About

Hsiao-Ying Shadow Huang is an Associate Professor in the Department of Mechanical and Aerospace Engineering at NC State University. She possesses a broad and interdisciplinary background in applied mechanics and the computational modeling of diverse material systems. Her teaching and research span the areas of mechanics of materials, non-equilibrium thermodynamics, continuum mechanics, and nonlinear elasticity. Dr. Huang's research program focuses on elucidating electrochemical–mechanical interactions in energy materials, including lithium-ion batteries and emerging systems beyond lithium, as well as investigating the structures and mechanics of biological materials. Her scholarly contributions have been recognized through numerous honors, including the Presidential Early Career Award for Scientists and Engineers (PECASE, 2017) from the White House, the NSF CAREER Award (2016), and the Outstanding Teacher Award (2020) at NC State University. She has served as Director of the MS Non-Thesis Program (2023-2026) in the MAE Department, Faculty Fellow for External Awards (2023-2024) in the Office for Faculty Excellence, and held the position of Associate Director of the Analytical Instrumentation Facility from 2018 to 2021. Dr. Huang teaches undergraduate courses such as Solid Mechanics and Strength of Mechanical Components, as well as graduate courses including Advanced Solid Mechanics and Modern Plasticity.

Research topics

• Computer Science
• Artificial Intelligence
• Medicine
• Physical medicine and rehabilitation
• Biomedical engineering
• Neuroscience
• Psychology
• Human–computer interaction
• Simulation
• Anatomy
• Engineering
• Mathematics
• Computer hardware
• Embedded system
• Structural engineering
• Physics

Selected publications

• Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions

  Journal of Neural Engineering · 2021 · 207 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  This review may guide the future collaborations among researchers in neuromechanics, neural engineering, assistive technologies, and amputee clinics in order to build and translate true bionic lower limbs to individuals with lower limb amputations for improved motor function.

  DOI

• Toward higher-performance bionic limbs for wider clinical use

  Nature Biomedical Engineering · 2021 · 261 citations

  • Computer Science
  • Artificial Intelligence
  • Human–computer interaction
  PublisherOA PDFDOI

• Using Reinforcement Learning to Estimate Human Joint Moments From Electromyography or Joint Kinematics: An Alternative Solution to Musculoskeletal-Based Biomechanics

  Journal of Biomechanical Engineering · 2020 · 37 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Physical medicine and rehabilitation

  Reinforcement learning (RL) has potential to provide innovative solutions to existing challenges in estimating joint moments in motion analysis, such as kinematic or electromyography (EMG) noise and unknown model parameters. Here, we explore feasibility of RL to assist joint moment estimation for biomechanical applications. Forearm and hand kinematics and forearm EMGs from four muscles during free finger and wrist movement were collected from six healthy subjects. Using the proximal policy optimization approach, we trained two types of RL agents that estimated joint moment based on measured kinematics or measured EMGs, respectively. To quantify the performance of trained RL agents, the estimated joint moment was used to drive a forward dynamic model for estimating kinematics, which was then compared with measured kinematics using Pearson correlation coefficient. The results demonstrated that both trained RL agents are feasible to estimate joint moment for wrist and metacarpophalangeal (MCP) joint motion prediction. The correlation coefficients between predicted and measured kinematics, derived from the kinematics-driven agent and subject-specific EMG-driven agents, were 98% ± 1% and 94% ± 3% for the wrist, respectively, and were 95% ± 2% and 84% ± 6% for the metacarpophalangeal joint, respectively. In addition, a biomechanically reasonable joint moment-angle-EMG relationship (i.e., dependence of joint moment on joint angle and EMG) was predicted using only 15 s of collected data. In conclusion, this study illustrates that an RL approach can be an alternative technique to conventional inverse dynamic analysis in human biomechanics study and EMG-driven human-machine interfacing applications.

  DOI

Recent grants

• Integrating Human Wearers' Perception and Cognition into Prosthesis Control Policy

  NSF · $788k · 2019–2023

• CAREER: Understanding and Analyzing User-Prosthesis Interaction for Designing a Volitional Controller for Powered Lower Limb Prostheses

  NSF · $490k · 2013–2018

• CHS: Medium: A Bi-directional Neural Interface for Bionic Prosthetic Legs

  NSF · $1.1M · 2020–2025

• Collaborative Research: SCH: Improving Older Adults' Mobility and Gait Ability in Real-World Ambulation with a Smart Robotic Ankle-Foot Orthosis

  NSF · $720k · 2023–2027

• CAREER: Understanding and Analyzing User-Prosthesis Interaction for Designing a Volitional Controller for Powered Lower Limb Prostheses

  NSF · $207k · 2012–2014

Frequent coauthors

• Ming Liu

  North Carolina State University

  165 shared

• Minhan Li

  112 shared

• Varun Nalam

  92 shared

• I‐Chieh Lee

  North Carolina State University

  76 shared

• Jennie Si

  Arizona State University

  72 shared

• Xiaogang Hu

  Pennsylvania State University

  68 shared

• Fan Zhang

  57 shared

• Dustin L. Crouch

  Knoxville College

  53 shared

Awards & honors

• Presidential Early Career Award for Scientists and Engineers…
• NSF CAREER Award (2016)
• Outstanding Teacher Award (2020) at NC State University

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