Deni Andrean

Papers

Total Citations

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About

Deni Andrean is a researcher in biomedical robotics and human-machine interfaces, with a focus on intelligent prosthetic control systems. His most cited work, "Controlling Robot Hand Using FFT as Input to the NN Algorithm" (2019, 9 citations), introduces a novel approach to prosthetic hand control by combining Fast Fourier Transform (FFT) signal processing with Neural Network (NN) algorithms. Using Myo Arm Sensors to capture electromyographic (EMG) signals, Andrean developed a system that interprets muscle movement frequencies to enable intuitive, real-time control of robotic hands. This work bridges the gap between raw biosignal acquisition and machine learning-based actuation, offering a more responsive and natural interface for amputees. By training neural networks on frequency-domain features rather than raw EMG data, his method improves classification accuracy and reduces computational load—a significant step toward practical, wearable prosthetics. Andrean’s contributions sit at the intersection of signal processing, neural computation, and assistive technology, demonstrating how embedded intelligence can restore dexterous function. His research continues to inspire advances in low-cost, adaptive prosthetic systems that learn from user physiology.