Speaker Bios

Marcia O’Malley is the Thomas Michael Panos Family Professor in Mechanical Engineering, Computer Science, Electrical and Computer Engineering, and Bioengineering in the George R. Brown School of Engineering at Rice University. She received her BS in Mechanical Engineering from Purdue University, and her MS and PhD in Mechanical Engineering from Vanderbilt University. Her research is in the areas of haptics and robotic rehabilitation, with a focus on the design and control of wearable robotic devices for training and rehabilitation. She has twice received the George R. Brown Award for Superior Teaching at Rice University. O’Malley was a recipient of both the ONR Young Investigator award and the NSF CAREER Award. She is a Fellow of the American Society of Mechanical Engineers, the Institute of Electrical and Electronics Engineers, and the American Institute for Medical and Biological Engineering.

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Dr. Nitin Sharma is an Associate Professor in the Joint Department of Biomedical Engineering at North Carolina State University-Raleigh and University of North Carolina-Chapel Hill. His research in hybrid exoskeletons and Tremor suppression is funded by NSF awards and NIH. His awards include NSF CAREER Award in 2018, IEEE Control Systems Technology Award 2019, and NIBIB Trailblazer Award in 2021.

Mahdi Tavakoli is a Professor in the Electrical and Computer Engineering Department and the Biomedical Engineering Department and a Senior University of Alberta Engineering Research Chair in Healthcare Robotics. He is also Scientific Vice-Director for the Institute for Smart Augmentative and Restorative Technologies (iSMART) at the University of Alberta. He received his PhD degree in Electrical and Computer Engineering from the University of Western Ontario, Canada, in 2005. From 2006 to 2008, he was a post-doctoral researcher at Canadian Surgical Technologies and Advanced Robotics (CSTAR), Canada, and an NSERC Post-Doctoral Fellow at Harvard University, USA. Dr. Tavakoli’s research interests involve medical robotics, image-guided surgery, and rehabilitation robotics. Dr. Tavakoli is the lead author of Haptics for Teleoperated Surgical Robotic Systems (World Scientific, 2008) and the Specialty Chief Editor for Frontiers in Robotics and AI (Robot Design Section). He is a Senior Member of IEEE and an Associate Editor for the International Journal of Robotics Research, IEEE Transactions on Medical Robotics and Bionics, IEEE/ASME Transactions on Mechatronics’ Focused Section with Advanced Intelligent Mechatronics, and Journal of Medical Robotics Research.

The restoration of hand and arm function is the highest rehabilitation priority for individuals with tetraplegia due to spinal cord injury. Functional electrical stimulation (FES) offers promise to restore reaching and grasping to these individuals and has shown success in rehabilitation applications. A major barrier to implementing FES-driven reaching neuroprostheses outside the clinic is the complex control problem caused by the everchanging detrimental muscle characteristics (i.e., atrophy, rapid fatigue, and fewer activatable muscles) that develop following spinal cord injury. Additionally, the goal- directed, non repetitive nature of reaching motions creates a need for a control strategy that can complete any novel, arbitrary reach. We addressed these barriers by developing person-specific, data- driven models of an individual’s arm when electrically stimulated. Using these models as the basis of a data-driven trajectory optimization and a model predictive control strategy that accounts for the person-specific muscle capabilities, we implemented a full-arm reaching controller in an individual with a C1-C2 level spinal cord injury. The controller achieved an average hand position accuracy of 8.5 cm. Person specific models like these have great potential to be used to drive rehabilitation as well as control assistive devices for daily use. This control strategy could also be used in combination with a robotic device for shared control of reaching motions that are infeasible or inaccurate with FES alone.

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Andreas Christou is a final-year Ph.D. candidate in Robotics and Autonomous Systems at the Centre for Doctoral Training at the University of Edinburgh, U.K. He received the Master of Engineering (M.Eng.) degree in Mechanical Engineering in 2019 from the University of Edinburgh, U.K., where he also participated in the Education Abroad Program at the University of California, Santa Barbara. Since then, Andreas carried out his research activities at the Statistical Machine Learning and Motor Control group (SLMC) at the University of Edinburgh. His research interests include the use of wearable robots and functional electrical stimulation for gait rehabilitation and human augmentation.

Emilia Ambrosini is Associate Professor at Department of Electronics, Information and Bioengineering at Politecnico di Milano. She received the Master’s Degree com laude in Biomedical Engineering from Politecnico di Milano in 2007 and the PhD com laude in Bioengineering in 2011. Since 2011, she carried out her research activity at NearLab (Neuroengineering and Medical Robotics Laboratory). Her research interests are the design of technologies based on robotics and Functional Electrical Stimulation for rehabilitation, daily life assistance and practice of sport activities of neurological patients and the development of advanced quantitative methods for training assessment and ecological monitoring of patients with neurological diseases and fragile people. She is co-authors of more than 55 papers in international journals indexed in Scopus (H-index 25, Apr 2024).

Dr. Satoshi Endo is a research fellow at the Chair of Information-oriented control, Technical University of Munich (https://www.ce.cit.tum.de/). He specialises in the intersection of neuroscientific principles and engineering, and his research focuses on modelling human functions to detect and understand neural deficits, with particular emphasis on conditions such as Parkinson’s disease and stroke. By adopting advanced machine learning techniques, he aims to promote the development of innovative rehabilitation systems that significantly improve patient outcomes. His current research is centered on leveraging a deep understanding of neuromuscular systems for the model-based control of hybrid neuroprosthesis devices.

Lucille Cazenave is a doctoral student at Imperial College London in the Human Robotics Group (https://www.imperial.ac.uk/human-robotics/), where her interest in neuroscience led her to investigate how technologies can impact movement recovery after neurological conditions. She is currently working on the development of human-machine interfaces combining robotics and functional electrical stimulation for upper-limb rehabilitation and on understanding how they affect stroke recovery outcomes, through the analysis of patients’ kinematics, neurophysiological measures, and qualitative metrics.