He (Helen) Huang

Helen’s research interest lies in science and technology that can establish and enhance a symbiotic relationship between humans and wearable assitive machines in order to augment motor function for individuals with disabilities. Her research goal is to create breakthroughs in human-machine symbiotic (HMS) systems that empower disabled people to lead more active and productive lives.






To achieve this overarching goal, her current research focuses on understanding how limb amputees and robotic prostheses interact with each other and their environments and development of advanced control for robotic prostheses, which are adaptive, efficient, and safe, in order to restore the motor function in individuals with limb amputations. Three research thrusts have been formulated in my lab:

Thrust 1: Investigation of neuromuscular control and sensorimotor integration in limb amputees and development of neural-machine interfaces for neural control of robotic prosthetic limbs

Thrust 2: Investigation of wearer-machine co-adaptation and development of optimal adaptive control for robotic prostheses that provide personalized assistance and can adapt to changes in wearers and environments.

Thrust 3: Investigation of error correction and tolerance in human-machine symbiotic (HMS) systems and development of fault tolerant control for robotic prosthetic legs to improve the wearer’s stability and safety

*For current projects and open opportunities, check our lab or CLEAR websites

Research Interests

Wearable robotics
Neural-machine interface
Robotic prosthetics and exoskeleton
Optimal adaptive control
Human-robot interaction


1-D Manual Tracing Based on a High Density Haptic Stimulation Grid - a Pilot Effort
Driscoll, B., Liu, M., & Huang, H. (2023), 2023 IEEE WORLD HAPTICS CONFERENCE, WHC, pp. 375–381. https://doi.org/10.1109/whc56415.2023.10224505
A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee Prosthesis
Alili, A., Nalam, V., Li, M., Liu, M., Feng, J., Si, J., & Huang, H. (2023), IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 31, 895–903. https://doi.org/10.1109/TNSRE.2023.3236217
A simplified model for whole-body angular momentum calculation
Liu, M., Naseri, A., Lee, I.-C., Hu, X., Lewek, M. D., & Huang, H. (2023), MEDICAL ENGINEERING & PHYSICS, 111. https://doi.org/10.1016/j.medengphy.2022.103944
Abduction/Adduction Assistance From Powered Hip Exoskeleton Enables Modulation of User Step Width During Walking
Alili, A., Fleming, A., Nalam, V., Liu, M., Dean, J., & Huang, H. (2023), IEEE Transactions on Biomedical Engineering, 1–9. https://doi.org/10.1109/tbme.2023.3301444
Angle-programmed tendril-like trajectories enable a multifunctional gripper with ultradelicacy, ultrastrength, and ultraprecision
Hong, Y., Zhao, Y., Berman, J., Chi, Y., Li, Y., Huang, H., & Yin, J. (2023), NATURE COMMUNICATIONS, 14(1). https://doi.org/10.1038/s41467-023-39741-6
Artificial intelligence meets medical robotics
Yip, M., Salcudean, S., Goldberg, K., Althoefer, K., Menciassi, A., Opfermann, J. D. D., … Lee, I.-C. (2023, July 14), SCIENCE, Vol. 381, pp. 141–146. https://doi.org/10.1126/science.adj3312
Assessing workload in using electromyography (EMG)-based prostheses
Park, J., Berman, J., Dodson, A., Liu, Y., Armstrong, M., Huang, H., … Zahabi, M. (2023, June 10), ERGONOMICS, Vol. 6. https://doi.org/10.1080/00140139.2023.2221413
Harnessing Machine Learning and Physiological Knowledge for a Novel EMG-Based Neural-Machine Interface
Berman, J., Hinson, R., Lee, I.-C., & Huang, H. (2023), IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 70(4), 1125–1136. https://doi.org/10.1109/TBME.2022.3210892
Hierarchical Optimization for Control of Robotic Knee Prostheses Toward Improved Symmetry of Propulsive Impulse
Li, M., Liu, W., Si, J., Stallrich, J. W., & Huang, H. (2023), IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 70(5), 1634–1642. https://doi.org/10.1109/TBME.2022.3224026
Offline Evaluation Matters: Investigation of the Influence of Offline Performance of EMG-Based Neural-Machine Interfaces on User Adaptation, Cognitive Load, and Physical Efforts in a Real-Time Application
Hinson, R. M., Berman, J., Lee, I.-C., Filer, W. G., & Huang, H. (2023), IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 31, 3055–3063. https://doi.org/10.1109/TNSRE.2023.3297448

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He (Helen) Huang