Optimal digital filtering for tremor suppression

Remote manually operated tasks such as those found in teleoperation, virtua l reality, or joystick-based computer access, require the generation of an intermediate electrical signal which is transmitted to the controlled subsy stem (robot arm, virtual environment, or a cursor in a computer screen). Wh en human movements are distorted, for instance, by tremor, performance can be improved by digitally filtering the intermediate signal before it reache s the controlled device, This paper introduces a novel tremor filtering fra mework in which digital equalizers are optimally designed through pursuit t racking task experiments. Due to inherent properties of the man-machine system, the design of tremor suppression equalizers presents two serious problems: 1) performance criter ia leading to optimizations that minimize mean-squared error are not effici ent for tremor elimination and 2) movement signals show ill-conditioned aut ocorrelation matrices, which often result in useless or unstable solutions. To address these problems, a new performance indicator in the context of t remor is introduced, and the optimal equalizer according to this new criter ion is developed. Ill-conditioning of the autocorrelation matrix is overcom e using a novel method which we call pulled-optimization. Experiments perfo rmed with artificially induced vibrations and a subject with Parkinson's di sease show significant improvement in performance. Additional results, alon g with MATLAB source code of the algorithms, and a customizable demo for PC joysticks, are available on the Internet at http://tremor-suppression.com.