A Novel Physical Human–Robot Interface With Pressure Distribution Measurement Based on Electrical Impedance Tomography

Measurements of pressure distribution on physical human–robot interfaces are crucial for ensuring the comfort and safety of human–exoskeleton interactions. To address this need, we propose a novel sensorized physical human–robot interface with pressure distribution measurement based on electrical impedance tomography (EIT). EIT sensors have the advantages of a simple structure and the ability to continuously measure pressure over a large area, making it a promising solution for wearable robots. A piezoresistive composite made of carbon black (CB) and flexible polymer was fabricated and embedded in a physical interface to compose the EIT sensor. To improve the spatial solution of the EIT inverse problem, a convolutional neural network (CNN)-enhanced Tikhonov regularization (referred to as CNN-TR) approach was adopted. The original compressed image is first reconstructed with Tikhonov regularization and then enhanced by the CNN model that is trained with simulation data. Then, a validation platform is built based on an MARK-10 force tester. The experimental results showed that the developed EIT sensor with CNN-TR reconstruction method achieves accurate localization and size estimation of the compressed area and is capable of distinguishing multiple compressed areas. In addition, the EIT sensor is embedded into a physical interface to measure the compressed areas between the physical interface and the human lower limb. The results validate that the proposed EIT pressure sensor is suitable for physical interfaces.