Arm motion reconstruction via feature clustering in joint angle space

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

doi:10.1109/ijcnn.2006.247120

Arm motion reconstruction via feature clustering in joint angle space

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

Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	International Joint Conference on Neural Networks 2006, IJCNN '06
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4678-4683
Number of pages	6
ISBN (Print)	0780394909, 9780780394902
DOIs	https://doi.org/10.1109/ijcnn.2006.247120
State	Published - 2006
Event	International Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada Duration: Jul 16 2006 → Jul 21 2006

Publication series

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

Other

Other	International Joint Conference on Neural Networks 2006, IJCNN '06
Country/Territory	Canada
City	Vancouver, BC
Period	7/16/06 → 7/21/06

ASJC Scopus subject areas

Software

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10.1109/ijcnn.2006.247120

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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). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ijcnn.2006.247120

Arm motion reconstruction via feature clustering in joint angle space. / DiGiovanna, Jack; Sanchez, Justin C.; Fregly, B. J.; Principe, Jose C.

International Joint Conference on Neural Networks 2006, IJCNN '06. Institute of Electrical and Electronics Engineers Inc., 2006. p. 4678-4683 1716749 (IEEE International Conference on Neural Networks - Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

DiGiovanna, J, Sanchez, JC, Fregly, BJ & Principe, JC 2006, Arm motion reconstruction via feature clustering in joint angle space. in International Joint Conference on Neural Networks 2006, IJCNN '06., 1716749, IEEE International Conference on Neural Networks - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 4678-4683, International Joint Conference on Neural Networks 2006, IJCNN '06, Vancouver, BC, Canada, 7/16/06. https://doi.org/10.1109/ijcnn.2006.247120

DiGiovanna J, Sanchez JC, Fregly BJ, Principe JC. Arm motion reconstruction via feature clustering in joint angle space. In International Joint Conference on Neural Networks 2006, IJCNN '06. Institute of Electrical and Electronics Engineers Inc. 2006. p. 4678-4683. 1716749. (IEEE International Conference on Neural Networks - Conference Proceedings). https://doi.org/10.1109/ijcnn.2006.247120

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AB - 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.

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Arm motion reconstruction via feature clustering in joint angle space

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