• Ph.D. Northwestern University, 2014
  • M.S. Northwestern University, 2011
  • B.S. Purdue University, 2009


Dr. Aaron Young recently joined Georgia Tech as an Assistant Professor in Mechanical Engineering and is interested in designing and improving powered orthotic and prosthetic control systems for persons with stroke, neurological injury or amputation. His previous experience includes a post-doctoral fellowship at the University of Michigan in the Human Neuromechanics Lab working with exoskeletons and powered orthoses to augment human performance.  He has also worked on the control of upper and lower limb prostheses at the Center for Bionic Medicine (CBM) at the Rehabilitation Institute of Chicago. His master’s work at CBM focused on the use of pattern recognition systems using myoelectric (EMG) signals to control upper limb prostheses. His dissertation work at CBM focused on sensory fusion of mechanical and EMG signals to enable an intent recognition system for powered lower limb prostheses for use by persons with a transfemoral amputation.

Research Areas and Descriptors

Automation & Mechatronics and Bioengineering: Upper and lower limb prostheses and orthoses, biological signal processing, intent recognition, control systems based on EMG and mechanical sensors

Control of powered robotics systems


Dr. Young’s research is focused on developing control systems to improve prosthetic and orthotic systems. His research is aimed at developing clinically translatable research that can be deployed on research and commercial systems in the near future. Some of the interesting research questions are how to successfully extract user intent from human subjects and how to use these signals to allow for accurate intent identification. Once the user intent is identified, smart control systems are needed to maximally enable individuals to accomplish useful tasks. For upper limb patients, these tasks might include grasping an object or moving an arm. For lower limb patients, these tasks might include standing from a seated position, walking, or climbing a stair.

Caption: One of the first uses of a full intent recognition system using mechanical and EMG signals helped an amputee with a knee disarticulation climb 103 floors of the Willis Tower. Seamless transitions were enabled between walking and stairs on the powered knee and ankle prosthesis developed by Vanderbilt University.

One of Dr. Young’s primary research interests is in intent recognition for lower limb prostheses and orthoses. Intelligent intent recognition systems are capable of allowing users to perform automatic, seamless and natural transitions between different locomotion modes. These modes include every-day tasks such as sitting, standing, walking, and traversing stairs and slopes. By developing and improving intent recognition and control techniques, we hope to improve the usability of the exciting new mechatronic prostheses and exoskeletons that are increasingly becoming commercially available.

Caption: Direct prosthesis joint control and advanced timing is possible using EMG signals recorded from the residual limb of a transfemoral amputee. This person has undergone Targeted Muscle Reinnervation surgery which enables advanced control of the ankle joint as well as the knee.

For upper limb prostheses, pattern recognition control based on myoelectric (EMG) input is promising for extending the number of degrees of freedom. Pattern recognition systems, similar to those used for speech recognition can be applied to an array of EMG inputs from the residual limb of an amputee. These patterns are mapped to specific motions of the prosthesis such as hand grasps or moving an elbow or wrist joint. One interesting research question that we are pursuing is to use pattern recognition techniques along with other machine learning methods to enable simultaneous movements of multiple joints. Humans in every-day life use simultaneous joint movements to allow for smooth, fluid movements. These types of movements are largely absent in current prosthesis control systems, and the aim is to enable and improve simultaneous joint control through pattern recognition and machine learning techniques.

Caption: Surface EMG signals capture underlying muscle movements. These signals are processed and categorized based on a pattern recognition system which maps overall feature patterns to a specific movement of the prosthesis 

Students who work with Dr. Young will work with an interdisciplinary group in robotics, mechanical, electrical and biomedical engineering. They will learn how to conduct human subject experiments and work with clinicians in physical therapy and P&O (Georgia Tech’s Prosthetics and Orthotics Program) to do clinically translatable research. Additionally, they will develop expertise in biological signal processing, mechatronic systems, machine learning, robotics and control.



Distinctions and Awards

  • Military Health System Research Symposium Team Award, 2015
  • BME Research Award in Neural and Rehabilitation Engineering, 2014
  • 3rd place award in Student Paper Competition at IEEE EMBC 2013 Conference, 2013
  • Open Finalist in 2013 EMBS Student Paper Competition, 2013
  • Sarah Baskin Award for Excellence in Research – 1st place, 2012
  • Northwestern Applied Research Day – Best Poster and 3rd place presentation, 2012
  • National Science Foundation Graduate Research Fellowship (NSF-GRFP), 2010
  • National Defense Science and Engineering Fellowship (NDSEG), 2010
  • Royal E. Cabell Fellowship, 2009
  • Phi Beta Kappa, 2009
  • Top Purdue Biomedical Senior Design Project, 2008
  • Havel-Decker BME scholar – outstanding BME junior award, 2008




Aaron Young, Daniel Ferris, “State-of-the-art and Future Directions for Robotic Lower Limb Exoskeletons.” IEEE Transaction on Neural Systems and Rehabilitation Engineering, in press.

