Bio-impedance identification of fingertip skin for enhancement of electro-tactile-based preference

Research in rehabilitation engineering has shown that electrodes can produce tactile sensations with appropriate electrical signals to stimulate the multiple tactile receptors located under the fingertip skin. Numerous equivalent skin–electrode interfaces have been modeled to characterize the electrical properties of the skin; however, the values of these circuit models are continually changing, due both to the nonlinearity associated with human fingertip skin and to individual user differences. As a result, electrical stimulation that is suitable in terms of current or voltage level for tactile sensations cannot be guaranteed for every user. An identification method is then necessary for characterizing the parameters of the skin–electrode interface circuit model so as to improve rendering consistency and comfort for every user regardless of skin condition. In this paper, we introduce a custom-built electro-tactile display terminal, and then using this display terminal for data collection, we present an online identification scheme for determining the bio-impedance parameters of the well-known Cole–Cole circuit model for the skin–electrode interface. For this, a modified Kalman least squares iterative approach is used that relies on measuring only one-port square wave stimulation voltages. The repeatability and reliability of the identification scheme are tested by identifying the resistor–capacitor (RC) load bio-impedance networks of different users with both a dry and slightly damp index fingertip over multiple identification trials. Additionally, because of the inevitable variation in the parameters over multiple measurements, the repeatability of multiple calculated RC models (dry and wet) is further evaluated. The significance of our work is that it greatly improves the tactile rendering performance of electrical stimulation (electro-tactile) systems and will benefit the development of electro-tactile-based rehabilitative robotic devices and human–robot interfaces.