Prototyping a car seat with variable-stiffness soft robotic modules

Abstract

This report concerned the development of a soft robotic module for a seat pan, its optimization and application. The soft robotic module has an LED and photosensors for determining the distance of indentation and the passive force created by compressing the foam spring inside. An air pressure sensor is used to determine the active force created when an air pump inflates the bellow with air. The system is trained by machine learning to calculate predictions of these distance and forces in real-time. In several iterations of the module the reproducibility and accuracy were developed in such a way that it could be built into a seat. Two modules are built into a seat pan and interestingly participants on the seat were able to experience significant comfort differences, showing that the principle works. Further development is needed to make a seat pan with more modules, combined with a central computing system that monitors, records and regulates the modules. Exploration of simplification by using a linear regression instead of a neural network to calculate the active force is recommended, as well exploration of improved functionality by dividing the neural network that calculates the distance of indentation.