A probabilistic algorithm for estimating the intention of computer users with movement disorders

Standard computer interfaces are inefficient for many users who have movement disorders. We believe this stems from the standard view that interface devices accurately represent the intention of the user. For those who have movement disorders, such as dystonia, it may be more effective to view the computer interface as an instrument used to estimate the user's intent. We develop a probabilistic algorithm to design an estimation device for computer input. We also implement the algorithm as part of text-entry software and perform a preliminary case study on a patient with dystonia, using a joystick as a text input device.

[1]  Alan F. Blackwell,et al.  Dasher: A Gesture-Driven Data Entry Interface for Mobile Computing , 2002 .

[2]  M. Hallett,et al.  Classification and definition of disorders causing hypertonia in childhood. , 2003, Pediatrics.

[3]  Teresa H. Y. Meng,et al.  Model-based neural decoding of reaching movements: a maximum likelihood approach , 2004, IEEE Transactions on Biomedical Engineering.

[4]  T. Sanger Arm Trajectories in Dyskinetic Cerebral Palsy Have Increased Random Variability , 2006, Journal of child neurology.

[5]  Leonard J. West,et al.  The Standard and Dvorak Keyboards Revisited: Direct Measures of Speed , 1998 .

[6]  W Hulstijn,et al.  Manual dexterity and keyboard use in spastic hemiparesis: a comparison between the impaired hand and the ‘good’ hand on a number of performance measures , 1998, Clinical rehabilitation.

[7]  Byron M. Yu,et al.  A high-performance brain–computer interface , 2006, Nature.

[8]  Mohamed A. El-Gebeily,et al.  Approximate solution of the Fokker‐Planck‐Kolmogorov equation by finite elements , 1994 .

[9]  Subhash Challa,et al.  Nonlinear filter design using Fokker-Planck-Kolmogorov probability density evolutions , 2000, IEEE Trans. Aerosp. Electron. Syst..

[10]  T. Sanger Pediatric movement disorders , 2003, Current opinion in neurology.

[11]  Terence D Sanger,et al.  Bayesian filtering of myoelectric signals. , 2007, Journal of neurophysiology.

[12]  Byron M. Yu,et al.  Mixture of Trajectory Models for Neural Decoding of Goal-directed Movements a Computational Model of Craving and Obsession Decoding Visual Inputs from Multiple Neurons in the Human Temporal Lobe Encoding Contribution of Individual Retinal Ganglion Cell Responses to Velocity and Acceleration , 2008 .