PalmBoard: Leveraging Implicit Touch Pressure in Statistical Decoding for Indirect Text Entry

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We investigated how to incorporate implicit touch pressure, finger pressure applied to a touch surface during typing, to improve text entry performance via statistical decoding. We focused on one-handed touch-typing on indirect interface as an example scenario. We first collected typing data on a pressure-sensitive touchpad, and analyzed users' typing behavior such as touch point distribution, key-to-finger mappings, and pressure images. Our investigation revealed distinct pressure patterns for different keys. Based on the findings, we performed a series of simulations to iteratively optimize the statistical decoding algorithm. Our investigation led to a Markov-Bayesian decoder incorporating pressure image data into decoding. It improved the top-1 accuracy from 53% to 74% over a naive Bayesian decoder. We then implemented PalmBoard, a text entry method that implemented the Markov-Bayesian decoder and effectively supported one-handed touch-typing on indirect interfaces. A user study showed participants achieved an average speed of 32.8 WPM with 0.6% error rate. Expert typists could achieve 40.2 WPM with 30 minutes of practice. Overall, our investigation showed that incorporating implicit touch pressure is effective in improving text entry decoding.