Ahmed Shehata, Ph.D. (Post-Doctoral Fellow)

Dr. Shehata joined the BLINC Lab in 2018 as a postdoctoral fellow. He received a B.Sc. degree in Engineering Materials Science and an M.Sc. degree in Mechatronics Engineering from the German University in Cairo (GUC), Egypt, in 2012 and 2013, respectively. In 2015, he pursued his doctoral studies at the Institute of Biomedical Engineering, University of New Brunswick, Canada where he acquired his Ph.D. degree in Electrical Engineering in 2018. Between 2013 and 2015, he worked as an Assistant Lecturer at the German University Berlin-campus, Germany, where he taught several graduate-level courses in Mechatronics Engineering.

Dr. Shehata spent the summer of 2017 in the Artificial Hands Area Lab, The Biorobotics Institute, Scuola Superiore Saint’Anna, Pisa, Italy working on exploring sensory feedback to improve myoelectric prosthesis control for upper-limb amputees.

His research interests include prosthesis control, computational motor control for human-machine interfaces, and sensory feedback systems for upper- and lower-limb prosthesis.

Dr. Shehata is a registered Member of the Egyptian Engineers Syndicate, the IEEE Engineering in Medicine and Biology Society (EMBS), and the IEEE Robotics and Automation Society (RAS).

He enjoys long walks, playing soccer, and building autonomous aerial vehicles in his spare time.

Publications:

  • D H. Blustein, A W. Shehata, K. Englehart, and J W. Sensinger. (2018). Conventional Analysis of Trial-by-trial Adaptation is Biased: Empirical and Theoretical Support Using a Bayesian Estimator. PLOS Computational Biology. In Review.
  • A W. Shehata, L F. Engels, M. Controzzi, C. Cipriani, E J. Scheme, and J W. Sensinger. (2018). Improving Internal Model Strength and Performance of Prosthetic Hands Using Augmented Feedback. Journal of NeuroEngineering and Rehabilitation. In Review.
  • A W. Shehata, E J. Scheme, and J W. Sensinger. (2018). Myoelectric Prosthesis Control: Improving Internal Model Strength and Performance using Augmented Feedback, Nature Scientific Reports. In Review.
  • A W. Shehata, E J. Scheme, and J W. Sensinger. (2018). Evaluating Internal Model Strength and Performance of Myoelectric Prosthesis Control Strategies. IEEE Transactions on Neural Systems & Rehabilitation Engineering. Epub ahead of print. https://doi.org/10.1109/TNSRE.2018.2826981
  • A W. Shehata, E J. Scheme, and J W. Sensinger. (2017). Myoelectric Prosthesis Control: Does Augmented Feedback Improve Internal Model Strength and Performance?. Proceedings of the Myoelectric Control Symposium.
  • A W. Shehata, E J. Scheme, and J W. Sensinger. (2017). The Effect of Myoelectric Prosthesis Control Strategies and Feedback Level on Adaptation Rate for a Target Acquisition Task. Proceedings of the IEEE 15th International Conference on Rehabilitation Robotics.
  • A W. Shehata and A. Khamis. (2014). A Hybrid Approach to Multi-robot Group Formation. Proceedings of the International Conference on Industry Academia Collaboration.
  • A W. Shehata and A. Khamis. (2013). Adaptive Group Formation in Multirobot Systems. Advances in Artificial Intelligence, Vol. 2013, Article ID 692658.

Presentations:

  • Shehata. (2018). Towards better prosthesis control: Using sensory feedback to improve performance, Emerging Technologies in Communications, Microsystems, Optoelectronics, and Sensors. Whistler, BC, Canada.
  • Shehata, E. Scheme, and J. Sensinger. (2017). Myoelectric Prosthesis Control: Does Augmented Feedback Improve Internal Model Strength and Performance?, Myoelectric Control Symposium. Fredericton, NB, Canada.
  • Shehata, E. Scheme, and J. Sensinger. (2017). The Effect of Myoelectric Prosthesis Control Strategies and Feedback Level on Adaptation Rate for a Target Acquisition Task. IEEE 15th International Conference on Rehabilitation Robotics. London, UK.
  • Shehata and A. Khamis. (2014). A Hybrid Approach to Multi-robot Group Formation. International Conference on Industry Academia Collaboration. Cairo, Egypt.