Alireza Rezaie Zangene

About

Alireza Rezaie Zangene is a researcher at the forefront of human-machine interaction and rehabilitation robotics, specializing in the continuous estimation of joint kinematics from surface electromyogram (sEMG) signals. His work focuses on developing advanced deep learning architectures to decode complex human movements, particularly during dynamic activities like running. Zangene’s major contributions include pioneering attention-driven neural network models, such as an efficient attention-based deep neural network and an attention-based bidirectional LSTM, which enable accurate, cross-subject estimation of knee joint angles and kinematics. These models address critical challenges in controlling rehabilitation robots for motor function restoration, offering robust performance even during high-intensity locomotion. His most cited paper (2023, 11 citations) demonstrates the power of attention mechanisms in improving sEMG-based joint estimation, while his subsequent work (2023, 3 citations) extends this to continuous, cross-subject applications. By integrating attention and recurrent neural networks, Zangene’s research advances the field of intelligent prosthetics and exoskeletons, paving the way for more intuitive and adaptive human-machine interfaces that can restore mobility in individuals with motor impairments.

Research Focus

Key Achievements

Top Papers

1. 1
   An efficient attention-driven deep neural network approach for continuous estimation of knee joint kinematics via sEMG signals during running

   11 citations · 2023
2. 2
   An Attention-based Bidirectional LSTM Model for Continuous Cross-Subject Estimation of Knee Joint Angle during Running from sEMG Signals

   3 citations · 2023

Key Collaborators

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