Customized optimal filter for eliminating operator's tremor

Remote manually operated tasks such as those found in teleoperation, virtual reality, or joystick-based computer access, require the generation of an intermediate signal which is transmitted to the controlled subsystem (robot arm, virtual environment or cursor). When man-machine movements are distorted by tremor, performance can be improved by digitally filtering the intermediate signal before it reaches the controlled device. This paper introduces a novel filtering framework in which digital equalizers are optimally designed after pursuit tracking task experiments. Due to inherent properties of the man-machine system, the design of tremor suppression equalizers presents two serious problems: (1) performance criteria leading to optimizations that minimize mean-squared error are not efficient for tremor elimination, and (2) movement signals show highly ill-conditioned autocorrelation matrices, which often result in useless or unstable solutions. A new performance indicator is introduced, namely the F-MSE _d, and the optimal equalizer according to this new criterion is developed. Ill-condition of the autocorrelation matrix is overcome using a novel method which we call pulled-optimization. Experiments performed with both a person with tremor disability, and a vibration inducing device, show significant results.