Hydrogel Pressure Distribution Sensors Based on an Imaging Strategy and Machine Learning

A flexible hydrogel pressure distribution sensor has promising application prospects. However, the current hydrogel pressure distribution sensors are based on an array-type structure with complicated wires and extremely low resolution, which greatly reduce the flexibility of hydrogel and limit their applications. To overcome these limitations, we proposed and designed a hydrogel pressure distribution sensor that is able to obtain pressure distribution inside a whole piece of hydrogel. This is specifically done by arranging electrodes only around the hydrogel, which employs the strategy of electrical impedance tomography (EIT). Meanwhile, PAAm/PAA-Fe³⁺ double-network hydrogels were prepared as hydrogel pressure-sensitive substrates, and the feasibility of PAAm/PAA-Fe³⁺ hydrogels as sensitive elements for hydrogel pressure distribution sensors was confirmed through mechanical and electrical tests. Furthermore, based on the hydrogel pressure distribution sensor, a pressure distribution reconstruction model was obtained with a machine learning method. To verify the feasibility of the hydrogel pressure distribution sensor based on the EIT strategy, the hydrogel sensor was applied with forces of known location and magnitude. Then, the actual sensor acquisition data were reconstructed and compared with the applied force.