Özet:

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 applying appropriate boundary conditions and simulation settings. The computational models are composed of a volume conductor of the human arm model and electrode settings. Also, two different computational study methods were used to determine the influence of the tissues? dielectric properties on the results. The quasistatic approximation was used by only considering the resistivity of the anatomical layers and the transient simulation was used to analyze the capacitive impact on the results. Finite element (FE) models were developed to simulate the potential distribution inside the skin, muscle, and bone layers of the upper arm for given electrode settings. Then, simulation results were recorded for various electrical properties and different study types. It was shown that the capacitive influence of the tissue may not be ignored for certain conditions due to significant variation in the induced electrical potential variation along with the target muscle. Also, the influence of the individual tissue?s electrical properties was investigated using a set of dielectric parameters. The results showed that the skin and muscle layers have a significant impact on the electrical potential variation across the muscle length.

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