MWCNT-coated yarn-based force sensors were effectively produced through a straightforward dipping-and-drying technique. The incorporation of a parallel capacitor enabled an impedance-based sensing approach that leverages variations in the area of Cole–Cole plots. This yielded a sensitivity of ∼0.8 kPa−1—substantially outperforming the sensitivity of roughly 0.49 kPa−1 of conventional resistance measurements—owing to the quadratic dependence of the visualized area variation of impedance data on the resistance ratio. Finally, a real-time impedance data detection system, utilizing an Arduino micro-controller and deep learning method, was demonstrated for a wide range of applications, such as wearable and healthcare devices.
Kim et al. (Wed,) studied this question.