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 |
|---|---|
| Title of host publication | IEEE International Conference on Neural Networks - Conference Proceedings |
| Pages | 4678-4683 |
| Number of pages | 6 |
| State | Published - Dec 1 2006 |
| Externally published | Yes |
| Event | International Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada Duration: Jul 16 2006 → Jul 21 2006 |
Other
| Other | International Joint Conference on Neural Networks 2006, IJCNN '06 |
|---|---|
| Country | Canada |
| City | Vancouver, BC |
| Period | 7/16/06 → 7/21/06 |
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ASJC Scopus subject areas
- Software
Cite this
Arm motion reconstruction via feature clustering in joint angle space. / DiGiovanna, Jack; Sanchez, Justin C.; Fregly, B. J.; Principe, Jose C.
IEEE International Conference on Neural Networks - Conference Proceedings. 2006. p. 4678-4683 1716749.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Arm motion reconstruction via feature clustering in joint angle space
AU - DiGiovanna, Jack
AU - Sanchez, Justin C.
AU - Fregly, B. J.
AU - Principe, Jose C.
PY - 2006/12/1
Y1 - 2006/12/1
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=40649128319&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=40649128319&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:40649128319
SN - 0780394909
SN - 9780780394902
SP - 4678
EP - 4683
BT - IEEE International Conference on Neural Networks - Conference Proceedings
ER -