Adapting human-machine interfaces to user performance.

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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