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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
The use of an Energy Monitor in the management of diabetes: a pilot study.
TL;DR: It was found that higher energy levels resulted in much lower fluctuations in BGL change between meals compared to low energy levels, and the weekly mean activity score showed an increase in activity levels from the second week to the final week.
Journal ArticleDOI
On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress.
Mohamed Elgendi,Richard Fletcher,Ian Norton,Matt Brearley,Derek Abbott,Nigel H. Lovell,Dale Schuurmans +6 more
TL;DR: The preliminary results indicate that the use of the energy of aa area, derived from PPG signals measured from emergency responders in tropical conditions, is promising in determining the heat stress level using 20-s recordings.
Posted Content
Estimating Lower Limb Kinematics using a Reduced Wearable Sensor Count
Luke Sy,Michael Raitor,Michael B. Del Rosario,Heba Khamis,Lauren Kark,Nigel H. Lovell,Stephen J. Redmond +6 more
TL;DR: An algorithm for accurately estimating pelvis, thigh, and shank kinematics during walking using only three wearable inertial sensors using a constrained Kalman filter is presented.
Journal ArticleDOI
A Six-Step Framework on Biomedical Signal Analysis for Tackling Noncommunicable Diseases: Current and Future Perspectives
Mohamed Elgendi,Newton Howard,Nigel H. Lovell,Andrzej Cichocki,Matt Brearley,Derek Abbott,Ian Adatia +6 more
TL;DR: A high-level framework using biomedical signal processing (BSP) for tackling diagnosis of noncommunicable diseases, especially in LMICs is presented, which is relevant to a wide variety of stakeholders, including researchers, policy makers, clinicians, computer scientists, and engineers.
Proceedings ArticleDOI
Classification of walking patterns on inclined surfaces from accelerometry data
TL;DR: This paper describes the classification of walking patterns on ascending and descending slopes based on features extracted from data recorded using a single waist-mounted tri-axial accelerometer, using the Gaussian Mixture Model to perform a four way classification task.