Simen Birkeland Skriubakken

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About

Simen Birkeland Skriubakken is a researcher at the forefront of applied deep learning and edge computing, with a primary focus on human-computer interaction and assistive technologies. His most cited work, "Deep Learning-Based Sign Language Digits Recognition From Thermal Images With Edge Computing System" (2021, 70 citations), represents a significant contribution to accessible communication technologies. This study pioneered the use of thermal imaging—rather than conventional RGB cameras—for recognizing sign language digits, addressing critical privacy and low-light limitations in real-world settings. By integrating deep neural networks with edge computing systems, Skriubakken demonstrated how gesture recognition could be performed locally on resource-constrained devices, enabling faster, more private, and more practical deployments in health assistive technologies, robotics, and crisis management. His work bridges the gap between cutting-edge AI and tangible, inclusive applications, particularly for individuals with hearing impairments. With his research appearing in high-impact venues and garnering steady citations, Skriubakken is establishing himself as an innovator in making intelligent systems more accessible, responsive, and respectful of user privacy—a vital direction for next-generation human-machine interfaces.