Hao Su

Zhang, S., Zhu, J., Huang, T.-H., Yu, S., Sen Huang, J., Lopez-Sanchez, I., … Su, H. (2024). Actuator optimization and deep learning-based control of pediatric knee exoskeleton for community-based mobility assistance. MECHATRONICS, 97. https://doi.org/10.1016/j.mechatronics.2023.103109

Liu, W., Liu, C., Sajedi, S., Su, H., Liang, X., & Zheng, M. (2024). An audio-based risky flight detection framework for quadrotors. IET CYBER-SYSTEMS AND ROBOTICS, 6(1). https://doi.org/10.1049/csy2.12105

Kruse, D., Schur, L., Johnson-Marcus, H.-A., Gilbert, L., Di Lallo, A., Gao, W., & Su, H. (2024, January 22). Assistive Technology's Potential to Improve Employment of People with Disabilities. JOURNAL OF OCCUPATIONAL REHABILITATION, Vol. 1. https://doi.org/10.1007/s10926-023-10164-w

Xing, X., Zhang, S., Huang, T., Huang, J. S., Su, H., & Li, Y. (2024, March 14). Spatial Iterative Learning Torque Control of Robotic Exoskeletons for High Accuracy and Rapid Convergence Assistance. IEEE-ASME TRANSACTIONS ON MECHATRONICS, Vol. 3. https://doi.org/10.1109/TMECH.2024.3365045

Yu, S., Yang, J., Huang, T.-H., Zhu, J., Visco, C. J., Hameed, F., … Su, H. (2023, January 21). Artificial Neural Network-Based Activities Classification, Gait Phase Estimation, and Prediction. ANNALS OF BIOMEDICAL ENGINEERING, Vol. 1. https://doi.org/10.1007/s10439-023-03151-y

Xiao, Q., Musa, M., Godage, I. S., Su, H., & Chen, Y. (2023). Kinematics and Stiffness Modeling of Soft Robot With a Concentric Backbone. JOURNAL OF MECHANISMS AND ROBOTICS-TRANSACTIONS OF THE ASME, 15(5). https://doi.org/10.1115/1.4055860

Wang, D., Li, J., Jian, Z., Su, H., Wang, H., & Fang, F. (2023). Modeling and control of a bedside cable-driven lower-limb rehabilitation robot for bedridden individuals. FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 11. https://doi.org/10.3389/fbioe.2023.1321905

Zhao, Y., Hong, Y., Li, Y., Qi, F., Qing, H., Su, H., & Yin, J. (2023). Physically intelligent autonomous soft robotic maze escaper. SCIENCE ADVANCES, 9(36). https://doi.org/10.1126/sciadv.adi3254

Luo, S., Androwis, G., Adamovich, S., Nunez, E., Su, H., & Zhou, X. (2023). Robust walking control of a lower limb rehabilitation exoskeleton coupled with a musculoskeletal model via deep reinforcement learning. JOURNAL OF NEUROENGINEERING AND REHABILITATION, 20(1). https://doi.org/10.1186/s12984-023-01147-2

Normand, M. A., Lee, J., Su, H., & Sulzer, J. S. (2023). The effect of hip exoskeleton weight on kinematics, kinetics, and electromyography during human walking. JOURNAL OF BIOMECHANICS, 152. https://doi.org/10.1016/j.jbiomech.2023.111552

Chen, Z., Liu, C., Su, H., Liang, X., & Zheng, M. (2022). A hybrid disturbance observer for delivery drone?s oscillation suppression. MECHATRONICS, 88. https://doi.org/10.1016/j.mechatronics.2022.102907

Buckner, C., Miikkulainen, R., Forrest, S., Milano, S., Zou, J., Prunk, C., … Marda, V. (2022, January 25). AI reflections in 2021. NATURE MACHINE INTELLIGENCE, Vol. 1. https://doi.org/10.1038/s42256-021-00435-7

Gonzalez, M., Su, H., & Fu, Q. (2022). Age-Dependent Upper Limb Myoelectric Control Capability in Typically Developing Children. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 30, 1009–1018. https://doi.org/10.1109/TNSRE.2022.3166800

Yu, S., Huang, T.-H., Di Lallo, A., Zhang, S., Wang, T., Fu, Q., & Su, H. (2022). Bio-inspired design of a self-aligning, lightweight, and highly-compliant cable-driven knee exoskeleton. FRONTIERS IN HUMAN NEUROSCIENCE, 16. https://doi.org/10.3389/fnhum.2022.1018160

Zhu, J., Jiao, C., Dominguez, I., Yu, S., & Su, H. (2022, June 3). Design and Backdrivability Modeling of a Portable High Torque Robotic Knee Prosthesis With Intrinsic Compliance for Agile Activities. IEEE-ASME TRANSACTIONS ON MECHATRONICS, Vol. 6. https://doi.org/10.1109/TMECH.2022.3176255

Chen, Y., Li, X., Su, H., Zhang, D., & Yu, H. (2022). Design of a Bio-Inspired Gait Phase Decoder Based on Temporal Convolution Network Architecture With Contralateral Surface Electromyography Toward Hip Prosthesis Control. FRONTIERS IN NEUROROBOTICS, 16. https://doi.org/10.3389/fnbot.2022.791169

