Silvia Del Din

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Impegnato/a
Newcastle University
Newcastle University Academic Track Fellow
Educazione e formazione
Newcastle Upon Tyne
Regno Unito

I received my Bachelor's Degree in Information Engineering in 2006 from the University of Padova (Italy) under the Supervision of Prof. Claudio Cobelli and my Master's Degree (cum laude) in Bioengineering in 2008 from the University of Padova ("Biomechanical analysis of swimmers through markerless motion capture." Supervisor: Ch.mo Prof. Claudio Cobelli, Co-advisor: Dr. Elena Ceseracciu).

In 2012 I completed my PhD in Bioengineering (Area of the Information Engineering PhD School) at the Department of Information Engineering of the University of Padova, under the supervision of Prof. Chiara Dalla Man ("Innovative Techniques for Biomechanical Evaluation of Stroke Survivors: Combined fMRI-Gait Analysis Assessment and Fugl-Meyer Clinical Scores Estimation Through Wearable Sensors.").

From February 2012 until August 2012 I have been a Post Doctoral Fellow at the Department of Information Engineering of the University of Padova.

Since September 2012 I have been working at the Translational and Clinical Research Institute of Newcastle University where I am a NUAcT Fellow collaborating with the Brain and Movement (BAM) Research Group.

Experience abroad
October 2010- September 2011:
Harvard Medical School, Department of Physical Medicine and Rehabilitation, Motion analysis Laboratory of Spaulding Rehabilitation Hospital, Boston (MA) USA.
I have been part of the Motion Analysis Laboratory team at Spaulding Rehabilitation Hospital in Boston in order to carry on my PhD research, under the supervision of Prof. Paolo Bonato. I have been involved in a rehabilitation project of stroke patients, based on the use of wearable sensors: the goal is to use accelerometers for gathering quantitative measures of movement quality, providing accurate assessments to guide the rehabilitation process. Models based on features extracted from wearable sensor data should be useful to accurately predict some clinical scores.