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University of Salford, Manchester

My Bio: 

Stefania Russo was born in Manduria, south Italy, where she completed her high school. Then she moved to Pisa where she received her Bachelor and Masters in Biomedical Engineer from the University of Pisa. As part of her education she undertook a placement in the Centre for Robotics Research in King’s College London, UK, where she worked on the development of an innovative soft sensor for minimally invasive surgery, in collaboration with the BioRobotics Institute, Scuola Superiore Sant’Anna. After this experience she worked as Field Clinical Specialist starting from July 2014, serving as primary resource for clinical support in the operating rooms during pacemaker and ICD systems implants.

Stefania is presently working as a PhD Research Fellow in the Marie Curie FP7 Framework : SMART-E project at the University of Salford, Manchester, UK  in the School of Computing, Science and Engineering, Autonomous System and Robotics centre. Her main research interests include Soft and Stretchable Sensor Skins, Compliant Robotics, Smart Materials, Machine Learning, Inverse problems analysis and Signal processing.

Stefania can describe herself as a well-rounded person, thanks to all her experiences in the universities and working fields. Her interests space between engineering and computer science to diving, travelling and reading.

What I do in Smart-E: 

Current sensing technologies are very challenging to implement over 3D surfaces, sometimes expensive and difficult to replace, while a soft and low-cost solution [1] able to reproduce some of the properties of our skin is needed, especially on high-deformable areas as the robotic-joints. In this way it is possible to enable and maximise the quality of the robot interactions’ with the surrounding environment.

This work describes an initial step towards the realisation of a stretchable and deformation-responsive “sensitive skin” for reproducing the human sensing capabilities in robotic applications. We are developing a sensor as a pressure sensitive fabric material [2] which responds to external stimuli by changing its electrical conductivity. 
Sensor data are acquiredin a tomographic approach through a customised PCB which is low-cost and with low power consumption, ideal for battery-powered operations [3].
The stretchable sensor is surrounded by electrodes for the electrical circuit and in this way, since it does not present internal wires, it is extremely soft and stretchable.  When an external stimulus is applied, the variations in the internal conductivity of the sensitive skin will change the distribution of the injected electrical current inside it, resulting in a variation of the measured voltages at the boundary. The collected potentials are then sent to a software for reconstructing the image of the internal conductivity distribution.

Two different set-ups (8 and 16 electrodes) are presented and tested along with 2 different stretchable and piezoresistive materials. Various experiments are conducted demonstrating the quality of the hardware setup and the successful reconstruction of the pressure images.

We have been working on the voltage data and study the performance parameters for the optimisation of the drive patterns. We have demonstrated that, depending on the present stimuli position over the conductive domain, the selection of electrodes on which current injection and voltage reading are performed, can be chosen dynamically resulting in an improved quality of the reconstructed image and system performance, as shown in [4-5]. 

Then, it is proposed to solve the contact detection and image reconstruction with Artificial Neural Networks which can learn to localize the contact position and detect the size of the target with high accuracy [6].

Finally, the sensor is placed over a robotic arm, to show its stretching capabilities and accurate response.


[1] S. Russo, T. Ranzani, H. Liu, S. Nefti-Meziani, K. Althoefer, and A. Menciassi “Soft and Stretchable Sensor Using Biocompatible Electrodes and Liquid for Medical Applications”. Soft Robotics. December 2015, 2(4): 146-154. doi:10.1089/soro.2015.0011.

[2] S. Russo, S. Nefti-Meziani, T. Gulrez and A. Tognetti, “Towards the Development of an EIT-based Stretchable Sensor for Multi-touch Industrial Human-Computer Interaction Systems”, in HCI International 2016, Toronto, Canada, 17 - 22 July 2016.

[3] Russo, S., Carbonaro, N., Tognetti, A., Nefti-Meziani, S., “Development of a High-Speed Current Injection and Voltage Measurement System for EIT-based Stretchable Sensors”, Technologies vol. 5, no. 3, p. 48, 2017, doi:10.3390/technologies5030048. 

[4] S. Russo, N. Carbonaro, A. Tognetti, S. Nefti-Meziani, “A Quantitative Evaluation of Drive Patterns in Electrical Impedance Tomography“ in Mobihealth 2016.

[5]  Russo, S., Carbonaro, N., Tognetti, A., Nefti-Meziani, S., “A Quantitative Evaluation for Dynamically Optimizing Drive Patterns Selection in EIT-based Stretchable Sensors”, Sensors Vol. 17, Pages 1999, 2017, doi:10.3390/s17091999

[6]  Russo, S., Assaf, R., Nefti-Meziani, S., “Towards a practical implementation of EIT-based Sensors using Artificial Neural Networks” In IEEE Sensors, 2017. Soon available on IEEE Xplore



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