Michael Muma

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About

Michael Muma is a leading researcher in radar-based sensing and autonomous systems for emergency response and biomedical applications. His work focuses on developing innovative signal processing and machine learning techniques to enable non-contact vital sign estimation and person localization using radar sensors. Muma’s major contributions include the design of semi-autonomous robotic platforms equipped with stepped-frequency continuous wave (SFCW) radar, which can penetrate debris and rubble to detect and monitor trapped victims in disaster scenarios. His most-cited paper, "Emergency Response Person Localization and Vital Sign Estimation Using a Semi-Autonomous Robot Mounted SFCW Radar" (2024, 22 citations), exemplifies his impact by addressing critical challenges in search and rescue, enhancing responder safety and efficiency. Beyond this, Muma has advanced robust statistical methods for radar signal processing, contributing to reliable detection in cluttered environments. His work has garnered attention for its practical implications in humanitarian technology, with citations reflecting its relevance to both academic research and real-world deployment. Muma’s achievements include bridging the gap between theoretical signal processing and field-ready robotic systems, making him a key figure in the evolution of intelligent sensing for life-saving applications.