Deep Learning Super-Resolution Network Facilitating Fiducial Tangibles on Capacitive Touchscreens

Rusu, Marius; Mayer, Sven

doi:10.1145/3544548.3580987

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems 2023

DOI: 10.1145/3544548.3580987

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Deep Learning Super-Resolution Network Facilitating Fiducial Tangibles on Capacitive Touchscreens

Marius Rusu

Sven Mayer²

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2024

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Cited by 1 publication

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Can Capacitive Touch Images Enhance Mobile Keyboard Decoding?

Lertvittayakumjorn,

Cai,

Dou

et al. 2024

Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology

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Capacitive touch sensors capture the two-dimensional spatial profile (referred to as a touch heatmap) of a finger's contact with a mobile touchscreen. However, the research and design of touchscreen mobile keyboards -one of the most speed and accuracy demanding touch interfaces -has focused on the location of the touch centroid derived from the touch image heatmap as the input, discarding the rest of the raw spatial signals. In this paper, we investigate whether touch heatmaps can be leveraged to further improve the tap decoding accuracy for mobile touchscreen keyboards. Specifically, we developed and evaluated machine-learning models that interpret user taps by using the centroids and/or the heatmaps as their input and studied the contribution of the heatmaps to model performance. The results show that adding the heatmap into the input feature set led to 21.4% relative reduction of character error rates on average, compared to using the centroid alone. Furthermore, we conducted a live user study with the centroid-based and heatmapbased decoders built into Pixel 6 Pro devices and observed lower error rate, faster typing speed, and higher self-reported satisfaction score based on the heatmap-based decoder than the centroid-based decoder. These findings underline the promise of utilizing touch heatmaps for improving typing experience in mobile keyboards. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing; • Computing methodologies → Machine learning.

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Can Capacitive Touch Images Enhance Mobile Keyboard Decoding?

Lertvittayakumjorn,

Cai,

Dou

et al. 2024

Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology

View full text Add to dashboard Cite

show abstract

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Deep Learning Super-Resolution Network Facilitating Fiducial Tangibles on Capacitive Touchscreens

Cited by 1 publication

References 97 publications

Can Capacitive Touch Images Enhance Mobile Keyboard Decoding?

Can Capacitive Touch Images Enhance Mobile Keyboard Decoding?

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