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.

High detection accuracy in piezoelectric-based force sensing in interactive displays has gained global attention. To achieve this, artificial neural networks (ANN)—successful and widely used machine learning algorithms—have been demonstrated to be potentially powerful tools, providing acceptable location detection accuracy of 95.2% and force level recognition of 93.3% in a previous study. While these values might be acceptable for conventional operations, e.g., opening a folder, they must be boosted for applications where intensive operations are performed. Furthermore, the relatively high computational cost reported prevents the popularity of ANN-based techniques in conventional artificial intelligence (AI) chip-free end-terminals. In this article, an ANN is designed and optimized for piezoelectric-based touch panels in interactive displays for the first time. The presented technique experimentally allows a conventional smart device to work smoothly with a high detection accuracy of above 97% for both location and force level detection with a low computational cost, thereby advancing the user experience, and serviced by piezoelectric-based touch interfaces in displays.

Piezoelectric force touch panels receive increased attentions in recent years. However, user-induced nonstable force-voltage responsivity limits their successful use in interactive displays. In this work, touch-induced capacitive information is used for estimating contact area and touch angle, which are further employed to interpret user performed force amplitude. A promising result of improving the stability of force-voltage responsivity by 85% is achieved, enhancing user experience and advancing the development of piezoelectric force sensing in interactive displays.

This paper evaluates the impact of the operating modes, power consumption, and placement of temperature sensors against the heater to the design of a calorimetric flow sensor, for the range of 4.7 to 56.5 liters per minute (slm). In contrast to previous works most of which simply indicated the choice of various design parameters rather than providing a justification, this work provides useful guidelines for optimizing low-power small-area flow sensors for respiratory monitoring applications. A figure of merit (FoM) which is defined as the product of power consumption and sensor size, the two most challenging design parameters in developing small medical devices and systems, is proposed for quantifying flow sensor performance. Although the analysis and simulation was drawn upon designs in the mm scale, a similar optimization process can be applied to flow sensors of anysize.

This work compares the resolution of calorimetric flow sensors in constant powere mode using symmetrically located sensing elements to those using asymmetrical locations, for flow rates higher than the turn-over point. The experimental results show that the resolution can be improved up to six times by placing the temperature sensors in unequal distances, for the same power consumption.