Katya Hill

Advances in Augmentative and Alternative Communication as Quality-of-Life Technology

Augmentative and alternative communication (AAC) technology is recommended for individuals with significant communication disorders, who may have motor limitations and other challenges that impact quality of life (QOL). The goal of AAC treatment is to optimize communication. This article presents innovations to the primary, secondary, and tertiary AAC components that are considered by rehabilitation clinicians when matching people with technology (MPT). Language considerations are paramount to the MPT process, and innovations are discussed on how features may enhance language performance. AAC technology has made performance and outcome gains contributing to the QOL of people who cannot speak.

Augmentative and Alternative Communication (AAC) Research and Development: The Challenge of Evidence-based Practice

Augmentative and Alternative Communication (AAC) computer-based systems are used by individuals with severe disabilities who cannot speak. AAC technology is designed with the goal to optimize communication and improve quality of life. This paper presents principles of design based on evidence-based practice (EBP) and language activity monitoring (LAM). When applied to the research and development of AAC computer-based technology, these principles provide quantitative data for making decisions about product performance and achievable outcomes focusing on the benefit to the end-user. The evaluation of a Mandarin language software application provides an example of how the steps of EBP and LAM data were applied during the initial research tasks. The EBP model offers a unique innovation to product development by challenging computer processing designers and programmers to design systematic and scientific data collection and analysis procedures specific to EBP.

Critical Issues Using Brain-Computer Interfaces for Augmentative and Alternative Communication

Brain-computer interfaces (BCIs) may potentially be of significant practical value to patients in advanced stages of amyotrophic lateral sclerosis and locked-in syndrome for whom conventional augmentative and alternative communication (AAC) systems, which require some measure of consistent voluntary muscle control, are not satisfactory options. However, BCIs have primarily been used for communication in laboratory research settings. This article discusses 4 critical issues that should be addressed as BCIs are translated out of laboratory settings to become fully functional BCI/AAC systems that may be implemented clinically. These issues include (1) identification of primary, secondary, and tertiary system features; (2) integrating BCI/AAC systems in the World Health Organizations International Classification of Functioning, Disability and Health framework; (3) implementing language-based assessment and intervention; and (4) performance measurement. A clinical demonstration project is presented as an example of research beginning to address these critical issues.

Reliability of Brain-Computer Interface Language Sample Transcription Procedures

We tested the reliability of transcribing language samples of daily brain-computer interface (BCI) communication recorded as language activity monitoring (LAM) logfiles. This study determined interrater reliability and interjudge agreement for transcription of communication of veterans with amyotrophic lateral sclerosis using a P300-based BCI as an augmentative and alternative communication (AAC) system. KeyLAM software recorded logfiles in a universal logfile format during use of BCI-controlled email and word processing applications. These logfiles were encrypted and sent to our laboratory for decryption, transcription, and analysis. The study reports reliability results on transcription of 345 daily logfile samples. The procedure was found to be accurate across transcribers/raters. Frequency of agreement ratios of 97.6% for total number of words and 93.5% for total utterances were found as measures of interrater reliability. Interjudge agreement was 100% for both measures. The results indicated that transcribing language samples using LAM data is highly reliable and the fidelity of the process can be maintained. LAM data supported the transcription of a large number of samples that could not have been completed using audio and video recordings of AAC speakers. This demonstrated efficiency of LAM tools to measure language performance benefits to BCI research and clinical communities.

Preliminary vocabulary frequency findings for Mandarin Chinese AAC treatments

In this paper, we describe the preliminary results of a study to identify the vocabulary frequency of native Mandarin Chinese speakers during a dyadic conversation. The language samples based on twelve (N=12) participants were analyzed to report the total number of words, total number of different word roots (TND), and number of spoken words used to make up 50%, 60%, 70% and 80% of the sample. Reported are the top 10 most frequently used MC words for each participant. The results will provide a high frequency or core vocabulary for MC to use for augmentative and alternative communication (AAC) interventions.

A Tutorial on Reliability Testing in AAC Language Sample Transcription and Analysis

Abstract Establishing reliability is an essential step in language sample transcription and analysis. This tutorial provides an illustration of replicable procedures for reliability testing during transcription and analysis of language samples generated by people who use augmentative and alternative communication (AAC) systems. Statistical measures used for testing agreement between raters coding categorical data are summarized. Detailed procedures for reliability testing in AAC language sample transcription and analysis are provided, beginning with the collection of raw language sample data. Procedures include guidelines for (a) establishing inter-judge agreement during the transcription process, and (b) using Cohens kappa to establish inter-rater reliability during deeper analysis of transcribed utterances. All procedures are demonstrated in a case example using language samples from children who use AAC.

