I earned my Master of Science in Biomedical Engineering summa cum laude in 2014 at the University of Bologna (Italy) under the supervision of Prof. Angelo Cappello. I was, then, involved in the project “Fall risk estimation and prevention in the elderly using a quantitative multifactorial approach” (Project ID Number 2010R277FT) managed by the Italian Ministry of Education, University and Research (Ministero dell’Istruzione, dell’Università e della Ricerca) under the supervision of the Prof. Rita Stagni. I am currently in the second year of my doctoral degree still under the supervision Prof. Rita Stagni.
My PhD project focuses on the development of signal processing and feature extraction algorithms of the center of mass acceleration signals during gait for the assessment of fall risk in healthy and pathological subjects with particular interest on the methodological aspects and clinical relevance.
Planned Research Project
Thanks to the ESB Mobility Award for Young Researchers I will travel at the University of Sheffield -Department of Mechanical Engineering INSIGNEO Institute for in silico Medicine- under the supervision of Prof. Claudia Mazzà.
The main goals of the project will be evaluating how motor control assessment changes depending on different environmental conditions (from conventional laboratory assessment to free moving in a daily living environment), and analyzing through musculo-skeletal modeling how different biomechanical motion pattern (e.g. joint kinematics, joint loads and muscular pattern activations) are related to the observed motor control capabilities. This international collaboration will help me to gain skills on musculoskeletal model and to complement my PhD thesis by acquiring a deep understanding of walking motor function as the result of the integration of different factors: motor control capabilities, biomechanical aspects (joint loads, kinematics and muscular activations) and environmental ones. In addition, this collaboration will give the opportunity to share expertise between University of Bologna and Sheffield enhancing the research outcomes of both groups.
Feihu Zhao started his postdoctoral position in Department of Biomedical Engineering, Eindhoven University of Technology (TU/e), the Netherlands in February 2016. Currently, he is working on in silico bone tissue engineering under the supervision of Dr. Sandra Hofmann in TU/e. From December 2012 to January 2016, Feihu did his PhD study under the supervision of Dr. Laoise M. McNamara in National University of Ireland, Galway (NUIG). Feihu’s PhD thesis title was In Silico Bone Tissue Engineering – A Multiscale and Multiphysics Approach, and he passed PhD thesis defense in April 2016. Before PhD study, Feihu obtained his master and bachelor degrees both in mechanical engineering from Tampere University of Technology (Finland) and Huaihai Institute of Technology (China), respectively. In his master thesis project, Feihu worked on modelling and optimising the biomedical device for cellular mechanical stimulation in Microsystem Technology (MST) Group (PI: Prof. Pasi Kallio) at Tampere University of Technology.
Planned research project
The ESB mobility award will support Feihu’s visiting research in Prof. Damien Lacroix’s lab in University of Sheffield (UK). The primary aim of this visiting research is to develop an in silico model, which can predict the in vitro bone tissue formation and resorption within a silk fibroin scaffold. During the visiting research in Sheffield, Feihu will further develop his in silico model by including the cellular activities into the model using a lattice approach, which was previously developed by Prof. Damien Lacroix and his colleagues. Finally, the in silico model will be validated by comparing to the results from bone tissue engineering experiments, which are carried out by Feihu’s collaborator in TU/e. The output from this in silico model will not only give the important implication to the future bone tissue engineering experiments (i.e. optimisation of mechanical stimulation), but also provide a platform for virtual experiments.