- Abstract:
-
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.
- Links To Paper
- 1st Link
- Bibtex format
- @InProceedings{EDI-INF-RR-0847,
- author = {
Benjamin Williams
and Marc Toussaint
and Amos Storkey
},
- title = {Extracting Motion Primitives from Natural Handwriting Data},
- book title = {Proceedings of the International Conference on Artificial Neural Networks},
- year = 2006,
- url = {http://www.anc.ed.ac.uk/~amos/publications/WilliamsToussaintStorkey2006ExtractingMotionPrimitives.pdf},
- }
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