Alistair A. McEwan

About

Alistair A. McEwan is pioneering the intersection of human-machine interaction and rehabilitation robotics, with a focused expertise in decoding complex human movement from biological signals. His research centers on leveraging deep learning architectures—particularly attention-driven neural networks and bidirectional LSTM models—to achieve continuous, cross-subject estimation of knee joint kinematics from surface electromyogram (sEMG) signals. McEwan’s major contribution lies in enabling accurate and robust joint angle estimation during dynamic, high-impact activities such as running, a critical advancement for controlling rehabilitation robots and restoring motor function in individuals with movement impairments. His most-cited work, an efficient attention-driven deep neural network approach published in 2023, has already garnered 11 citations, underscoring its immediate impact on the field. A subsequent study further refined cross-subject estimation capabilities, demonstrating the generalizability of his methods. By tackling the challenge of real-time, non-invasive kinematic prediction during complex locomotion, McEwan is laying the groundwork for next-generation prosthetic and exoskeleton control systems that adapt seamlessly to natural human movement.

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

Contact & Links