Multi-step prediction of physiological tremor with random quaternion neurons for surgical robotics applications

• Main Authors: Wang, Yubo, Tatinati, Sivanagaraja, Adhikari, Kabita, Huang, Liyu, Nazarpour, Kianoush, Ang, Wei Tech, Veluvolu, Kalyana C.
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2018
• Subjects: Surgical Robotics; Physiological Tremor; DRNTU::Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/88364; http://hdl.handle.net/10220/45765
• Institution: Nanyang Technological University

Digital filters are employed in hand-held robotic instruments to separate the concomitant involuntary physiological tremor motion from the desired motion of micro-surgeons. Inherent phase-lag in digital filters induces phase distortion (time-lag/delay) into the separated tremor motion and it adversely affects the final tremor compensation. Owing to the necessity of digital filters in hand-held instruments, multi-step prediction of physiological tremor motion is proposed as a solution to counter the induced delay. In this paper, a quaternion variant for extreme learning machines (QELMs) is developed for multi-step prediction of the tremor motion. The learning paradigm of the QELM integrates the identified underlying relationship from 3-D tremor motion in the Hermitian space with the fast learning merits of ELMs theories to predict the tremor motion for a known horizon. Real tremor data acquired from micro-surgeons and novice subjects are employed to validate the QELM for various prediction horizons in-line with the delay induced by the order of digital filters. Prediction inferences underpin that the QELM method elegantly learns the cross-dimensional coupling of the tremor motion with random quaternion neurons and hence obtained significant improvement in prediction performance at all prediction horizons compared with existing methods.