Publikation

Online Movement Prediction in a Robotic Application Scenario

Anett Seeland, Hendrik Wöhrle, Sirko Straube, Elsa Andrea Kirchner

In: Proceedings of the 6th International IEEE EMBS Conference on Neural Engineering. International IEEE/EMBS Conference on Neural Engineering (NER-2013) November 6-8 San Diego CA United States Seiten 41-44 11/2013.

Abstrakt

Current movement prediction systems based on electroencephalography were mainly developed and evaluated in highly controlled scenarios, in which subjects concentrate only on the desired task with as few as possible disturbing sources present. However, it has not been addressed sufficiently how the suggested methods perform in more complex and uncontrolled environments. In this work we predict arm movements online in a robotic teleoperation scenario and present a completely online running methodology. The system is evaluated on ten sessions from three subjects. Evaluation criteria are the overall classification performance and the success in predicting an upcoming movement in the application. Our results confirm that it is possible to predict movements in less restricted applications motivating the transfer of these methods to real world applications.

Projekte

Weitere Links

130808_Online_Movement_Prediction_in_a_Robotic_Application_Scenario_NER_Seeland.pdf (pdf, 395 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence