Adapting human-machine interfaces to user performance.
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.
Danziger
Zachary
Z
Northwestern University, Evanston, IL 60208 USA. ZacharyDanziger2011@u.northwestrn.edu
Fishbach
Alon
A
Mussa-Ivaldi
Ferdinando A
FA
D000465:Algorithms; D001185:Artificial Intelligence; D001676:Biofeedback, Psychology / Q000502:physiology; D002540:Cerebral Cortex / Q000503:physiopathology*; D003143:Communication Devices for People with Disabilities; D004569:Electroencephalography / Q000295:instrumentation*; D005071:Evoked Potentials / Q000502:physiology; D006801:Humans; D015233:Models, Statistical; D009068:Movement; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation; D012984:Software; D014584:User-Computer Interface