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ABSTRACT: People with disabilities such as multiple sclerosis and Parkinson's disease have difficulty operating conventional movement-sensing joysticks (MSJs) because of varying levels of tremor. We developed an isometric joystick (IJ) that has performed as well as a conventional MSJ when used by persons with upper-limb impairments in real and virtual wheelchair driving tasks. The Weighted-Frequency Fourier Linear Combiner (WFLC) filter has been used to cancel tremor effectively in microsurgery. In this study, we compared an MSJ, IJ, and IJ with the WFLC filter in individuals performing a virtual driving task. Although the WFLC filter did not improve driving performance in this study, the IJ without a filter yielded better results than the conventional MSJ and thus may be a potential alternative to the MSJ in minimizing the effects of tremor.
The Journal of Rehabilitation Research and Development 02/2009; 46(2):269-75. · 1.78 Impact Factor
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2008 IEEE International Conference on Robotics and Automation, ICRA 2008, May 19-23, 2008, Pasadena, California, USA; 01/2008
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ABSTRACT: People with athetoid cerebral palsy (CP) have difficulty using computers due to unintentional involuntary movements in the upper extremities. A neural network-based system has been developed to cancel the undesired motion, and speed up the movements and accuracy in target acquisition and path tracking tasks while using an isometric joystick (IJ). Nonlinear filtering algorithms were created with neural networks using nonlinear models to help people with athetoid CP to access the computer. This paper presents unfiltered test data that have been collected from patients, and describes the planned filtering approach.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2007; 2007:1434-6.
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ABSTRACT: This paper presents a novel approach to modeling and filtering of athetoid motion for use in assistive computer interfaces during targeting tasks such as clicking on icons. Data were recorded from a subject with athetosis during unassisted icon-clicking trials with an isometric joystick. In order to facilitate development and preliminary testing of filter designs without the cost and difficulty of repeated testing with subjects with athetosis, a quantitative model of the recorded subject data was developed using pseudoinverse methods. Using this model within the visuomotor control loop for the icon-clicking task, a prediction filter was then developed to reduce the target acquisition time for the user. The filter consists of an "autoregressive stretching window" system which selects five data points evenly distributed across the input and output histories to predict the intended target, together with a second-order system that smoothes the movement of the cursor. The filter has demonstrated a reduction of up to 49% in target acquisition time in preliminary experiments with the athetoid model.
