Soma Fumoto
Papers
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Total Citations
1
H-Index
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About
Soma Fumoto is a researcher advancing human-robot interaction through innovative action recognition systems. Their work focuses on enabling service robots to understand human intentions by recognizing gestures and movements in real-world environments. Fumoto’s most-cited paper, “Lightweight Hand-Waving Action Recognition Using Reservoir Computing in a Cafeteria Environment” (2025), addresses the critical challenge of deploying efficient, low-computational recognition systems in dynamic settings like cafeterias. By leveraging reservoir computing, a machine learning approach suited for temporal data, Fumoto demonstrates how robots can accurately interpret simple human gestures—such as hand-waving—without requiring extensive hardware resources. This contribution is particularly relevant amid global labor shortages and the push for operational efficiency in service robotics. While early in their career, Fumoto’s work lays a foundation for more intuitive human-robot collaboration, emphasizing practical, lightweight solutions that can be embedded directly into robotic systems. Their research holds promise for making service robots more responsive and context-aware in everyday environments, bridging the gap between complex machine learning models and real-time, on-device deployment.
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