Text prediction is one of the most widely used techniques to enhance the communication rate in augmentative and alternative communication. Prediction systems are traditionally used by people with disabilities (e.g. people with motor and speech impairments). However, new applications, such as writing short text messages via mobile phones, have recently appeared. A vast amount of heterogeneous text prediction methods and techniques can be found in literature. Their heterogeneity makes it difficult to understand and compare them, in order to select the most convenient technique for a specific design. This paper presents a survey on text prediction techniques with the intention to provide a systematic view of this field. Prediction applications and related features, such as block size, dictionary structure, prediction method, user interface, etc., are examined. In addition, prediction measurement parameters and published results are compared. A large number of factors that may influence prediction results, including the acceptance of the system by the users, are reviewed, and their influence on the performance and usability of the system is discussed.

BackgroundRhythmic Auditory Stimulation (RAS) is an effective technique to improve gait and reduce freezing episodes for Persons with Parkinson’s Disease (PwPD). The BeatHealth system, which comprises a mobile application, gait sensors, and a website, exploits the potential of the RAS technique. This paper describes the tools used for co-designing and evaluating the system and discusses the results and conclusions.MethodsPersonas, interviews, use cases, and ethnographic observations were used to define the functional requirements of the system. Low fidelity prototypes were created for iterative and incremental evaluation with end-users. Field trials were also performed with the final system. The process followed a user centered design methodology defined for this project with the aim of building a useful, usable, and easy-to-use system.ResultsFunctional requirements of the system were produced as a result of the initial exploration phase. Building upon these, mock-ups for the BeatHealth system were created. The mobile application was iterated twice, with the second version of it achieving a rating of 75 when assessed by participants through the System Usability Scale (SUS). After another iteration field trials were performed and the mobile application was rated with an average 78.6 using SUS. Participants rated two website mock-ups, one for health professionals and another for end-users, as good except from minor issues related to visual design (e.g. font size), which were resolved in the final version.ConclusionThe high ratings obtained in the evaluation of the BeatHealth system demonstrate the benefit of applying a user centered design methodology which involves stakeholders from the very beginning. Other important lessons were learned through the process of design and development of the system, such as the importance of motivational aspects, the techniques which work best, and the extra care that has to be taken when evaluating non-functional mock-ups with end users.

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Ambient Assisted Living environments provide support to people with disabilities and elderly people, usually at home. This concept can be extended to public spaces, where ubiquitous accessible services allow people with disabilities to access intelligent machines such as information kiosks. One of the key issues in achieving full accessibility is the instantaneous generation of an adapted accessible interface suited to the specific user that requests the service. In this paper we present the method used by the EGOKI interface generator to select the most suitable interaction resources and modalities for each user in the automatic creation of the interface. The validation of the interfaces generated for four different types of users is presented and discussed.

Abstract.Word-prediction appears to be a good aid to enhance messagecomposition rate for people with physical disabilities. Usually wordprediction is based on statistical information (mainly on word frequencies). Hit rate can be enhanced by trying to imitate the behaviour of a human interlocutor (who uses syntactic and semantic information). In this paper some new approaches based on Artificial Intelligence methods are presented. Advantages of syntactic and semantic analysis in relation to bare statistical methods are studied. Furthermore, the integration with human-computer interfaces for disabled users is also described.