Making touchscreen keyboards adaptive to keys, hand postures, and individuals

We propose a new approach for improving text entry accuracy on touchscreen keyboards by adapting the underlying spatial model to factors such as input hand postures, individuals, and target key positions. To combine these factors together, we introduce a hierarchical spatial backoff model (SBM) that consists of submodels with different levels of complexity. The most general model includes no adaptive factors, whereas the most specific model includes all three. Considering that in practice people may switch hand postures (e.g., from two-thumb to one-finger) to better suit a situation, and that the specific submodels may take time to train for each user, a specific submodel should be applied only if its corresponding input posture can be identified with confidence, and if the submodel has enough training data from the user. We introduce the backoff mechanism to fall back to a simpler model if either of these conditions are not met. We implemented a prototype system capable of reducing the language-model-independent error rate by 13.2% using an online posture classifier with 86.4% accuracy. Further improvements in error rate may be possible with even better posture classification.