Arm motion reconstruction via feature clustering in joint angle space

Jack DiGiovanna, Justin C. Sanchez, B. J. Fregly, Jose C. Principe

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

Abstract

We hypothesize that a set of movemes can be used to reconstruct bio mechanically realistic movements. Using parameters from a reaching and grasping task we create a representative three-dimensional motion. From this motion we extract features from the joint angle space. We believe that the physiological importance of these features makes them worth investigating as possible movemes. Machine learning techniques are employed to cluster similar features. The clusters are then used to recursively reconstruct the motion trajectory. Even with only twenty clusters, the average trajectory reconstruction error in Cartesian space is less than 1% of the dynamic range of motion. Our ability to create and analyze realistic motions may be crucial to both future BMI experiments where a desired signal is not available and our understanding of motor control.

Original languageEnglish (US)
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
Pages4678-4683
Number of pages6
StatePublished - Dec 1 2006
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: Jul 16 2006Jul 21 2006

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Other

OtherInternational Joint Conference on Neural Networks 2006, IJCNN '06
CountryCanada
CityVancouver, BC
Period7/16/067/21/06

ASJC Scopus subject areas

  • Software

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    DiGiovanna, J., Sanchez, J. C., Fregly, B. J., & Principe, J. C. (2006). Arm motion reconstruction via feature clustering in joint angle space. In International Joint Conference on Neural Networks 2006, IJCNN '06 (pp. 4678-4683). [1716749] (IEEE International Conference on Neural Networks - Conference Proceedings).