Improving Pressure Prediction Accuracy for Capacitive Sensors Using Machine Learning Techniques

Abstract

Capacitive sensors are used in a wide range of industries, including wearable technology, robotics, industrial automation, and healthcare monitoring. Capacitive pressure gauges are a good example, but environmental factors, such as variations in temperature can significantly affect their pressure prediction performance. However, the accuracy of pressure prediction is often affected by ambient variables such as humidity and temperature, and the nonlinear characteristics of the sensor. To solve these problems, this research paper proposes a machine learning-based calibration method for capacitive sensors which would further improve the accuracy of pressure prediction. Special attention was paid to the Support Vector Regression (SVR) strategy, which was deemed the best, primarily due to its ability to accurately simulate sparse data for nonlinear functions requirement for embedded systems. Accordingly, in experimental tests, the hyper parameters of the Radial Basis Function (RBF) core used to construct the RBF model, were optimized to achieve the best results. The proposed SVR model achieved 98% prediction accuracy in experimental results, a significant improvement over traditional linear regression. This approach provides a scalable method for future smart sensors bridging the limitations of field deployment and enabling high-resolution modeling of industrially useful instruments.