NDLI: Arm Motion Reconstruction via Feature Clustering in Joint Angle Space

Content Provider	IEEE Xplore Digital Library
Author	DiGiovanna, J. Sanchez, J.C. Fregly, B.J. Principe, J.C.
Copyright Year	2006
Description	Author affiliation: Florida Univ., Gainesville (DiGiovanna, J.)
Abstract	We hypothesize that a set of movements can be used to reconstruct biomechanically 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 movements. 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.
Starting Page	4678
Ending Page	4683
File Size	275820
Page Count	6
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.247120
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Motor drives Biomedical engineering Muscles Biological system modeling Supervised learning Speech processing Feature extraction Machine learning Dynamic range Signal analysis
Content Type	Text
Resource Type	Article

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