Multi-step tremor prediction autoregressive (AR) model and Kalman filter (KF) for surgical robotic applications

摘要

This paper focus on developing computationally simple and efficient tremor estimation algorithm suitable for real-time applications. Autoregressive (AR) model in combination with Kalman filter (KF) is employed for tremor estimation in surgical robotics devices. A study 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. Experimental results for 1-DOF tremor estimation are provided to validate the approach.