# Text Typing Using Blink-to-Alphabet Tree for Patients with Neuro-Locomotor Disabilities

## TL;DR

This paper introduces a blink-based text typing method for ALS patients, using a tree structure to select letters and an AI to improve typing speed and accuracy.

## Contribution

A novel blink-to-alphabet tree method with integrated large language model for efficient text input without calibration.

## Key findings

- The method outperforms existing techniques in typing speed and accuracy.
- Participants typed letters using 3-9 blinks without calibration.
- Integration of LLM improved sentence correction and input efficiency.

## Abstract

Lou Gehrig’s disease, also known as ALS, is a progressive neurodegenerative condition that weakens muscles and can lead to paralysis as it progresses. For patients with severe paralysis, eye-tracking devices such as eye mouse enable communication. However, the equipment is expensive, and the calibration process is very difficult and frustrating for patients to use. To alleviate this problem, we propose a simple and efficient method to type texts intuitively with graphical guidance on the screen. Specifically, the method detects patients’ eye blinks in video frames to navigate through three sequential steps, narrowing down the choices from 9 letters, to 3 letters, and finally to a single letter (from a 26-letter alphabet). In this way, a patient is able to rapidly type a letter of the alphabet by blinking a minimum of three times and a maximum of nine times. The proposed method integrates an API of large language model (LLM) to further accelerate text input and correct sentences in terms of typographical errors, spacing, and upper/lower case. Experiments on ten participants demonstrate that the proposed method significantly outperforms three state-of-the-art methods in both typing speed and typing accuracy, without requiring any calibration process.