N
Nigel H. Lovell
Researcher at University of New South Wales
Publications - 678
Citations - 19383
Nigel H. Lovell is an academic researcher from University of New South Wales. The author has contributed to research in topics: Retinal ganglion & Blood pump. The author has an hindex of 58, co-authored 634 publications receiving 16465 citations. Previous affiliations of Nigel H. Lovell include NICTA & AmeriCorps VISTA.
Papers
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Journal ArticleDOI
Non-invasive estimation of pulsatile flow and differential pressure in an implantable rotary blood pump for heart failure patients.
TL;DR: The models developed herein will play a vital role in developing a robust control system of the pump flow coping with changing physiological demands.
Journal ArticleDOI
Health-Enabling and Ambient Assistive Technologies: Past, Present, Future
Reinhold Haux,Sabine Koch,Nigel H. Lovell,Michael Marschollek,Naoki Nakashima,Klaus-Hendrik Wolf +5 more
TL;DR: The state of the art of health-enabling and ambient assistive technologies in 1992 and today, and its evolution over the last 25 years as well as where the field is expected to be in the next 25 years are described.
Journal ArticleDOI
Cytotoxicity of implantable microelectrode arrays produced by laser micromachining
Rylie A. Green,Juan S. Ordonez,Martin Schuettler,Laura A. Poole-Warren,Nigel H. Lovell,Gregg J. Suaning +5 more
TL;DR: It was found that laser micromachining produces oxides of silicon and platinum on the PDMS and Pt respectively, and there was negligible change in the biological response to either extracts or cell growth directly on the composite electrode array.
Journal ArticleDOI
Can Triaxial Accelerometry Accurately Recognize Inclined Walking Terrains
TL;DR: This paper investigates the benefits of automatic gait analysis approaches including step-by-step gait segmentation and heel-strike recognition of the accelerometry signal in classifying various gradients and aims to improve the accuracy of daily EE estimates with accurate measures on terrain inclinations.
Proceedings ArticleDOI
Characterization of memory load in an arithmetic task using non-linear analysis of EEG signals
TL;DR: Experimental results show that the values of the measures extracted from the delta frequency band of signals acquired from the frontal and occipital lobes of the brain vary in accordance with the task difficulty level induced, indicating more regularity and predictability in the signals.