User-Adaptive Text Entry for Augmentative and Alternative Communication

TL;DR

This paper introduces a multi-character querying method for adaptive text entry in communication devices, significantly improving efficiency by reducing queries while maintaining accuracy and approaching theoretical capacity.

Contribution

It extends previous single-character adaptive schemes to multi-character querying, enhancing performance and convergence to information theoretic limits.

Findings

20% fewer queries needed in simulated spelling

Method converges to information theoretic capacity

No accuracy penalty with multi-character querying

Abstract

The viability of an Augmentative and Alternative Communication device often depends on its ability to adapt to an individual user's unique abilities. Though human input can be noisy, there is often structure to our errors. For example, keyboard keys adjacent to a target may be more likely to be pressed in error. Furthermore, there can be structure in the input message itself (e.g. `u' is likely to follow `q'). In a previous work, `Recursive Bayesian Coding for BCIs' (IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016), a query strategy considers these structures to offer an error-adaptive single-character text entry scheme. However, constraining ourselves to single-character entry limits performance. A single user input may be able to resolve more uncertainty than the next character has. In this work, we extend the previous framework to incorporate multi-character querying similar to word completion. During simulated spelling, our method requires $20\%$ fewer queries compared to single-character querying with no accuracy penalty. Most significantly, we show that this multi-character querying scheme converges to the information theoretic capacity of the discrete, memoryless user input model.