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Predictive Tree-based Virtual Keyboard for Improved Gaze Typing

Hrushikesh Etikikota, Yogesh Kumar Meena

TL;DR

The Flex-Tree on-screen keyboard, developed using a two-stage tree-based character selection system with ten commands, received high ratings on the system usability scale and low-weighted ratings on the NASA Task Load Index for both input modalities, highlighting its user-centred design.

Abstract

On-screen keyboard eye-typing systems are limited due to the lack of predictive text and user-centred approaches, resulting in low text entry rates and frequent recalibration. This work proposes integrating the prediction by partial matching (PPM) technique into a tree-based virtual keyboard. We developed the Flex-Tree on-screen keyboard using a two-stage tree-based character selection system with ten commands, testing it with three degree of PPM (PPM1, PPM2, PPM3). Flex-Tree provides access to 72 English characters, including upper- and lower-case letters, numbers, and special characters, and offers functionalities like the delete command for corrections. The system was evaluated with sixteen healthy volunteers using two specially designed typing tasks, including the hand-picked and random-picked sentences. The spelling task was performed using two input modalities: (i) a mouse and (ii) a portable eye-tracker. Two experiments were conducted, encompassing 24 different conditions. The typing performance of Flex-Tree was compared with that of a tree-based virtual keyboard with an alphabetic arrangement (NoPPM) and the Dasher on-screen keyboard for new users. Flex-Tree with PPM3 outperformed the other keyboards, achieving average text entry speeds of 27.7 letters/min with a mouse and 16.3 letters/min with an eye-tracker. Using the eye-tracker, the information transfer rates at the command and letter levels were 108.4 bits/min and 100.7 bits/min, respectively. Flex-Tree, across all three degree of PPM, received high ratings on the system usability scale and low-weighted ratings on the NASA Task Load Index for both input modalities, highlighting its user-centred design.

Predictive Tree-based Virtual Keyboard for Improved Gaze Typing

TL;DR

The Flex-Tree on-screen keyboard, developed using a two-stage tree-based character selection system with ten commands, received high ratings on the system usability scale and low-weighted ratings on the NASA Task Load Index for both input modalities, highlighting its user-centred design.

Abstract

On-screen keyboard eye-typing systems are limited due to the lack of predictive text and user-centred approaches, resulting in low text entry rates and frequent recalibration. This work proposes integrating the prediction by partial matching (PPM) technique into a tree-based virtual keyboard. We developed the Flex-Tree on-screen keyboard using a two-stage tree-based character selection system with ten commands, testing it with three degree of PPM (PPM1, PPM2, PPM3). Flex-Tree provides access to 72 English characters, including upper- and lower-case letters, numbers, and special characters, and offers functionalities like the delete command for corrections. The system was evaluated with sixteen healthy volunteers using two specially designed typing tasks, including the hand-picked and random-picked sentences. The spelling task was performed using two input modalities: (i) a mouse and (ii) a portable eye-tracker. Two experiments were conducted, encompassing 24 different conditions. The typing performance of Flex-Tree was compared with that of a tree-based virtual keyboard with an alphabetic arrangement (NoPPM) and the Dasher on-screen keyboard for new users. Flex-Tree with PPM3 outperformed the other keyboards, achieving average text entry speeds of 27.7 letters/min with a mouse and 16.3 letters/min with an eye-tracker. Using the eye-tracker, the information transfer rates at the command and letter levels were 108.4 bits/min and 100.7 bits/min, respectively. Flex-Tree, across all three degree of PPM, received high ratings on the system usability scale and low-weighted ratings on the NASA Task Load Index for both input modalities, highlighting its user-centred design.

Paper Structure

This paper contains 18 sections, 2 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Proposed prediction by partial matching for tree-based interfaces
  3. System Overview
  4. Flex-Tree on-screen keyboard
  5. Dasher on-screen keyboard
  6. Experimental Protocol
  7. Participants
  8. Design and operational procedure
  9. Experiment 1
  10. Experiment 2
  11. Performance evaluation
  12. Results
  13. Experiment 1
  14. Experiment 2
  15. Subjective Evaluation
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Left: Workflow diagram of Flex-Tree on-screen keyboard with proposed PPM method. Middle: GUI of the proposed Flex-Tree with PPM2 in level one after typing "and t” is shown in the output text display. Right: GUI of the reproduced Dasher on-screen keyboard includes the input text display for typing spelling task ward2000dashertuisku2008now.
  • Figure 2: Subjective evaluation outcomes: the average SUS score with a mouse (a) and an eye-tracker (b), the average NASA-TLX score with a mouse (c) and an eye-tracker (d).