Speaker: Silvia Del Din (Newcastle University).
Abstract: Gait is emerging as a powerful tool to detect early risk and monitor disease progression across a number of diseases (e.g. Parkinson’s disease (PD)). Typically quantitative gait assessment has been limited to specialised and expensive laboratory facilities (e.g. three-dimensional motion capture systems). However, measuring PD gait in home and community settings may provide a more accurate reflection of gait performance as it allows walking activity to be captured over time in habitual contexts. Modern wearable technology (e.g. accelerometers, IMUs) allow objective measurement of real-world walking activity/behaviour (macro level, e.g. step count) as well as discrete gait characteristics (micro level, e.g. walking speed). Quantification of PD macro and micro digital gait characteristics in unsupervised environments presents considerable challenges. This presentation will address the feasibility, methodological advantages and challenges of measuring macro and micro digital gait characteristics during real-world activity. The use of digital gait characteristics as a measurement tool for discriminating pathology (people with PD vs. healthy controls) and detecting risk (e.g. prodromal PD, fall risk) will also be discussed.
Bio: Silvia received her Bachelor's Degree in Information Engineering and her Master's Degree in Bioengineering in 2008 from the University of Padova. In 2012, she completed her 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."). She is a Senior Research Associate at the Translational and Clinical Research Institute of Newcastle University in the Brain and Movement (BAM) Research Group led by Prof. Lynn Rochester. She is responsible for the wearable technology research theme within the BAM research group.The aim of her translational research is enhancing the use of wearable technology together with innovative data analysis techniques in clinical practice as a tool for quantifying digital mobility outcomes in neurodegenerative disorders (i.e. Parkinson’s disease) and healthy aging phenotype both in constrained (e.g. laboratory based data) and free-living environments (real world data). She has published over 50 papers in this field. She has been PI and co-applicant in a series of national and international grants.