Static Force Touch Detection Based on Artificial Neural Networks

The introduction of force touch detection improves the efficiency of human-machine interactivities. In previous studies, characteristics of dynamic force touch events and corresponding classification methods have been developed. However, static force touch is still untouched due to two challenges: user-induced pulses and hysteresis. In this article, an artificial neural network (ANN) is designed and optimized for addressing these two undesired phenomena. Experimental results demonstrate after attenuating these two issues, an average detection error of 0.15 N is successfully achieved.