Bulea, T. C., Sharma, N., Sikdar, S., & Su, H. (2022, June 22). Editorial: Next Generation User-Adaptive Wearable Robots. FRONTIERS IN ROBOTICS AND AI, Vol. 9. https://doi.org/10.3389/frobt.2022.920655

Afzal, T., Zhu, F., Tseng, S.-C., Lincoln, J. A., Francisco, G. E., Su, H., & Chang, S.-H. (2022). Evaluation of Muscle Synergy During Exoskeleton-Assisted Walking in Persons With Multiple Sclerosis. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 69(10), 3265–3274. https://doi.org/10.1109/TBME.2022.3166705

Chang, S.-H., Tseng, S.-C., Su, H., & Francisco, G. E. (2022, October 11). How can wearable robotic and sensor technology advance neurorehabilitation? FRONTIERS IN NEUROROBOTICS, Vol. 16. https://doi.org/10.3389/fnbot.2022.1033516

Huang, T.-H., Zhang, S., Yu, S., MacLean, M. K., Zhu, J., Di Lallo, A., … Su, H. (2022). Modeling and Stiffness-Based Continuous Torque Control of Lightweight Quasi-Direct-Drive Knee Exoskeletons for Versatile Walking Assistance. IEEE TRANSACTIONS ON ROBOTICS, 38(3), 1442–1459. https://doi.org/10.1109/TRO.2022.3170287

Zhao, Y., Hong, Y., Qi, F., Chi, Y., Su, H., & Yin, J. (2022, December 27). Self-Sustained Snapping Drives Autonomous Dancing and Motion in Free-Standing Wavy Rings. ADVANCED MATERIALS, Vol. 12. https://doi.org/10.1002/adma.202207372

Su, H., Kwok, K.-W., Cleary, K., Iordachita, I., Cavusoglu, M. C., Desai, J. P., & Fischer, G. S. (2022, May 3). State of the Art and Future Opportunities in MRI-Guided Robot-Assisted Surgery and Interventions. PROCEEDINGS OF THE IEEE, Vol. 5. https://doi.org/10.1109/JPROC.2022.3169146

Li, X., He, B., Deng, Z., Chen, Y., Wang, D., Fan, Y., … Yu, H. (2021). A Center of Mass Estimation and Control Strategy for Body-Weight-Support Treadmill Training. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 29, 2388–2398. https://doi.org/10.1109/TNSRE.2021.3126104

Chen, S., Stevenson, D. T., Yu, S., Mioskowska, M., Yi, J., Su, H., & Trkov, M. (2021, August). Wearable Knee Assistive Devices for Kneeling Tasks in Construction. IEEE-ASME TRANSACTIONS ON MECHATRONICS, Vol. 26, pp. 1989–1996. https://doi.org/10.1109/TMECH.2021.3081367

Tang, Y., Chi, Y., Sun, J., Huang, T.-H., Maghsoudi, O. H., Spence, A., … Yin, J. (2020). Leveraging elastic instabilities for amplified performance: Spine-inspired high-speed and high-force soft robots. SCIENCE ADVANCES, 6(19). https://doi.org/10.1126/SCIADV.AAZ6912

Comfort-Centered Design of a Lightweight and Backdrivable Knee Exoskeleton. (2018). IEEE Robotics and Automation Letters (RA-L).

Su, H., Iordorchita, I., Tokuda, J., Hata, N., Liu, X., & Fischer, G. S. (2017). Fiber Optic Force Sensors for MRI-Guided Interventions and Rehabilitation. IEEE Sensors Journal.

Su, H., Shang, W., Li, G., & Fischer, G. S. (2017). MRI-Guided Telesurgery System Using a Fabry-Perot Interferometry Force Sensor and a Pneumatic Haptic Device. Annals of Biomedical Engineering.

Su, H., Li, G., Rucker, D. C., Webster, R. J., III, & Fischer, G. S. (2016). A Concentric Tube Continuum Robot with Piezoelectric Actuation for MRI-Guided Closed-Loop Targeting. Annals of Biomedical Engineering.

Piezoelectrically Actuated Robotic System for MRI-Guided Prostate Percutaneous Therapy. (2015). IEEE/ASME Transactions on Mechatronics. https://doi.org/10.1109/TMECH.2014.2359413

Su, H., Shang, W., Cole, G., Li, G., Harrington, K., Camilo, A., … Fischer, G. S. (2015). Robotic System for MRI-Guided Stereotactic Neurosurgery. IEEE/ASME Transactions on Mechatronics.

A MRI-Guided Concentric Tube Continuum Robot with Piezoelectric Actuation: A Feasibility Study. (2012). IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/ICRA.2012.6224550

An Affordable Compact Humanoid Robot for Autism Spectrum Disorder Interventions in Children. (2011). Annual International Conference of the IEEE Engineering in Medicine and Biology. https://doi.org/10.1109/IEMBS.2011.6091316

Real-time MRI-Guided Needle Placement Robot with Integrated Fiber Optic Force Sensing. (2011). IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/ICRA.2011.5979539