Effect of the Impact Contact Duration on Machine Learning Models for Impact Position Detection

Touch-screens have become the most common way of interaction between humans and machines. Nevertheless, the lack of rich haptic-feedback on most of the devices limits the quality and effectivity of the interaction. To develop novel haptic feedback generation strategies, the authors of this work, are initially experimenting with machine learning algorithms to detect the position of a finger over a tactile surface. This paper presents the experimental study to determine the effect of the impact contact duration on the different machine learning (ML) and neural network (NN) models that were previously proposed for impact position detection. A new version of a Linear Impact Generator (LIG) is presented and an experimental study, with a high-speed camera, is carried out to characterize the LIG. Additionally, two different pre-processing methods are compared (i.e. Magnitude Spectrogram representation and FFT frequency-domain representation), showing that the FFT representation contains richer information to describe the impact position. The best model achieved an error (Validation MAE) of 0.18 % or 0.31 mm and (Test MAE) of 1.16 % or 2 mm. Lastly, it was demonstrated that the impact contact duration has a direct effect on the precision of the impact position prediction. When the contact duration changes, the error increases to (Test MAE) 27.12 % or 48 mm on average.