title: Multistep prediction of physiological tremor based on machine learning for robotics assisted microsurgery.
abstract: For effective tremor compensation in robotics assisted hand-held device, accurate filtering of tremulous motion is necessary. The time-varying unknown phase delay that arises due to both software (filtering) and hardware (sensors) in these robotics instruments adversely affects the device performance. In this paper, moving window-based least squares support vector machines approach is formulated for multistep prediction of tremor to overcome the time-varying delay. This approach relies on the kernel-learning technique and does not require the knowledge of prediction horizon compared to the existing methods that require the delay to be known as a priori. The proposed method is evaluated through simulations and experiments with the tremor data recorded from surgeons and novice subjects. Comparison with the state-of-the-art techniques highlights the suitability and better performance of the proposed method.
authors.forename: Sivanagaraja
authors.initials: S
authors.lastname: Tatinati
authors.forename: Kalyana C
authors.initials: KC
authors.lastname: Veluvolu
authors.forename: Wei Tech
authors.initials: WT
authors.lastname: Ang
mesh_terms: D003198:Computer Simulation; D006801:Humans; D008866:Microsurgery / Q000379:methods*; D015233:Models, Statistical; D065287:Robotic Surgical Procedures / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D014202:Tremor / Q000503:physiopathology*