Kanna Nakayama
Papers
2
Total Citations
20
H-Index
2
About
Kanna Nakayama’s research sits at the intersection of robotics, biomechanics, and neural control, where she tackles the fundamental challenge of redundancy in movement. Her most cited work (2014, 17 citations) pioneers the extraction and implementation of muscle synergies—neural building blocks that simplify the control of complex, multi-joint upper limb movements. By decoding human electromyographic signals, she not only identified these synergies but also physically interpreted them, offering a biologically inspired framework for neuro-mechanical control in robots. This approach directly addresses how to manage the many degrees of freedom in a force-producing task, a core problem in robotics. Nakayama further advanced the field with her 2013 work introducing the “agonist-antagonist ratio (A-A ratio)” and “agonist-antagonist activity (A-A activity).” Drawing an elegant analogy between human and robotic systems, she demonstrated a linear relationship in antagonistic-driven control—a concept akin to the equilibrium point hypothesis but with clearer, more implementable linearity. Her contributions are foundational for developing intuitive EMG interfaces and more natural prosthetic control, bridging the gap between biological motor control and robotic actuation.
Research Focus
Key Achievements
Top Papers
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