Fast Switch Scanning Keyboards: Minimal Expected Query Decision Trees
Matt Higger, Mohammad Moghadamfalahi, Fernando Quivira, Deniz, Erdogmus

TL;DR
This paper introduces a method to optimize switch scanning decision trees in AAC systems by minimizing expected query costs, leading to faster character selection especially in timed single-switch scenarios.
Contribution
It formulates the decision tree optimization as a Huffman coding problem with variable costs and extends existing algorithms to produce minimal expected query decision trees for AAC.
Findings
Minimizing EQPD reduces selection times in switch scanning.
The proposed method outperforms traditional tree structures in timed scenarios.
Modeling user detection probabilities improves decision tree efficiency.
Abstract
Augmentative and Alternative Communication (AAC) systems allow people with disabilities to provide input to devices which empower them to more fully interact with their environment. Within AAC, switch scanning is a common paradigm for spelling where a set of characters is highlighted and the user is queried as to whether their target character is in the highlighted set. These queries are used to traverse a decision tree which successively prunes away characters until only a single one remains (the estimate). This work seeks a decision tree which requires the fewest expected queries per decision sequence (EQPD). In particular, we remove the constraint that the decision tree needs to be a row-item or group-row-item style tree and minimize EQPD. We pose the problem as a Huffman code with variable, integer cost and solve it with a mild extension of Golin's method in "A dynamic programming…
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Taxonomy
TopicsAlgorithms and Data Compression · Advanced Image and Video Retrieval Techniques · DNA and Biological Computing
