Linguistic and temporal resources of pre-stored utterances in everyday conversations

Aided communicators often have an opportunity to express themselves with speech-generating devices (SGDs) that produce symbol by symbol (SBS) and/or pre-stored (PS) utterances. Studies on the usage of PS utterances report that these utterances affect conversations positively, but it appears that aided communicators and professionals may have divergent views on their benefits. The aim of this study is to analyse how school-aged aided communicators, their mothers, peers, and speech and language therapists (SLTs) co-construct the social actions of PS utterances during their everyday interactions. The theoretical framework of this study is conversation analysis. This approach to analysing the data enhances our understanding of the linguistic and temporal resources of PS utterances and how they are used to reinforce various rich social actions that promote the progress of aided conversations to resemble natural spoken conversations. The results of this study will help SLTs and teachers in their planning content for SGDs as well as in teaching, and guiding aided communicators and their partners to utilize PS utterances in combination with SBS utterances during their conversations.Peer reviewe

Computer-based support for patients with limited English

The paper describes a proposal for computer-based aids for patients with limited or no English. The paper describes the barriers to health-care experienced due to linguistic problems, then suggests some computer-based remedies incorporating a multi-engine machine translation system based on a corpus of doctor-patient interviews which provides a dialogue model for the system. The doctor's and patient's interfaces are described. Ideas from Augmentative and Alternative Communication and in particular picture-based communication are incorporated. The initial proposal will focus on Urdu- and Somali-speaking patients with respiratory problems.

Aktivitetsdiamanten : Modellering av en vidareutvecklad tillgänglighet

Syftet med forskningen som presenteras i denna avhandling är att vidareutveckla tillgänglighetsområdet i riktning mot en större perspektivrikedom. Avhandlingen baseras på kulturhistorisk aktivitetsteori (CHAT). Den analyserar en systemisk helhet utifrån sådana mänskliga, artefaktuella och naturliga faktorer som påverkar en individs handlingsmöjligheter i konkreta situationer.Avhandlingen har två huvudresultat:En vidareutvecklad tillgänglighet innehållande:* Epitillgänglighet, tillgänglighetens tidsanda, som innefattar hur erfarenheter av aktiviteter påverkar tillgänglighetsmöjligheter, lärande, förväntningar, attityder, tillit, krav och förnekanden hos individen och hennes mänskliga, artefaktuella och naturliga omvärld.* Levd tillgänglighet, som innefattar individens förväntningar och hur hon i den aktuella situationen upplever möjligheterna att kunna göra det hon vill.* Planerad tillgänglighet, som består av alla förutbestämda tillgänglighetsfaktorer utifrån planer, riktlinjer och principer.Aktivitetsdiamanten, en modell för tillgänglighet: Aktivitetsdiamanten beskriver ett mänskligt aktivitetssystem där subjekt-objekt-kopplingen inte sker direkt utan via mänskliga, artefaktuella och naturliga inslag i miljön. Modellen bygger på samspelet mellan dessa fyra element (subjekt, objekt, omgivande natur/artefakter och människor) och är situerad i tid och rum. Olika aktörer med olika aktivitetssystem kan vara inblandade. Modellen kan också användas longitudinellt över tid.Avhandlingen är baserad på en serie explorativa studier av unika individers aktivitetssystem där människor, artefakter och natur tillsammans påverkar tillgängligheten. Handlingen står i centrum, inte funktionsnedsättningarna och inte heller de diskriminerande faktorerna i samhället.Avhandlingen består av en avhandlingskappa och följande fyra publikationer:I. The Activity Diamond: a model for multifaceted accessibility. Status: Insänd till The Scandinavian Journal of Disability Research 2009-05-05.II. An Activity Systemic Approach to Augmentative and Alternative Communication. Status: Insänd till AAC Journal 2009-07-24.III. Towards the Era of Mixed Reality: Accessibility Meets Three Waves of HCI. Status: Long paper presenterat vid USAB 2009 (Usability & HCI Learning from the Extreme) 2009-11-10, http://usab.icchp.org/. Status: Insänd 2009-07-21, accepterad 2009-09-11.IV. An activity theoretical approach to the International Classification of Functioning, Disability and Health. Status: Insänd till Disability and Rehabilitation 2009-10- 30

