Massimo Sartori

Massimo Sartori (M’08) received the B.Sc. and M.Sc. degrees in computer engineering and the Ph.D. degree in information and communication science and technologies from the University of Padova, Padova, Italy, in 2005, 2007, and 2011, respectively. During his Ph.D. degree, he was a Visiting Student with the School of Sport Science, Exercise, and Health, University of Western Australia, Crawley WA, Australia, and with the Neuromuscular Biomechanics Laboratory, Stanford University, Stanford, CA, USA. In 2011, he spent a research period with the Centre for Musculoskeletal Research at the Griffith Health Institute, Griffith University, Nathan, Qld., Australia. In 2013, he was a Visiting Scholar with the National Center for Simulation in Rehabilitation Research (NCSRR) at Stanford University. Since 2012, he has been a Postdoctoral Fellow at the Institute of Neurorehabilitation Systems, University Medical Center Gottingen, Gottingen, Germany. His research interests include the intersection between movement neurophysiology, biomechanics, and human–machine interfacing. He applies neuromusculoskeletal modeling and electrophysiological signal processing, in a translational way to understand healthy and pathological human movement and to develop personalized neurorehabilitation technologies. Dr. Sartori is currently a Guest Associate Editor of the IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING and in the Frontiers in Computational Neuroscience. In 2014, he received an NCSRR OpenSim Fellowship.

Associated articles

TBME, Featured Articles
Neural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies
The development of personalized neurorehabilitation and augmentation technologies requires the profound understanding of the neuro-mechanical processes underlying an individual’s motor function, impairment, and recovery. A major challenge is the difficulty of accessing the in vivo neural activity underlying human movement... Read more
TBME,
Neural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies
This review aims to discuss clinically viable methods for accessing the neural information underlying an individual’s movement from electrophysiological recordings and the development of subject-specific musculoskeletal modeling formulations that can be driven by the extracted neural features... Read more
TBME,
Neural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies
This review aims to discuss clinically viable methods for accessing the neural information underlying an individual’s movement from electrophysiological recordings and the development of subject-specific musculoskeletal modeling formulations that can be driven by the extracted neural features... Read more
TBME,
Neural Data-Driven Musculoskeletal Modeling for Personalized Neurorehabilitation Technologies
This review aims to discuss clinically viable methods for accessing the neural information underlying an individual’s movement from electrophysiological recordings and the development of subject-specific musculoskeletal modeling formulations that can be driven by the extracted neural features... Read more