was used to hit targets on a computer screen. Subjects were required to move the cursor at the endpoint of the simulated arm.

Keywords

Computational modeling; Computer simulation; Control systems; Error correction; Fingers; Least squares approximation; Machine learning; Machine learning algorithms; Man machine systems; Testing; Algorithms; Artificial Intelligence; Biofeedback (Psychology); Cerebral Cortex; Communication Aids for Disabled; Electroencephalography; Evoked Potentials; Humans; Models, Statistical; Movement; Neural Networks (Computer); Reproducibility of Results; Signal Processing, Computer-Assisted; Software; User-Computer Interface;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE

Conference_Location

Vancouver, BC

ISSN

1557-170X

Print_ISBN

978-1-4244-1814-5

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2008.4650209

Filename

4650209