Piezoelectric and Machine Learning-Based Technique for Classifying Force Levels and Locations of Multiple Force Touch Events

Current commercial force touch panels can merely detect a single force touch's location and amplitude. However, in many applications, multiple force touch events can occur at the same time among different locations of the touch panel. To satisfy this need, in this article, a piezoelectric and machine learning-based technique is proposed. Here, the piezoelectric film-based touch panel is used to detect different force levels, while the machine learning algorithm is developed to interpret the locations and strengths of user applied multiple force touch events. High detection accuracy of 92.3% for location determination and 88.2% for force level recognition is achieved.