Lindsey Kent

About

Lindsey Kent’s research lies at the intersection of biomedical signal processing and neural network modelling, with a particular focus on the biomechanics of human movement. Her most cited work, “Modelling Of Muscle EMG To Torque By The Neural Network Model Of Backpropagation” (2005), addresses the complex, nonlinear relationship between electromyographic (EMG) signals and joint torque. In this study, Kent applied a multilayer perceptron trained via backpropagation to map EMG activity from the ankle joint to isometric torque under supine conditions. This work demonstrated the feasibility of using artificial neural networks to decode neuromuscular signals, offering a computational framework that could advance prosthetic control and rehabilitation technologies. While her citation count remains modest, her early adoption of machine learning for biomechanical modelling reflects a forward-thinking approach in a field that has since embraced deep learning. Kent’s contribution is notable for its methodological clarity and its role in bridging neural network theory with practical, physiological measurement—a foundation that continues to inform research in human-machine interfaces and motor control.

Research Focus

Key Achievements

Top Papers

1. 1
   Modelling Of Muscle EMG To Torque By The Neural Network Model Of Backpropagation

   4 citations · 2005

Key Collaborators

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