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Predicting Communication Rates: Efficacy of a Scanning Model

Mankowski, Robert E. (2009) Predicting Communication Rates: Efficacy of a Scanning Model. Master's Thesis, University of Pittsburgh. (Unpublished)

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Interaction with the surrounding environment is an essential element of ever day life. For individuals' with severe motor and communicative disabilities, single switch scanning is used as method to control their environment and communicate. Despite being very slow, it is often the only option for individuals who cannot use other interfaces. The alteration of timing parameters and scanning system configurations impacts the communication rate of those using single switch scanning. The ability to select and recommend an efficient configuration for an individual with a disability is essential. Predictive models could assist in the goal of achieving the best possible match between user and assistive technology device, but consideration of an individual's single switch scanning tendencies has not been included in communication rate prediction models. Modeling software developed as part of this research study utilizes scan settings, switch settings, error tendencies, error correction strategies, and the matrix configuration to calculate and predict a communication rate. Five participants with disabilities who use single switch scanning were recruited for this study. Participants were asked to transcribe sentences using an on-screen keyboard configured with settings used on their own communication devices. The participant's error types, frequencies, and correction methods were acquired as well as their text entry rate (TER) during sentence transcription. These individual tendencies and system configuration were used as baseline input parameters to a scanning model application that calculated a TER based upon those parameters. The scanning model was used with the participant's tendencies and at least three varied system configurations. Participants were asked to transcribe sentences with these three configurations The predicted TERs of the model were compared to the actual TERs observed during sentence transcription for accuracy. Results showed that prediction were 90% accurate on average. Model TER predictions were less than one character per minute different from observed baseline TER for each participant. Average model predictions for configuration scenarios were less than one character per minute different from observed configuration TER.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Mankowski, Robert E.rem47@pitt.eduREM47
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSimpson, Richard Cris20@pitt.eduRIS20
Committee MemberLoPresti, Edmund
Committee MemberColtellaro, John
Date: 10 September 2009
Date Type: Completion
Defense Date: 29 July 2009
Approval Date: 10 September 2009
Submission Date: 26 July 2009
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: School of Health and Rehabilitation Sciences > Health and Rehabilitation Sciences
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: assistive technology; augmentative and alternative communication; single-switch scanning
Other ID:, etd-07262009-195906
Date Deposited: 10 Nov 2011 19:54
Last Modified: 15 Nov 2016 13:46


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