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Total Citations
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About
B. K. Aamod is a researcher at the forefront of robotic rehabilitation and human-machine interaction, with a focus on advancing stroke therapy through intelligent control systems. Their key research areas include model predictive control, surface electromyography (sEMG) signal processing, and edge-based machine learning for rehabilitative robotics. Aamod’s major contribution lies in developing a novel approach that integrates convolutional neural networks (CNNs) with sEMG data to predict limb angles in real time, enabling robotic systems to adapt to a patient’s voluntary movements during therapy. This work, published in 2024 and already garnering 2 citations, addresses a critical challenge in later-stage rehabilitation: allowing patients to move freely when no assistance is needed, thereby promoting active participation and more effective recovery. By advancing model predictive control for robotic rehabilitative systems, Aamod’s research bridges the gap between assistive technology and patient autonomy, offering a promising pathway toward personalized, responsive stroke rehabilitation. Their work is particularly notable for its focus on edge computing, ensuring low-latency, practical deployment in clinical settings.
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Top Papers
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