He (Helen) Huang
- Email: hhuang11@ncsu.edu
- Office: 4402D Engineering Building III
- Website: https://bme.unc.edu/peoples/he-helen-huang/
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
Publications
- 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
- 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
- Offline Evaluation Matters: Investigation of the Influence of Offline Performance on Real-Time Operation of Electromyography-Based Neural-Machine Interfaces
- Hinson, R. M., Berman, J., Filer, W., Kamper, D., Hu, X., & Huang, H. (2023), IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 31, 680–689. https://doi.org/10.1109/TNSRE.2022.3226229
- Reinforcement Learning Control With Knowledge Shaping
- Gao, X., Si, J., & Huang, H. (2023), IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2023.3243631
