Towards online adaptation and personalization of key-target resizing for mobile devices

Abstract

Software (soft) keyboards are becoming increasingly popular on mobile devices. To attempt to improve soft keyboard input accuracy, key-target resizing algorithms that dynamically change the size of each key's target area have been developed. Although methods that employ personalized touch models have been shown to outperform general models, previous work has relied upon laboratory-based offline calibration to collect the data necessary to build these models. Such approaches are unrealistic and interuptive, and it is unlikely that offline calibration can be applied in a realistic usage setting, as hundreds or thousands of touch points are necessary to build the models. To combat this problem, this paper explores the possibility of online adaptation of key-target resizing algorithms. In particular, we propose and examine three online data collection methods that can be used to build and dynamically update personalized key-target resizing models. Our results suggest that a data collection methodology that makes inference based on vocabulary and error correction behavior is able to perform on par with gold standard personalized models, while reducing relative error rate by 10.4% over general models. This approach is simple, computationally inexpensive, and calculable via information that the system already has access to. Additionally, we show that these models can be built quickly, requiring less than one week's worth of text input by an average mobile device user.