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An R-Based Landscape Validation of a Competing Risk Model

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Validating a model of row-column scanning.

Robert Mankowski¹, Richard C Simpson, Heidi H Koester

¹Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, PA, USA.

Disability and Rehabilitation. Assistive Technology

|October 20, 2012

Summary

This summary is machine-generated.

A new model accurately predicts communication rates for single switch scanning users by considering scanning settings and error tendencies. This model is significantly more accurate than previous methods that ignored potential user errors.

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Area of Science:

Augmentative and Alternative Communication (AAC)
Human-Computer Interaction
Assistive Technology Research

Background:

Single switch scanning is a crucial method for individuals with severe motor and communicative disabilities to access computers and communicate.
Existing models for predicting communication rates often overlook user-specific error tendencies and correction strategies, limiting their accuracy.

Purpose of the Study:

To develop and validate a predictive model for user communication rate in single switch scanning.
The model incorporates scanning interface settings, error tendencies, error correction strategies, and matrix configuration.

Main Methods:

Five participants with severe disabilities used single switch scanning to transcribe sentences.
Data were collected using various on-screen keyboard configurations.
A predictive model estimated Transcribable Error Rate (TER), which was compared against actual TER.

Main Results:

The developed model achieved an average prediction accuracy of 90%.
The model identified an average error rate of 10.49% per participant.
This contrasts sharply with a previously published error-free model, which showed an average error of 79.7%.

Conclusions:

The developed row-column scanning model significantly outperforms models that do not account for errors.
The findings highlight the importance of incorporating error likelihood into communication rate predictions.
Further refinements to the model are planned based on these results.