Translating and validating a Mandarin Chinese version of the computerized revised token test

Abstract The primary objective of this study was to validate the item translation of the Computerized Revised Token Test-Mandarin (CRTT-Mandarin) and to investigate the concurrent validity of this translation in normal control participants (NP) and in persons with aphasia (PWA). The translation of the Computerized Revised Token Test into CRTT-Mandarin was accomplished by forward and backward translation procedures. The concurrent validity was evaluated by comparing the performance of NP and PWA on CRTT-Mandarin and on the Concise Chinese Aphasia Test (CCAT; ); the single standardized Mandarin Chinese aphasia test. Eleven PWA and 19 NP who were native Chinese speakers served as participants. All the participants were administered both aphasia tests. Tests of difference between groups and tests of association were computed between the CRTT-Mandarin overall and between subtest scores and each of the CCAT subtests and overall score. A high degree of agreement was achieved between the forward and backward translations of CRTT and CRTT-Mandarin tests. Statistical analyses revealed significantly lower scores for the PWA than the NP on the overall and subtest scores of CRTT-Mandarin. All correlations were significant. A valid translation of CRTT-Mandarin reliably distinguished comprehension performance between PWA and NP. Concurrent validity is judged to be satisfactory with high positive correlations between CCAT and CRTT-Mandarin.

Development and evaluation of a mobile AAC: a virtual therapist and speech assistant for people with communication disabilities

Abstract Purpose: The currently existing Augmentative and Alternative Communication (AAC) technologies have limitations to produce the best communication rehabilitation outcomes and therefore a better solution is needed. Method: In this work, a mobile AAC app was developed based on results from research studies. Sophisticated AAC language programming, embedded training materials, and real-time communication performance reporting were integrated into the app. Two groups of study participants were recruited to participate a usability study and a preliminary feasibility study for the purpose of evaluating this mobile AAC app, respectively. Results: A tablet-based AAC app was developed to support communication rehabilitation. User studies of the app were conducted and included able-bodied individuals and people with verbal communication disabilities. All study participants agreed that the app establishes a usable alternative treatment protocol for communication rehabilitation. Conclusions: The app’s integrated features have great potential to maximize users’ communication effectiveness, enhance language skills, and ultimately improve users’ quality of life. Implications for rehabilitation We have developed and evaluated an integrated mobile AAC language-based app. This tablet-based app integrated AAC with embedded trainings and real-time performance report.

Effects of text generation on P300 brain-computer interface performance

Brain-computer interfaces (BCIs) are intended to provide independent communication for those with the most severe physical impairments. However, development and testing of BCIs is typically conducted with copy-spelling of provided text, which models only a small portion of a functional communication task. This study was designed to determine how BCI performance is affected by novel text generation. We used a within-subject single-session study design in which subjects used a BCI to perform copy-spelling of provided text and to generate self-composed text to describe a picture. Additional off-line analysis was performed to identify changes in the event-related potentials that the BCI detects and to examine the effects of training the BCI classifier on task-specific data. Accuracy was reduced during the picture description task; (t(8)=2.59 p=0.0321). Creating the classifier using self-generated text data significantly improved accuracy on these data; (t(7)=-2.68, p=0.0317), but did not bring performance up to the level achieved during copy-spelling. Thus, this study shows that the task for which the BCI is used makes a difference in BCI accuracy. Task-specific BCI classifiers are a first step to counteract this effect, but additional study is needed.

An mHealth Platform for Supporting Clinical Data Integration into Augmentative and Alternative Communication Service Delivery: User-Centered Design and Usability Evaluation

Background The recent trend of increasing health care costs in the United States is likely not sustainable. To make health care more economically sustainable, attention must be directed toward improving the quality while simultaneously reducing the cost of health care. One of the recommended approaches to provide better care at a lower cost is to develop high-quality data collection and reporting systems, which support health care professionals in making optimal clinical decisions based on solid, extensive evidence. Objective The objective of this project was to develop an integrated mobile health Augmentative and Alternative Communication (AAC) platform consisting of an AAC mobile app and a Web-based clinician portal for supporting evidence-based clinical service delivery. Methods A questionnaire and interviews were used to collect clinicians’ ideas regarding what constitutes their desired “clinically relevant” data. In response, a Web-based portal was designed by combining mobile and Web technologies with an AAC intervention to create an integrated platform for supporting data collection, integration, and reporting. Finally, a usability study was conducted with health care professionals. Results A Web-based portal was created and integrated with a tablet-based AAC mobile app and data analysis procedures. In the usability study, all participants agreed that the integrated platform provided the ability to collect comprehensive clinical evidence, automatically analyze collected data in real time, and generate clinically relevant performance measures through an easily accessible Web-based portal. Conclusions The integrated platform offers a better approach for clinical data reporting and analytics. Additionally, the platform streamlines the workflow of AAC clinical service delivery.