Crossroads in aphasia rehabilitation

This thesis focusses on two types of aphasia rehabilitation, cognitive linguistic treatment (CLT) and AAC (Augmentative and Alternative Communication) training. In a study of the effect of nonlinguistic variables on the outcome of CLT, it was shown, that neuropsychological data contributed significantly to the prediction of verbal communicative ability after treatment. It is concluded that a neuropsychological assessment is needed in all aphasic patients before treatment is started. In a study of the efficacy of AAC in people with a severe aphasia, a computerised communication aid, TouchSpeak (TS), was developed and tested in 34 aphasic patients. 57 % of the participants used the device in everyday life scenarios and overall communicative abilities improved with TS training. The final chapter presents a new test for overall (verbal and nonverbal) communication in people with a severe aphasia. In a pilot group, patients with a severe aphasia showed large variation in overall scores. In addition, several communicative patterns could be described

Bayesian machine learning applied in a brain-computer interface for disabled users

A brain-computer interface (BCI) is a system that enables control of devices or communication with other persons, only through cerebral activity, without using muscles. The main application for BCIs is assistive technology for disabled persons. Examples for devices that can be controlled by BCIs are artificial limbs, spelling devices, or environment control systems. BCI research has seen renewed interest in recent years, and it has been convincingly shown that communication via a BCI is in principle feasible. However, present day systems still have shortcomings that prevent their widespread application. In part, these shortcomings are caused by limitations in the functionality of the pattern recognition algorithms used for discriminating brain signals in BCIs. Moreover, BCIs are often tested exclusively with able-bodied persons instead of conducting tests with the target user group, namely disabled persons. The goal of this thesis is to extend the functionality of pattern recognition algorithms for BCI systems and to move towards systems that are helpful for disabled users. We discuss extensions of linear discriminant analysis (LDA), which is a simple but efficient method for pattern recognition. In particular, a framework from Bayesian machine learning, the so-called evidence framework, is applied to LDA. An algorithm is obtained that learns classifiers quickly, robustly, and fully automatically. An extension of this algorithm allows to automatically reduce the number of sensors needed for acquisition of brain signals. More specifically, the algorithm allows to perform electrode selection. The algorithm for electrode selection is based on a concept known as automatic relevance determination (ARD) in Bayesian machine learning. The last part of the algorithmic development in this thesis concerns methods for computing accurate estimates of class probabilities in LDA-like classifiers. These probabilities are used to build a BCI that dynamically adapts the amount of acquired data, so that a preset, approximate bound on the probability of misclassifications is not exceeded. To test the algorithms described in this thesis, a BCI specifically tailored for disabled persons is introduced. The system uses electroencephalogram (EEG) signals and is based on the P300 evoked potential. Datasets recorded from five disabled and four able-bodied subjects are used to show that the Bayesian version of LDA outperforms plain LDA in terms of classification accuracy. Also, the impact of different static electrode configurations on classification accuracy is tested. In addition, experiments with the same datasets demonstrate that the algorithm for electrode selection is computationally efficient, yields physiologically plausible results, and improves classification accuracy over static electrode configurations. The classification accuracy is further improved by dynamically adapting the amount of acquired data. Besides the datasets recorded from disabled and able-bodied subjects, benchmark datasets from BCI competitions are used to show that the algorithms discussed in this thesis are competitive with state-of-the-art electroencephalogram (EEG) classification algorithms. While the experiments in this thesis are uniquely performed with P300 datasets, the presented algorithms might also be useful for other types of BCI systems based on the EEG. This is the case because functionalities such as robust and automatic computation of classifiers, electrode selection, and estimation of class probabilities are useful in many BCI systems. Seen from a more general point of view, many applications that rely on the classification of cerebral activity could possibly benefit from the methods developed in this thesis. Among the potential applications are interrogative polygraphy ("lie detection") and clinical applications, for example coma outcome prognosis and depth of anesthesia monitoring