Physiological Tremor Estimation With Autoregressive (AR) Model and Kalman Filter for Robotics Applications

Abstract

This paper focuses on developing a simple and efficient tremor estimation algorithm suitable for real-time applications. Autoregressive model in combination with Kalman filter is employed for tremor estimation in robotics devices. A research is conducted with tremor data recorded from surgeons and novice subjects for model identification and characteristics. Results show that appropriate choice of model parameters improves the estimation accuracy. Comparison with existing tremor estimation methods is performed to analyze the performance. Experimental results for 1-DOF tremor estimation are provided to validate the approach.