The field of tactile sensing and haptic feedback in robotics and automotive design is experiencing significant advancements, driven by innovative approaches to data analysis and sensor integration. Recent developments highlight a shift towards more data-efficient and flexible sensing solutions, leveraging techniques such as Electrical Impedance Tomography (EIT) and deep learning models to enhance the accuracy and applicability of tactile feedback systems. These advancements are particularly notable in the context of robotic manipulation, where the fusion of tactile and visual data is being explored to improve task performance in complex environments. Additionally, there is a growing emphasis on the affective quality of tactile interactions, with studies focusing on predicting and optimizing user experience through detailed analysis of force profiles. The integration of these technologies into human-machine interfaces is also expanding, with flexible EIT sensors demonstrating potential for dynamic and multi-functional tactile interactions in real-time applications. Overall, the field is moving towards more integrated, adaptable, and user-centric solutions that promise to significantly enhance the capabilities of tactile sensing in various industries.