A Novel Approach to Quantum-Enhanced EMG Signal Processing for Pediatric Upper Limb Prosthetic Control
Abstract
This paper focuses on motor control difficulties of CULD chiLdren. Classical myoelectric prostheses often become inadequate due to the weak and underdeveloped signals from new neuromuscular systems and have a high rejection rate. We present a quantum-inspired prosthetic control approach to enhance weak myoelectric signals in children from 3 to $\mathbf{1 6}$ years of age employing quantum neural networks. Capitalizing on superposition and entanglement, the device combines EMBA and pressure readings to discern voluntary movement from activity due to growth or fatigue. In a clinical trial with 32 children, training time decreased from 6 weeks to $\mathbf{1 0}$ days, while grip recognition accuracy increased to $\mathbf{9 1 \%}$, compared to only $\mathbf{5 8 \%}$ in standard devices. The system performed well on fine motor tasks considered important for learning and play. Perhaps the last remaining hurdles to overcome are figuring out how to maintain reliability during growth spurts, and how to shrink quantum processors down for pediatric use.
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