One-touch calibration of hum-noise-based touch sensor for unknown users utilizing models trained by different users

Abstract

Hum-noise-based touch sensors (HumTouch) are capable of recognizing human touch on semiconductive materials using the current leaking from the finger to the surface. Thus far, calibration for these hum-noise-based touch sensors has been performed for individual users because of the individual differences in hum-driven electric currents in human bodies. However, for applications designed for unknown users, time-consuming calibration for individual users is not preferred, and a new user should be able to use the sensor immediately. For this purpose, we propose a new calibration method for HumTouch. In this method, learning datasets collected from multiple people and a few extra samples from a new user are collectively used to establish a touch localization estimator. The estimator is computed using the kernel regression method with weighted samples from the new user. For a 20 $$\times $$ × 18 cm$$^2$$ 2 paper, the mean localization error is reduced from 1.24 cm to 0.90 cm with only one sample from the new user. Hence, a new user can establish a semipersonalized localization estimator by touching only one point on the surface. This method improves the localization performance of HumTouch sensors in an easy-to-access manner.