Azar Ghamangiz Nosheri

About

Azar Ghamangiz Nosheri is a researcher at the forefront of human-computer interaction and biomedical signal processing, with a specialized focus on electromyography (EMG)-based gesture recognition. Her most cited work, "Hand Movements Detection Using EMG Signals for Human-Computer Interface and Convolution Neural Network" (2024), introduces a robust CNN-based framework for decoding hand movements from EMG signals. This study, involving 40 participants and Myo armband recordings, demonstrates a three-layer convolutional architecture that achieves high accuracy in real-time gesture classification—a critical step toward intuitive, non-invasive control interfaces for prosthetics and assistive technologies. While her citation count is still growing, the work’s practical methodology and clear translational potential mark a significant contribution to the field. Nosheri’s research bridges machine learning and neural engineering, offering scalable solutions for next-generation human-machine interfaces. Her efforts are paving the way for more natural, responsive systems that could empower individuals with motor impairments, making her a promising voice in the evolving landscape of biosignal-based interaction.

Research Focus

Key Achievements

Top Papers

1. 1
   Hand Movements Detection Using EMG Signals for Human-Computer Interface and convolution neural network

   3 citations · 2024

Key Collaborators

Contact & Links