Upper limb loss has a significant impact on individual socioeconomic life. Human-machine interface (HMI) using surface electromyography (sEMG) establishes a link between the user and a hand prosthesis to recognize hand gestures and motions which allows the control of robotic machines and prostheses to perform dexterous tasks. Numerous methods aimed to enhance hand gesture and motion recognition toward an HMI. Bio-impedance analysis (BIA) is a noninvasive way of assessing body compositions and has been recently used for hand motion interpretation using `brute force? pattern recognition. The impedance variation in the body mostly depends on the precise stimulation using appropriate electrical features of the associated tissue layers. It has been reported that the electrical properties of these layers varied significantly. Thus, it is essential to investigate the influence of these variations on the stimulator design for the hand motion interpretation. This may not be possible using experimental approaches. Alternatively, using highly advanced computational models, this can be readily investigated by attaining the available range of the electrical properties of each tissue layer and a
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