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A Bayesian computational model for online character recognition and disability assessment during cursive eye writing.

Diard J, Rynik V, Lorenceau J - Front Psychol (2013)

Bottom Line: Preliminary experimental results are presented, which illustrate the method, showing the feasibility of character recognition in the context of eye writing.We then show experimentally how a model of the unknown character can be used to detect trajectories that are likely to be new symbols, and how disability assessment can be performed by opportunistically observing characteristics of fine motor control, as letter are being traced.Experimental analyses also help identify specificities of eye writing, as compared to handwriting, and the resulting technical challenges.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire de Psychologie et NeuroCognition, Université Grenoble Alpes-CNRS Grenoble, France.

ABSTRACT
This research involves a novel apparatus, in which the user is presented with an illusion inducing visual stimulus. The user perceives illusory movement that can be followed by the eye, so that smooth pursuit eye movements can be sustained in arbitrary directions. Thus, free-flow trajectories of any shape can be traced. In other words, coupled with an eye-tracking device, this apparatus enables "eye writing," which appears to be an original object of study. We adapt a previous model of reading and writing to this context. We describe a probabilistic model called the Bayesian Action-Perception for Eye On-Line model (BAP-EOL). It encodes probabilistic knowledge about isolated letter trajectories, their size, high-frequency components of the produced trajectory, and pupil diameter. We show how Bayesian inference, in this single model, can be used to solve several tasks, like letter recognition and novelty detection (i.e., recognizing when a presented character is not part of the learned database). We are interested in the potential use of the eye writing apparatus by motor impaired patients: the final task we solve by Bayesian inference is disability assessment (i.e., measuring and tracking the evolution of motor characteristics of produced trajectories). Preliminary experimental results are presented, which illustrate the method, showing the feasibility of character recognition in the context of eye writing. We then show experimentally how a model of the unknown character can be used to detect trajectories that are likely to be new symbols, and how disability assessment can be performed by opportunistically observing characteristics of fine motor control, as letter are being traced. Experimental analyses also help identify specificities of eye writing, as compared to handwriting, and the resulting technical challenges.

No MeSH data available.


Related in: MedlinePlus

Illustration of the filtering and trimming of trajectories. Left: Raw trajectory for a “y” exemplar (blue dots), and the resulting filtered and trimmed trajectory (red dots). Right: acceleration profile of the raw trajectory. Spurious saccades in movement initiation correspond to easily detected and filtered peaks.
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Figure 4: Illustration of the filtering and trimming of trajectories. Left: Raw trajectory for a “y” exemplar (blue dots), and the resulting filtered and trimmed trajectory (red dots). Right: acceleration profile of the raw trajectory. Spurious saccades in movement initiation correspond to easily detected and filtered peaks.

Mentions: This trajectory trimming process is robust with respect to the chosen threshold value for acceleration. Figure 4 shows a typical acceleration profile for a raw trajectory in the database: saccades correspond to large peaks in acceleration amplitude. Any threshold value between 0.3 and 1 space-unit/time_unit2would roughly yield identical trimmed trajectories from the raw trajectories in our database. This trimming process is, however, slightly sensitive to the number of points that can be trimmed at the beginnings and ends of trajectories: the trade-off between data clean-up and conservation of information was set empirically once, achieving satisfactory compromise, without further parameter fiddling.


A Bayesian computational model for online character recognition and disability assessment during cursive eye writing.

Diard J, Rynik V, Lorenceau J - Front Psychol (2013)

Illustration of the filtering and trimming of trajectories. Left: Raw trajectory for a “y” exemplar (blue dots), and the resulting filtered and trimmed trajectory (red dots). Right: acceleration profile of the raw trajectory. Spurious saccades in movement initiation correspond to easily detected and filtered peaks.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3822325&req=5

Figure 4: Illustration of the filtering and trimming of trajectories. Left: Raw trajectory for a “y” exemplar (blue dots), and the resulting filtered and trimmed trajectory (red dots). Right: acceleration profile of the raw trajectory. Spurious saccades in movement initiation correspond to easily detected and filtered peaks.
Mentions: This trajectory trimming process is robust with respect to the chosen threshold value for acceleration. Figure 4 shows a typical acceleration profile for a raw trajectory in the database: saccades correspond to large peaks in acceleration amplitude. Any threshold value between 0.3 and 1 space-unit/time_unit2would roughly yield identical trimmed trajectories from the raw trajectories in our database. This trimming process is, however, slightly sensitive to the number of points that can be trimmed at the beginnings and ends of trajectories: the trade-off between data clean-up and conservation of information was set empirically once, achieving satisfactory compromise, without further parameter fiddling.

Bottom Line: Preliminary experimental results are presented, which illustrate the method, showing the feasibility of character recognition in the context of eye writing.We then show experimentally how a model of the unknown character can be used to detect trajectories that are likely to be new symbols, and how disability assessment can be performed by opportunistically observing characteristics of fine motor control, as letter are being traced.Experimental analyses also help identify specificities of eye writing, as compared to handwriting, and the resulting technical challenges.

View Article: PubMed Central - PubMed

Affiliation: Laboratoire de Psychologie et NeuroCognition, Université Grenoble Alpes-CNRS Grenoble, France.

ABSTRACT
This research involves a novel apparatus, in which the user is presented with an illusion inducing visual stimulus. The user perceives illusory movement that can be followed by the eye, so that smooth pursuit eye movements can be sustained in arbitrary directions. Thus, free-flow trajectories of any shape can be traced. In other words, coupled with an eye-tracking device, this apparatus enables "eye writing," which appears to be an original object of study. We adapt a previous model of reading and writing to this context. We describe a probabilistic model called the Bayesian Action-Perception for Eye On-Line model (BAP-EOL). It encodes probabilistic knowledge about isolated letter trajectories, their size, high-frequency components of the produced trajectory, and pupil diameter. We show how Bayesian inference, in this single model, can be used to solve several tasks, like letter recognition and novelty detection (i.e., recognizing when a presented character is not part of the learned database). We are interested in the potential use of the eye writing apparatus by motor impaired patients: the final task we solve by Bayesian inference is disability assessment (i.e., measuring and tracking the evolution of motor characteristics of produced trajectories). Preliminary experimental results are presented, which illustrate the method, showing the feasibility of character recognition in the context of eye writing. We then show experimentally how a model of the unknown character can be used to detect trajectories that are likely to be new symbols, and how disability assessment can be performed by opportunistically observing characteristics of fine motor control, as letter are being traced. Experimental analyses also help identify specificities of eye writing, as compared to handwriting, and the resulting technical challenges.

No MeSH data available.


Related in: MedlinePlus