Ahmed W. Shehata, Ph.D. (Adjunct Faculty, Research Associate)

Dr. Shehata is an adjunct professor in the Department of Biomedical Engineering, Faculty of Engineering. He joined the BLINC Lab in 2018 as a postdoctoral fellow and in 2021 continued his work with the lab as a research associate. 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), the Association of Professional Engineers and Geoscientists of Alberta (APEGA), and the IEEE Robotics and Automation Society (RAS).

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

Selected Publications:

  • A W. Shehata, H E. Williams, J S. Hebert and P M. Pilarski. 2021. “Machine Learning for Control of Prosthetic Arms Using Electromyographic Signals” IEEE Signal Processing Magazine, SPM; 4:38. 10.1109/MSP.2021.3075931
  • P D. Marasco, J S. Hebert, J W. Sensinger, D T. Beckler, Z C. Thumser, A W. Shehata, H E. Williams, K R. Wilson. 2021 “Neurorobotic fusion of prosthetic touch, kinesthesia, and movement in bionic upper limbs promotes intrinsic brain behaviors” Science Robotics, Sci. Robot. 6, eabf3368. 10.1126/scirobotics.abf3368
  • D H. Blustein, A W. Shehata, E S. Kuylenstierna, K B. Englehart, and J W. Sensinger. 2021. “An Analytical Method Reduces Noise Bias in Motor Adaptation Analysis” Scientific Reports, SR; 11:9245. 10.1038/s41598-021-88688-5
  • M I. Keri, A W. Shehata, J S. Hebert and A H. Vette. 2021. “A Cost-Effective Inertial Measurement System for Tracking Movement and Triggering Kinesthetic Feedback in Lower-Limb Prosthesis Users” Sensors 21, no. 5:1844. 10.3390/s21051844
  • A W. Shehata, M. Rehani, Z E. Jassat and J S. Hebert. 2020. “Mechanotactile Sensory Feedback Improves Embodiment of a Prosthetic Hand” Frontiers in Neuroscience, 14, p.263. 10.3389/fnins.2020.00263
  • C J. Stiegelmar, D H. Blustein, J W. Sensinger, J S. Hebert, and A W. Shehata. 2020. “Towards Quantifying The Sense of Agency and its Contribution to Embodiment of Myoelectric Prostheses”, Proceedings of the Myoelectric Control Symposium, MEC. Link
  • B W. Hallworth, J A. Austin, H E. Williams, M. Rehani, A W. Shehata, and J S. Hebert. 2020. “A Modular Adjustable Transhumeral Prosthetic Socket for Evaluating Myoelectric Control” IEEE Journal of Translational Engineering in Health & Medicine, JTEHM; 8: 0700210. 10.1109/JTEHM.2020.3006416
  • D H. Blustein, N. Mesa, E. Kuylenstierna, K. Parsons, C J. Stiegelmar, J S. Hebert, and A W. Shehata. 2020. “Towards Objective Assessment of Ownership Over a Prosthesis”, Proceedings of the Myoelectric Control Symposium, MEC. Link
  • A W. Shehata, M. Keri, M. Gomez, P D. Marasco, A H. Vette, and J S. Hebert, “Skin Stretch Enhances Illusory Movement in Persons with Lower-Limb Amputation,” 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), Toronto, ON, Canada, 2019, pp. 1233-1238. 10.1109/ICORR.2019.8779477
  • L F. Engels, A W. Shehata, E J. Scheme, J W. Sensinger, and C. Cipriani. (2019). When Less Is More – Discrete Tactile Feedback Dominates Continuous Audio Biofeedback in the Integrated Percept While Controlling a Myoelectric Prosthetic Hand. Frontiers in Neuroscience, 13, p.578. 10.3389/fnins.2019.00578
  • D. 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 14(12): e1006501. 10.1371/journal.pcbi.1006501
  • J. Austin, A W. Shehata, M R. Dawson, J. Carey, and J S. Hebert, “Improving Performance of Pattern Recognition-Based Myoelectric Control Using a Desktop Robotic Arm Training Tool,” 2018 IEEE Life Sciences Conference (LSC), Montreal, QC, 2018, pp. 231-234. 10.1109/LSC.2018.8572214
  • M. Keri, A W. Shehata, Q A. Boser, A H. Vette, and J S. Hebert, “Development and Verification of a Low-Cost Prosthetic Knee Motion Sensor,” 2018 IEEE Life Sciences Conference (LSC), Montreal, QC, 2018, pp. 283-286. 10.1109/LSC.2018.8572092
  • 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; 15:70. 10.1186/s12984-018-0417-4
  • A W. Shehata, E J. Scheme, and J W. Sensinger. (2018). Audible Feedback Improves Internal Model Strength and Performance of Myoelectric Prosthesis Control, Scientific Reports; 8, Article number: 8541. 10.1038/s41598-018-26810-w
  • 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; 26:1046–55. 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. Link
  • 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. 10.1109/ICORR.2017.8009246

Selected talks:

  • Shehata. (2020). Machine Learning for Bionic Limb Control, UNB Computational Motor Control group, Canada (Virtual)
  • Shehata. (2019). Discrete or Continuous Sensory Feedback for Regression-based Myoelectric Control – Time to Decide, Deutsches Zentrum für Luft- und Raumfahrt (DLR) – German Aerospace Center. Munich, Germany.
  • 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.

We are always looking for talented and highly qualified personnel and students. If you are interested in working/studying in our lab, please fill out the following form and I will get back to you as soon as possible.

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