User-Oriented Piezoelectric Force Sensing and Artificial Neural Networks in Interactive Displays

Force touch based interactivity has been widely integrated into displays equipped in most of smart electronic systems such as smartphones and tablets. This paper reports on application of artificial neural networks to analyze data generated from piezoelectric based touch panels for providing customized force sensing operation. Based on the experimental results, high force sensing accuracy (93.3%) is achieved when three force levels are used. Two-dimensional sensing, also achieved with the proposed technique, with high detection accuracy (95.2%). The technique presented here not only achieves high accuracy, but also allows users to define the range of force levels through behavioral means thus enhancing interactivity experience.

[1]  J. Robertson,et al.  Ultrathin Multifunctional Graphene-PVDF Layers for Multidimensional Touch Interactivity for Flexible Displays. , 2017, ACS applied materials & interfaces.

[2]  M. Humayun Kabir,et al.  Machine Learning Based Adaptive Context-Aware System for Smart Home Environment , 2015 .

[3]  Gareth James,et al.  Variance and Bias for General Loss Functions , 2003, Machine Learning.

[4]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[5]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[6]  Maureen Schmitter-Edgecombe,et al.  Automated Cognitive Health Assessment From Smart Home-Based Behavior Data , 2016, IEEE Journal of Biomedical and Health Informatics.

[7]  Andrew L. Maas.  Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .

[8]  Arokia Nathan,et al.  P‐209: Augmenting Capacitive Touch with Piezoelectric Force Sensing , 2017 .

[9]  Kurth Reynolds,et al.  46‐1: Invited Paper: Touch and Display Integration with Force , 2016 .

[10]  Khattab M. Ali Alheeti,et al.  Hybrid intrusion detection in connected self-driving vehicles , 2016, 2016 22nd International Conference on Automation and Computing (ICAC).

[11]  Gary Kamen,et al.  Adaptations in motor unit discharge activity with force control training in young and older human adults , 2000, European Journal of Applied Physiology.

[12]  Václav Snásel,et al.  Metaheuristic design of feedforward neural networks: A review of two decades of research , 2017, Eng. Appl. Artif. Intell..

[13]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[14]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[15]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[16]  Jong-Hyun Ahn,et al.  Graphene-P(VDF-TrFE) multilayer film for flexible applications. , 2013, ACS nano.

[17]  Lalu Banoth,et al.  A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection , 2017 .

[18]  Erhan Guven,et al.  A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection , 2016, IEEE Communications Surveys & Tutorials.

[19]  Gregory M. P. O'Hare,et al.  Dynamic sensor event segmentation for real-time activity recognition in a smart home context , 2014, Personal and Ubiquitous Computing.

[20]  Jan Peters,et al.  Reinforcement learning in robotics: A survey , 2013, Int. J. Robotics Res..

[21]  Shuo Gao,et al.  Interactive Displays: The Next Omnipresent Technology [Point of View] , 2016, Proc. IEEE.

[22]  Geoff Walker,et al.  A review of technologies for sensing contact location on the surface of a display , 2012 .

[23]  Zhibin Zhang,et al.  Flexible piezoelectric nanogenerator made of poly(vinylidenefluoride-co-trifluoroethylene) (PVDF-TrFE) thin film , 2014 .

[24]  Lynette A. Jones,et al.  The Control and Perception of Finger Forces , 2014, The Human Hand as an Inspiration for Robot Hand Development.

[25]  Mari Zakrzewski,et al.  Printable, Transparent, and Flexible Touch Panels Working in Sunlight and Moist Environments , 2014 .

[26]  Tom M. Mitchell,et al.  Weakly Supervised Extraction of Computer Security Events from Twitter , 2015, WWW.

[27]  Luca Maiolo,et al.  A Comparison Among Low Temperature Piezoelectric Flexible Sensors Based on Polysilicon TFTs for Advanced Tactile Sensing on Plastic , 2016, Journal of Display Technology.

[28]  S Gao,et al.  Why Piezoelectric Based Force Sensing is not Successful in Interactive Displays , 2018 .

[29]  Shuo Gao,et al.  Piezoelectric vs. Capacitive Based Force Sensing in Capacitive Touch Panels , 2016, IEEE Access.

[30]  Byoung Hun Lee,et al.  Characteristics of a pressure sensitive touch sensor using a piezoelectric PVDF-TrFE/MoS2 stack , 2013, Nanotechnology.

[31]  Byron Boots,et al.  Agile Off-Road Autonomous Driving Using End-to-End Deep Imitation Learning , 2017, ArXiv.

[32]  Terrence J. Sejnowski,et al.  Unsupervised Learning , 2018, Encyclopedia of GIS.

[33]  Charles T. Leonard,et al.  The effect of intervening forces on finger force perception , 2008, Neuroscience Letters.

[34]  B. Lee,et al.  Sensitivity improvement of graphene/Al2O3/PVDF-TrFE stacked touch device through Al seed assisted dielectric scaling , 2015 .