keyValuate: A Framework for Rapid Evaluation of Adaptive Keyboards on Touchscreens

We propose a general-purpose framework for the implementation and evaluation of adaptive virtual keyboards based on unprocessed sensory information from an interactive surface. We furthermore describe an implementation on a commercially available optical touchscreen that features real-time visualization of the underlying key classification process. The typing application, which uses support vector machine classifiers and bivariate Gaussian distributions to differentiate between keys, was evaluated in a user study with 24 participants. The adaptive keyboard performed significantly better in terms of typing speed and error rates compared to a standard onscreen keyboard (approximately 40% speedup and 25% reduced error rates). We also performed evaluations with reduced sensor resolutions and additive noise in order to verify the generalizability of the presented approach for other sensing techniques. Our approach showed high robustness in both conditions. Based on these findings, we discuss possible implications for future implementations of virtual keyboards.