Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88364
Title: Multi-step prediction of physiological tremor with random quaternion neurons for surgical robotics applications
Authors: Wang, Yubo
Tatinati, Sivanagaraja
Adhikari, Kabita
Huang, Liyu
Nazarpour, Kianoush
Ang, Wei Tech
Veluvolu, Kalyana C.
Keywords: Surgical Robotics
Physiological Tremor
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Wang, Y., Tatinati, S., Adhikari, K., Huang, L., Nazarpour, K., Ang, W. T., & Veluvolu, K. C. (2018). Multi-step prediction of physiological tremor with random quaternion neurons for surgical robotics applications. IEEE Access, 6, 42216-42226. doi:10.1109/ACCESS.2018.2852323
Series/Report no.: IEEE Access
Abstract: 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.
URI: https://hdl.handle.net/10356/88364
http://hdl.handle.net/10220/45765
DOI: http://dx.doi.org/10.1109/ACCESS.2018.2852323
Rights: © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
metadata.item.grantfulltext: open
metadata.item.fulltext: With Fulltext
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