Skip to main content


We envision a computation foundation and engineering framework that enables seamless integration and co-adaptation of smart wearable machines (such as robotic prostheses, exoskeletons, functional electrical stimulation) and humans to assist and augment human physical abilities, performance, and experiences.

We plan to achieve our vision via study of wearer-machine interaction and development of breakthrough technologies in wearable robots and neural-machine interfaces. The wearer-machine integrated system needs to achieve the following functions.

  • Environment Adaptation
  • Personalization of Wearable Robots and Wearer-Robot Co-Adaptation
  • Safety
  • Embodiment

Neural Control of Prosthetic Legs

The function of current robotic prosthetic legs is limited due to the lack of neural-machine interface (NMI). NREL pioneered a novel phase-dependent, neuromuscular-mechanical fusion-based NMI for powered artificial legs. The NMI recognizes the user’s locomotion mode and predict the locomotion mode transitions. This basic engineering framework has been published in 2009 and 2011; it has been widely adopted since then. The technology has enabled individuals with lower limb amputations to walk with robotic prosthetic devices intuitively and negotiate changing terrains smoothly and seamlessly.

Our recent research effort focuses on restoring anticipatory postural control of prosthetic ankle in individuals with transtibial amputations via feed-forward, continuous neural control. This novel concept, if successful, can transform the way amputees interact with their powered prostheses and significantly improve their postural stability and motor functions.

Related news and videos: Check out our discussion about this research topic at

Neural Control of Prosthetic Arms

Neural control of prosthetic arms is essential for prosthesis embodiment. Our major contributions along this research line include (1) enhancing the robustness and practical value of EMG pattern recognition-based prosthesis control (machine learning-based approach) and (2) developing generic EMG-driven musculoskeletal model for prosthetic arm control (physiological model-based approach). Our methods have been applied to patients with below elbow amputations, as well as amputees with targeted muscle reinnervation (TMR) surgeries.  Currently, we are developing different smart control strategies for dexterous prosthesis hands and wrists in order to reduce the user’s cognitive load in operating the prosthesis in activities of daily living.

Related news and videos: Check out our research news at NC State and

Optimal Adaptive Control of Prosthetic Legs for Personalized Walking Assistance

Personalization of assistive wearable machines, such as exoskeletons and robotic prostheses, is usually done in clinics manually and heuristically. Our lab has developed several breakthrough technologies that can automatically tuning 12 control parameters simultaneously for robotic prosthetic legs in order to provide personalized walking assistance. Our first approach is to design a cyber expert system that learns to tune a robotic prosthesis from certified prosthetists. The research results were published in 2013 and 2016. Th related US patent has been issued recently. Our second approach is based on reinforcement learning-based optimal adaptive control. This approach does not depend on any prior knowledge on prosthesis tuning. The detailed design can be found in our publications (2017, 2019a, 2019b). Both approaches resulted in learned tuning policy that may be directly deployed in the clinics for automatic robotic prosthesis tuning.  Our innovation may transform the current clinical practice and improve the function and quality of life for individuals with lower limb amputations.

Related news and videos: Check out our research news at IEEE Spectrum and


Wearer-Robot Interaction and Coordination

Understanding how human interacts with rehabilitation machine is critical to develop effective assistive or therapeutic training tools for medical rehabilitation. Our contribution in this area include (1) investigation of the effects of lower limb prosthesis control errors on the amputees’ walking stability and device acceptance, (2) Understanding the influence of prosthesis mechanics on wearer’s gait performance, (3) studying how augmented feedback and virtual reality can enhance the task performance of individuals with lower limb amputations, and (4) study of human attention, intention, and cognitive work load when interacting with different robotic prostheses in various environments and task contexts.

Related news and videos: Check out our research news at NC State and

Exoskeletons for Improved Walking Stability

We have recently started to design a new hip exoskeleton. Our innovation lies in the design of active hip abduction/adduction joints, beyond the powered hip flexion/extension, in order to assist gait and dynamic postural stability at the same time. In human walking, control of foot placement in both the mediolateral and anteroposterior directions has long been recognized as an effective mechanism for maintaining gait stability. During walking, beyond the forward step length regulated by hip flexion/extension, adaptation of the step width can be adjusted by hip abduction/adduction motions. More research will be conducted on individuals with neuromuscular deficits to evaluate the hip exoskeleton design.




We thank the prior and current research sponsorship from NSF (CISE/CHS, CISE/NRI, ENG/M3X, ENG/EPCN, ECCS/CPS, CISE/SCH, NSF Graduate Research Fellowship), HHS/NIH (NIBIB, NICHD), HHS/NIDILRR, DOD/CDMRP, DOD/DARPA, NC State/UNC BME, CLEAR, and Össur .
We thank our research participants, research collaborators, clinical partners, and local amputee support groups for supporting NREL’s research and education programs.