Human Motor Augmentation (HMA) is an engineering field aimed at enhancing human capabilities beyond the able-bodied spectrum via technology, focusing on manipulation and locomotion. This is theoretically feasible due to task null spaces — variable spaces permitting motor variations without impacting concurrent and related physiological functions. These spaces span neural, kinematic, and muscular variables. This thesis explores if two distinct task null spaces — a kinematic one defined by diaphragmatic modulation, and a muscular one defined by electromyographic (EMG) signals from auricular muscles (AMs) — can be integrated to create a two degrees of freedom (2DoFs) human-machine interface (HMI) preserving essential null space properties.