The design and modelling of spelling paradigms for assistive devices using binary control signals

Abstract

Patients suffering from diseases affecting verbal communication can make use of assistive devices to improve communication. Some severely disabled patients can only produce yes-or-no responses to communicate. These responses can be created through a physical switch, eye blink, or a 'mental' click created by brain activity. The yes-or-no responses can be used to communicate by making multiple selections between two groups of letters. Through deduction paradigms the target letter can be determined. Current assistive devices use a Row Column or Huffman paradigm to communicate. Communication rates achieved with these paradigms are slow compared to regular conversation rates and to assistive devices using eye-tracking. Furthermore, these deduction paradigms have only been tested in assistive devices with no or little noise. Noise affects the yes-or-no responses and leads to the selection of incorrect letters. There are assistive devices that suffer from high noise levels which affect communication rates. This work evaluates four communication paradigms for a range of noise conditions to improve communication rates. Row Column, Huffman, and two novel paradigms are evaluated. The two novel paradigms, based on Variable-Length Error-Correcting code and Weighted Huffman encoding, are designed for environments with noise. Evaluation of these paradigms is done through simulation and human experiments. A mathematical model is developed for simulation, and an emulator emulating an assistive device is used for human experiments. Spelling speed and cognitive effort are used as performance measures. The simulations were shown to be useful as a tool for predicting the relative performance of the paradigms in real use situations. Results from the simulation and experiments found that the effect of noise is paradigm dependent and should be taken into consideration. A novel paradigm was shown to be optimal for selective noise conditions. Row Column scanning scored the best on cognitive effort, while Huffman encoding resulted in the fastest typing speed in almost all noise conditions. Through emulation and simulation, Huffman encoding is validated as the optimal paradigm to increase communication rates. The mathematical model set a basis on which more research into optimal communication paradigms for discrete control assistive devices can be conducted.