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Chung Hyun

Chung Hyun Goh

Assistant Professor

Phone: 903.566.6125
Email: cgoh@uttyler.edu
Building:   RBN 1012
Department: Mechanical Engineering

Degrees

  • B.S., Ordnance Engineering, Korea Military Academy, Keoul, Korea, 1987
  • M.S.M.E., Mechanical Engineering, Oklahoma State University, Stillwater, OK, 1992
  • Ph.D., Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 2002

Courses Taught

  • Engineering graphics and design
  • Dynamics of machinery
  • Robotics vision and control
  • System dynamics and control
  • Finite element methods.


Complete Biography

Research Interests

Dr. Goh’s research integrates artificial intelligence and machine learning into generative design workflows to promote sustainability and innovation in engineering. He develops robotic and automated systems for rehabilitation and healthcare, emphasizing adaptive assistance, sensor integration, and human-centered control. His technical expertise includes finite element analysis, multi-body dynamics, and multi-physics simulation to model biomechanical and electromechanical systems. He also designs smart medical devices with embedded sensors and real-time adaptive control, and applies computational mechanics and optimization techniques to enhance performance and efficiency across complex engineered systems.

Dr. Chung-Hyun Goh is leading the development of an AI-integrated Robotic Walking Training Device (RWTD) designed for gait rehabilitation in elderly and disabled individuals. His research bridges biomechanics, robotics, and machine learning to create adaptive and safe training environments.

Key Features:

  • Multi-joint actuation (hip, knee, ankle) enabling lifelike gait cycle replication.
  • Real-time EMG and joint feedback for closed-loop control.
  • AI-enhanced gait pattern classification and personalized progression plans.
  • Simulink-based musculoskeletal simulation for design validation and inverse dynamics analysis.
  • Fall detection and recovery system using ML classifiers such as SVM.

Core Objectives:

  1. Restore ambulatory function in neurorehabilitation settings.
  2. Enhance motor learning via AI-guided repetition and correction.
  3. Integrate IoT-enabled feedback for remote supervision and tele-rehabilitation.

Robotic Walking Training Device[ 3D CAD Rendering of the Robotic Walking Training Device ]

Trajectory Prediction

Trajectory Prediction

[ Gait Trajectory Prediction using ML: Knee Joint (Top) and Ankle Joint (Bottom)]

Awards and Honors

  • UT Tyler “Crystal Talon” Outstanding Scholarship & Creativity Award (2017)
  • Paralyzed Veterans of America (PVA) Education Award (2017)
  • Papers of Distinction, 43rd ASME Design Automation Conference (2017)
  • NSF Summer Institute Fellowships (2013)
  • Republic of Korea Prime Minister Award (2012)

Selected Publications

  • Stroud, J., Yan, E.T., Anthony, J., Walker, K., Goh, C.H. (2025). Optimizing Screw Fixation in Total Hip Arthroplasty: A Deep Learning and Finite Element Analysis Approach. Applied Sciences, 15, 3722. https://doi.org/10.3390/app15073722.
  • Jeon, W., Dalby, A., Dong, X.N., Goh, C.H. (2025). Effects of Initial Foot Position on Neuromuscular and Biomechanical Control During Stand-to-Sit Movement. PLoS ONE, 20(2): e0315738.
  • Jeon, W., Dong, X.N., Dalby, A., Goh, C.H. (2024). The Influence of Smoothness and Speed of Stand-to-Sit Movement on Joint Kinematics, Kinetics, and Muscle Activation Patterns. Frontiers in Human Neuroscience, 18, Article 1399179.
  • Anthony, J., Goh, C.H., Yazdanshenas, A., Wang, Y.T. (2024). Redesign of Leg Assembly and Implementation of Reinforcement Learning for a Multi-Purpose Rehabilitation Robotic Device. Applied Sciences, 14(2), 516–528.
  • Bourgeois, A., Rice, B., Goh, C.H. (2023). Design Optimization of the Lift Mechanism in a Robotic Walking Training Device. Applied Sciences, 14(1), 327–347.
  • Bumbard, K.B., Herrington, H., Ibrahim, A., Goh, C.H. (2022). Incorporation of Torsion Springs in a Knee Exoskeleton for Crouch Gait Correction. Applied Sciences, 12, 7034–7048.
  • Anthony, J., Dixon, A., Goh, C.H., Lucci, M. (2022). Feedback Control of Medication Delivery Device Using Machine Learning-Based Control Co-Design. Journal of Software Engineering and Applications, 15, 220–239.
  • Goh, C.H., McDowell, D.L., Neu, R.W. (2006). Plasticity in Polycrystalline Fretting Fatigue Contacts. Journal of the Mechanics and Physics of Solids, 54(2), 340–367.
  • Goh, C.H., Neu, R.W., McDowell, D.L. (2003). Crystallographic Plasticity in Fretting of Ti-6Al-4V. International Journal of Plasticity, 19(10), 1627–1650.

Curriculum Vitae
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