Ensemble Learning-Based Technique for Force Classifications in Piezoelectric Touch Panels

Human-machine interfaces (HMI) based on piezoelectric sensors have been receiving increasing attention due to their high force detection accuracy and low energy consumption, along with the ability to deploy user-oriented force sensing capability enabled by artificial intelligence. However, reports to date use a large amount of data to construct a customized model, which reduces user experience. To address this issue, an ensemble learning-based technique is presented in this paper, for building a customized classification model with a much lower data set. Experimental results demonstrate high force detection accuracy (98.32%) at low computational cost and at data collection times of less than a minute. This implies much fewer user operations yet enhancing the user experiences of HMI.