Predicción anticipada de intención motora para prótesis rápidas mediante un modelo neuronal híbrido con EMG y presión.

Las prótesis mioeléctricas actuales presentan una latencia de control que degrada la sensación de agencia. Este trabajo propone un enfoque multimodal para inferir la intención motora en fase transitoria, combinando señales sEMG con presión. Se propone una arquitectura híbrida (TCN/Attention) con cuantificación de incertidumbre (MC-Dropout). Validado con NinaPro y GRABMyo, el sistema logra latencias <150 ms. Se incluye verificación formal matemática mediante Lean 4.

Keywords

EMG, Deep Learning, Prótesis, Lean 4, Neurociencia

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