Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Automated Derivation of Primitives for Movement Classification
Autonomous Robots
Cerebellar Contributions to Motor Timing: A PET Study of Auditory and Visual Rhythm Reproduction
Journal of Cognitive Neuroscience
A New Generative Feature Set Based on Entropy Distance for Discriminative Classification
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Consistency of functional learning methods based on derivatives
Pattern Recognition Letters
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For the past 10 years it has become clear that biological movement is made up of sub-routine type blocks, or motor primitives, with a central controller timing the activation of these blocks, creating synergies of muscle activation. This paper shows that it is possible to use a factorial hidden Markov model to infer primitives in handwriting data. These primitives are not predefined in terms of location of occurrence within the handwriting, and they are not limited or defined by a particular character set. Also, the variation in the data can to a large extent be explained by timing variation in the triggering of the primitives. Once an appropriate set of primitives has been inferred, the characters can be represented as a set of timings of primitive activations, along with variances, giving a very compact representation of the character. Separating the motor system into a motor primitive part, and a timing control gives us a possible insight into how we might create scribbles on paper.