60‐3: Invited Paper: Machine Learning Approaches to Active Stylus for Capacitive Touch Screen Panel Applications

Abstract

This paper introduces machine learning approaches on adding the stylus‐touch to the capacitive touch screen technology. The proposed schemes can discriminate the stylus‐touch from finger‐touch as well as no‐touch by means of classification algorithms using support vector machine and anomaly detection. The high frequency pulses are sent from a stylus to a touch screen and the receiver classifies the received sample sequences into three classes of no‐touch, finger‐touch, and stylus‐touch. In addition, some possible applications of data transmission and user authentication are demonstrated.