Yee Mon Aung

About

Yee Mon Aung is a leading researcher in the field of biomedical engineering, specializing in the development of intelligent, human-machine interfaces for rehabilitation robotics. Her core research focuses on harnessing surface electromyography (sEMG) signals to decode human intent, enabling more intuitive and effective assistive technologies for individuals with physical impairments. Aung’s major contributions lie in the application of advanced neural networks—such as Artificial Neural Networks (ANN) and Generalized Regression Neural Networks (GRNN)—to accurately predict joint angles from muscle activity. Her seminal 2013 paper, "Estimation of Upper Limb Joint Angle Using Surface EMG Signal," which has garnered 70 citations, established a foundational method for using EMG to detect user-intended motion in robot-assisted upper limb therapy. This work is complemented by her 2012 study on shoulder angle prediction (40 citations) and her 2015 investigation into knee joint angle estimation. Collectively, her research has significantly advanced the field of "assist-as-needed" rehabilitation, providing the critical algorithms that allow robotic devices to respond seamlessly to a patient’s voluntary muscle signals, thereby promoting faster and more natural recovery of motor function.

Research Focus

Key Achievements

Top Papers

1. 1
   Estimation of Upper Limb Joint Angle Using Surface EMG Signal

   70 citations · 2013
2. 2
   sEMG Based ANN for Shoulder Angle Prediction

   40 citations · 2012
3. 3
   The estimation of Knee Joint angle based on Generalized Regression Neural Network (GRNN)

   8 citations · 2015

Key Collaborators

Contact & Links