A computational theory for movement pattern recognition based on optimal movement pattern generation

We have previously proposed an optimal trajectory and control theory for continuous movements, such as reaching or cursive handwriting. According to Marr's three-level description of brain function, our theory can be summarized as follows: (1) The computational theory is the minimum torque-change model; (2) the intermediate representation of a pattern is given as a set of via-points extracted from an example pattern; and (3) algorithm and hardware are provided by FIRM, a neural network that can generate and control minimum torque-change trajectories. In this paper, we propose a computational theory for movement pattern recognition that is based on our theory for optimal movement pattern generation. The three levels of the description of brain function in the recognition theory are tightly coupled with those for pattern generation. In recognition, the generation process and the recognition process are actually two flows of information in opposite directions within a single functional unit. In our theory, if the input movement trajectory data are identical to the optimal movement pattern reconstructed from an intermediate representation of some symbol, the input data are recognized as that symbol. If an error exists between the movement trajectory data and the generated trajectory, the putative symbol is corrected, and the generation is repeated. In particular, we present concrete computational procedures for the recognition of connected cursive handwritten characters, as well as for the estimation of phonemic timing in natural speech. Our most important contribution is to demonstrate the computational realizability for the ‘motor theory of movement pattern perception’: the movement-pattern recognition process can be realized by actively recruiting the movementpattern formation process. The way in which the formation process is utilized in pattern recognition in our theory suggests a duality between movement pattern formation and movement pattern perception.

[1]  A M Liberman,et al.  Perception of the speech code. , 1967, Psychological review.

[2]  J. Freyd,et al.  Representing the dynamics of a static form , 1983, Memory & cognition.

[3]  A. Liberman,et al.  The motor theory of speech perception revised , 1985, Cognition.

[4]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[5]  M. Babcock,et al.  Perception of dynamic information in static handwritten forms. , 1988, The American journal of psychology.

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

[7]  M. Kawato Motor theory of speech perception revisited from minimum-torque-change neural network model , 1989 .

[8]  Abhijit S. Pandya,et al.  Dynamic pattern recognition of coordinated biological motion , 1990, Neural Networks.

[9]  K. Shirai,et al.  Estimation of articulatory motion using neural networks , 1991 .

[10]  G Papcun,et al.  Inferring articulation and recognizing gestures from acoustics with a neural network trained on x-ray microbeam data. , 1992, The Journal of the Acoustical Society of America.

[11]  Yasuharu Koike,et al.  Physiologically Based Speech Synthesis , 1992, NIPS.

[12]  M. Kawato,et al.  Physiologically-Based Speech Synthesis Using Neural Networks (Special Section on Speech Synthesis: Current Technologies and Equipment) , 1993 .

[13]  Eric Vatikiotis-Bateson,et al.  Inverse Dynamics of Speech Motor Control , 1993, NIPS.

[14]  Mitsuo Kawato,et al.  A neural network model for arm trajectory formation using forward and inverse dynamics models , 1993, Neural Networks.

[15]  Mitsuo Kawato,et al.  TRAJECTORY FORMATION IN ARM MOVEMENTS: MINIMIZATION PRINCIPLES AND PROCEDURES , 1996 .

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

[17]  M. Kawato,et al.  Trajectory formation of arm movement by cascade neural network model based on minimum torque-change criterion , 1990, Biological Cybernetics.

[18]  Mitsuo Kawato,et al.  A theory for cursive handwriting based on the minimization principle , 1995, Biological Cybernetics.