Haptic error fields for robotic training

Moria E. Fisher, Felix C. Huang, Verena Klamroth-Marganska, Robert Riener, James L. Patton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


Error feedback is critical for supporting motor adaptation in rehabilitation, sports, piloting, and skilled manual tasks. Error augmentation interventions, in which participants' errors are amplified with either visual or haptic feedback during training has shown success over repetitive practice. Here we show that the statistical tendencies arising from free movement exploration can improve error augmentation with customized training forces that vary across the trajectory. We hypothesized that with customized error augmentation participants will adapt faster to learning a visual-motor distortion and have greater improvement than participants receiving standard error augmentation and participants repetitively practicing the task. We tested twenty-one participants using a robotic exoskeleton device restricted to two degrees of freedom. We found that participants receiving customized forces adapted faster and consequently changed with smaller forces. Further, change in error was greatest for participants receiving customized forces. These promising results support the need for customization to target subject specific errors.

Original languageEnglish (US)
Title of host publicationIEEE World Haptics Conference, WHC 2015
EditorsJ. Edward Colgate, Hong Z. Tan, Hong Z. Tan, Seungmoon Choi, Gregory J. Gerling
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479966240
StatePublished - Aug 4 2015
Event10th IEEE World Haptics Conference, WHC 2015 - Evanston, United States
Duration: Jun 22 2015Jun 26 2015

Publication series

NameIEEE World Haptics Conference, WHC 2015


Other10th IEEE World Haptics Conference, WHC 2015
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction


Dive into the research topics of 'Haptic error fields for robotic training'. Together they form a unique fingerprint.

Cite this