Michael Cherry, Sridhar Kota, Aaron Young, Daniel Ferris, “Running with an Elastic Lower Limb Exoskeleton.” Journal of Applied Biomechanics, in press.

Levi Hargrove, Aaron Young, Ann Simon, Nicholas Fey, Robert Lipschutz, Suzanne Finucane, Elizabeth Halsne, Kimberly Ingraham, Todd Kuiken, “Intuitive control of a powered prosthetic leg during ambulation: A randomized clinical trial.” The Journal of the American Medical Association (JAMA), Volume 313, Issue 22 (2015) pp. 2244-2252.

Aaron Young, Levi Hargrove, “A Classification Method for User-Independent Intent Recognition for Transfemoral Amputees using Powered Lower Limb Prostheses.” IEEE Transaction on Neural Systems and Rehabilitation Engineering, in press.

Aaron Young, Todd Kuiken, Levi Hargrove, “Analysis of using EMG to enhance intent recognition in powered lower limb prostheses.” Journal of Neural Engineering, in press.

Nicholas Fey, Ann Simon, Aaron Young, Levi Hargrove, “Controlling knee swing initiation and ankle plantarflexion with an active prosthesis on level and inclined surfaces at variable walking speeds.” Journal of Translational Engineering in Health and Medicine, in press.

Ann Simon, Kimberly Ingraham, Nicholas Fey, Suzanne Finucane, Robert Lipschutz, Aaron Young, Levi Hargrove, “Configuring a powered knee and ankle prosthesis for transfemoral amputees within five specific ambulation modes.” PLoS ONE, Volume 9 Issue 6 (2014) e99387.

Dennis Tkach, Aaron Young, Lauren Smith, Elliott Rouse, Levi Hargrove, “Real-Time and Offline Performance of Pattern Recognition Myoelectric Control Using a Generic Electrode Grid with Targeted Muscle Reinnervation Patients.” Special edition of the IEEE Transaction on Neural Systems and Rehabilitation Engineering for Advances in Control of Multi-functional Powered Upper-Limb Prostheses. Volume 22, Issue 4 (2014) pp. 727-734.

Aaron Young, Lauren Smith, Elliott Rouse, Levi Hargrove, “A Comparison of Real-Time Controllability of Pattern Recognition to Conventional Myoelectric Control for Discrete and Simultaneous Movements.” Journal of NeuroEngineering and Rehabilitation, Volume 11, Issue: 5 (2014).

Aaron Young, Ann Simon, Levi Hargrove, “A Training Method for Locomotion Mode Prediction Using Powered Lower Limb Prosthesis.” IEEE Transaction on Neural Systems and Rehabilitation Engineering, Volume 22, Issue 3 (2014) pp. 671-677.

Aaron Young, Ann Simon, Nicholas Fey, Levi Hargrove, “Intent Recognition in a Powered Lower Limb Prosthesis using Time History Information.” Annals of Biomedical Engineering, Volume 42, Issue: 3 (2014) pp. 631-641.

Levi Hargrove, Ann Simon, Aaron Young, Robert Lipschutz, Suzanne Finucane, Douglas Smith, and Todd Kuiken. “Robotic Leg Control with EMG Decoding in an Amputee with Nerve Transfers.” New England Journal of Medicine, Volume 369, Issue: 9 (2013) pp. 1237-1242.

Aaron Young, Lauren Smith, Elliott Rouse, Levi Hargrove, “Classification of simultaneous movements using surface EMG pattern recognition.” IEEE Transactions on Biomedical Engineering, Volume 60, Issue: 5 (2013) pp. 1250-1258.

Aaron Young, Levi Hargrove, Todd Kuiken. “Improving Myoelectric Pattern Recognition Robustness to Electrode Shift by Changing Interelectrode Distance and Electrode Configuration.” IEEE Transactions on Biomedical Engineering, Volume 59, Issue: 3 (2012) pp. 645-652.

Aaron Young, Levi Hargrove, Todd Kuiken. “The Effects of Electrode Size and Orientation on the Sensitivity of Myoelectric Pattern Recognition Systems to Electrode Shift.” IEEE Transactions on Biomedical Engineering, Volume 58, Issue: 9 (2011) pp. 2537-2544.

Sabrina S. Jedlicka, Maria Dadarlat, Travis Hassell, Yanzhu Lin, Aaron Young, Min Zhang, Pedro Irazoqui, Jenna L. Rickus. “Calibration of Neurotransmitter Release from Neural Cells for Therapeutic Implants.” International Journal of Neural Systems, Volume 19, Issue: 3 (2009) pp. 197-212.


Aaron Young and Levi Hargrove, “Ambulation Prediction Controller for Lower Limb Assistive Device.” U.S. Patent Application No. 13/925,668.

Aaron Young and Levi Hargrove, “Systems and Methods for Hierarchical Pattern Recognition for Simultaneous Control of Multiple Degree of Freedom Movements for Prosthetics.” U.S. Provisional Patent Application No. 61/659,887.