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Dr Emma Villeneuve

Dr Emma Villeneuve

Associate Research Fellow in Healthcare Modelling

2393

South Cloisters 2.30

Emma has worked as a researcher in statistical signal processing for over 6 years, with various applications in instrumentation, astrophysics and biomechanics.

Emma received her Master's degree in Physics in 2009 from the School of Engineering PHELMA, Grenoble, and a Master's degree in Signal and Image Processing in 2009, from the Grenoble Institute of Technology, France.

From 2009 to 2013, she worked as a Research and Teaching assistant at IRAP and at the University of Toulouse where she received her Ph.D in Signal Processing on estimation and deconvolution methods for astrophysical hyperspectral data. Then she joined the SPHERE project at the University of Reading, working on statistical methods for the reconstruction of human kinematics from wearable sensors.

Emma currently works within the PenCHORD team (Peninsula Collaboration for Health Operational Research and Development), focussing on the NeoNet project.

Her research interests are in the areas of statistical signal processing, particularly on biomedical applications.

Qualifications

2007 BSc (PHELMA Grenoble, France)
2009 MEng: French Engineer degree, Physics and Instrumentation (PHELMA Grenoble)
2009 MSc Signal and Image Processing (Grenoble Institute of Technology)
2012 PhD Statistical Signal Processing (University of Toulouse)

Research

Research interests

Statistical signal processing for Healthcare Modelling

Research projects

PenCHORD is engaged in a range of projects looking at modelling health service and delivery to improve the NHS within the South-West of the UK.

NeoNet project: The right cot, at the right time, at the right place 2. Providing a national demand/capacity model for neonatal care in England.

Key publications | Publications by category | Publications by year

Publications by category


Journal articles

King RC, Villeneuve E, White RJ, Sherratt RS, Holderbaum W, Harwin WS (2017). Application of data fusion techniques and technologies for wearable health monitoring. Med Eng Phys, 42, 1-12. Abstract.  Author URL.
Villeneuve E, Harwin W, Holderbaum W, Janko B, Sherratt RS (2017). Reconstruction of Angular Kinematics from Wrist-Worn Inertial Sensor Data for Smart Home Healthcare. IEEE Access, 5, 2351-2363.
Villeneuve E, Harwin W, Holderbaum W, Sherratt RS, White R (2016). Signal Quality and Compactness of a Dual-Accelerometer System for Gyro-Free Human Motion Analysis. IEEE Sensors Journal, 16(16), 6261-6269.
Villeneuve E, Carfantan H (2014). Nonlinear Deconvolution of Hyperspectral Data with MCMC for Studying the Kinematics of Galaxies. IEEE Transactions on Image Processing, 23(10), 4322-4335.

Publications by year


2017

King RC, Villeneuve E, White RJ, Sherratt RS, Holderbaum W, Harwin WS (2017). Application of data fusion techniques and technologies for wearable health monitoring. Med Eng Phys, 42, 1-12. Abstract.  Author URL.
Villeneuve E, Harwin W, Holderbaum W, Janko B, Sherratt RS (2017). Reconstruction of Angular Kinematics from Wrist-Worn Inertial Sensor Data for Smart Home Healthcare. IEEE Access, 5, 2351-2363.

2016

Villeneuve E, Harwin W, Holderbaum W, Sherratt RS, White R (2016). Signal Quality and Compactness of a Dual-Accelerometer System for Gyro-Free Human Motion Analysis. IEEE Sensors Journal, 16(16), 6261-6269.

2014

Villeneuve E, Carfantan H (2014). Nonlinear Deconvolution of Hyperspectral Data with MCMC for Studying the Kinematics of Galaxies. IEEE Transactions on Image Processing, 23(10), 4322-4335.

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