Arm Motion Reconstruction via Feature Clustering in Joint Angle Space

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.

[1]  A. Guyton,et al.  Textbook of Medical Physiology , 1961 .

[2]  M. Nagurka,et al.  Fourier-Based Optimal Control of Nonlinear Dynamic Systems , 1990 .

[3]  P. Morasso,et al.  Trajectory formation and handwriting: A computational model , 1982, Biological Cybernetics.

[4]  Mitsuo Kawato,et al.  Internal models for motor control and trajectory planning , 1999, Current Opinion in Neurobiology.

[5]  Zoubin Ghahramani,et al.  Unsupervised learning of sensory-motor primitives , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[6]  Reza Shadmehr,et al.  Internal models of limb dynamics and the encoding of limb state , 2005, Journal of neural engineering.

[7]  宇野 洋二,et al.  Formation and control of optimal trajectory in human multijoint arm movement : minimum torque-change model , 1988 .

[8]  D. Klatt,et al.  Structure of a phonological rule component for a synthesis-by-rule program , 1976 .

[9]  Rieko Osu,et al.  Quantitative examinations for multi joint arm trajectory planning--using a robust calculation algorithm of the minimum commanded torque change trajectory , 2001, Neural Networks.

[10]  M. Kawato,et al.  Formation and control of optimal trajectory in human multijoint arm movement , 1989, Biological Cybernetics.

[11]  J.C. Sanchez,et al.  Learning the contributions of the motor, premotor, and posterior parietal cortices for hand trajectory reconstruction in a brain machine interface , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..

[12]  Kip A Ludwig,et al.  Naïve coadaptive cortical control , 2005, Journal of neural engineering.

[13]  Jerald D. Kralik,et al.  Real-time prediction of hand trajectory by ensembles of cortical neurons in primates , 2000, Nature.

[14]  David M. Santucci,et al.  Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates , 2003, PLoS biology.

[15]  S L Delp,et al.  A graphics-based software system to develop and analyze models of musculoskeletal structures. , 1995, Computers in biology and medicine.

[16]  Carlo L. Bottasso,et al.  A Method for Inferring the Optimization Cost Function of Experimentally Observed Motor Strategies , 2005 .

[17]  Dawn M. Taylor,et al.  Extraction algorithms for cortical control of arm prosthetics , 2001, Current Opinion in Neurobiology.

[18]  D M Wolpert,et al.  Multiple paired forward and inverse models for motor control , 1998, Neural Networks.

[19]  A. J. van den Bogert,et al.  Human muscle modelling from a user's perspective. , 1998, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[20]  John J. Craig,et al.  Introduction to Robotics Mechanics and Control , 1986 .

[21]  John J. Craig,et al.  Introduction to robotics - mechanics and control (2. ed.) , 1989 .

[22]  Deniz Erdogmus,et al.  Input-output mapping performance of linear and nonlinear models for estimating hand trajectories from cortical neuronal firing patterns , 2002, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing.

[23]  Kurt A. Thoroughman,et al.  combination of motor primitives , 2000 .

[24]  Scott L. Delp,et al.  A Model of the Upper Extremity for Simulating Musculoskeletal Surgery and Analyzing Neuromuscular Control , 2005, Annals of Biomedical Engineering.