Fewer typing errors when touching a smartphone keyboard: ETH Computer scientists have developed a new AI solution that enables touchscreens to sense with eight times higher resolution than current devices. Thanks to AI, their solution can infer much more precisely where fingers touch the screen.
We are probably all familiar with this: if you want to quickly type a message on your smartphone, you sometimes hit the wrong letters on the small keyboard – or on other input buttons in an app. But it's not just us. The touch sensors, which detect finger input on the touch screen, have not changed much since they were first released in mobile phones in the mid-2000s.
In contrast, the screens of smartphones and tablets are now providing unprecedented visual quality, which is even more evident with each new generation of devices: higher colour fidelity, higher resolution, crisper contrast. A latest-generation iPhone, for example, has a display resolution of 2532x1170 pixels. But the touch sensor it integrates can only detect input with a resolution of around 32x15 pixels – that's almost 80 times lower than the display resolution: "And here we are, wondering why we make so many typing errors on the small keyboard? We think that we should be able to select objects with pixel accuracy through touch, but that’s certainly not the case," says Christian Holz, ETH computer science professor from the Sensing, Interaction & Perception Lab (SIPLAB) in an interview in the ETH Computer Science Department's "Spotlights" series.
Together with his doctoral student Paul Streli, Holz has now developed an artificial intelligence (AI) called "CapContact" that gives touch screens super-resolution so that they can reliably detect when and where fingers actually touch the display surface, with much higher accuracy than current devices do. This week they presented their new AI solution at ACM CHI 2021, the premier conference in on Human Factors in Computing Systems.
Recognising where fingers touch the screen
The ETH researchers developed the AI for capacitive touch screens, which are the types of touch screens used in all our mobile phones, tablets, and laptops. The sensor detects the position of the fingers by the fact that the electrical field between the sensor lines changes due to the proximity of a finger when touching the screen surface. Because capacitive sensing inherently captures this proximity, it cannot actually detect true contact – which is sufficient for interaction, however, because the intensity of a measured touch decreases exponentially with the growing distance of the fingers.
Capacitance sensing was never designed to infer with pinpoint accuracy where a touch actually occurs on the screen, says Holz: "It only detects the proximity of our fingers." The touch screens of today's devices thus interpolate the location where the input is made with the finger from coarse proximity measurements. In their project, the researchers aimed to address both shortcomings of these ubiquitous sensors: On the one hand, they had to increase the currently low resolution of the sensors and, on the other hand, they had to find out how to precisely infer the respective contact area between finger and display surface from the capacitive measurements.
Consequently, "CapContact", the novel method Streli and Holz developed for this purpose, combines two approaches: On the one hand, they use the touch screens as image sensors. According to Holz, a touch screen is essentially a very low-resolution depth camera that can see about eight millimetres far. A depth camera does not capture coloured images, but records an image of how close objects are. On the other hand, "CapContact" exploits this insight to accurately detect the contact areas between fingers and surfaces through a novel deep learning algorithm that the researchers developed.
"First, 'CapContact' estimates the actual contact areas between fingers and touchscreens upon touch," says Holz, "Second, it generates these contact areas at eight times the resolution of current touch sensors, enabling our touch devices to detect touch much more precisely."
To train the AI, the researchers constructed a custom apparatus that records the capacitive intensities, i.e., the types of measurements our phones and tablets record, and the true contact maps through an optical high-resolution pressure sensor. By capturing a multitude of touches from several test participants, the researchers captured a training dataset, from which "CapContact" learned to predict super-resolution contact areas from the coarse and low-resolution sensor data of today's touch devices.
This article was first published on May 12 by ETH Zurich.