A Neural Network-based Low-cost Soft Sensor for Touch Recognition and Deformation Capture

Abstract

We propose a novel, cost-effective soft sensor capable of detecting contact force, multiple touch points, and reflecting sensor interaction in real-time with a 3D virtual surface representation. Our fabrication process has been optimized for cost efficiency through careful material selection, utilization of automated machinery, and low-cost hardware. The sensor can be easily replicated without the need for complex laboratory equipment. The sensor employs trained neural network models for real-time signal translation into localization, force measurement, and deformation mapping. We have also developed an efficient data collection system that captures accurate 2D localization, force measurement, and 3D surface data to generate a high-quality pre-validated data set. This data set is filtered using prior knowledge before being fed to two neural network models. Our interactive prototype demonstrates the stability and accuracy of the low-cost soft sensor, delivering reliable results in both single-point and multi-point contact scenarios.