Hang Pham
Papers
3
Total Citations
16
H-Index
3
About
Hang Pham is a researcher at the intersection of human movement science and human-robot interaction, with key contributions in electromyography (EMG)-based gait recognition and risk-aware robotics. Pham’s foundational work on a Locally Linear Embedding (LLE) and Hidden Markov Model (HMM) framework for recognizing human gait from muscle activity—rather than traditional kinematic data—has garnered over 10 citations, establishing a novel approach to decoding movement intention directly from EMG signals. This research, published in 2015, addresses a critical gap in assistive robotics and rehabilitation by enabling robots to anticipate user movement from its physiological source. More recently, Pham has advanced human-robot interaction with a 2024 paper on risk-calibrated, set-valued intent prediction, a method that quantifies uncertainty in human intent to enhance safety in collaborative settings. By combining rigorous signal processing with probabilistic modeling, Pham’s work bridges biomechanics and robotics, offering practical pathways for more intuitive and safer assistive devices. Their research is particularly valuable for students and engineers designing exoskeletons, prosthetics, or collaborative robots that must respond reliably to human muscle signals.
Research Focus
Key Achievements
Top Papers
- 1A LLE-HMM-based framework for recognizing human gait movement from EMG10 citations · 2015
- 2Risk-Calibrated Human-Robot Interaction via Set-Valued Intent Prediction3 citations · 2024
- 3