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Previous research suggests that Participatory Design (PD) is a very promising approach for involving children with Autism Spectrum Conditions (ASC) in generating design solutions, but also in practising essential skills (e.g. collaboration skills). However, because of their difficulties with social communication accompanied by rigidity in thought and behaviour, these children face significant challenges during PD. Specifically, in the idea generation stage of PD, they have difficulties expressing their creativity. Based on user participation, we built a first version of a low fidelity prototype (iGeneration) for a technology-based tool to elicit creativity in children with ASC during idea generation. This article reports on a research study which explored the feasibility of iGeneration to support children with ASC in creating design ideas for an educational game. The results revealed that the participants engaged and enjoyed working with iGeneration. The children created a number of ideas which were diverse, novel and appropriate for the design tasks.

The rate of successful completion of studies for students with Autism Spectrum Disorders (ASD) in Higher Education (HE) is only 40%, which is lower than that for many other students with disabilities. This is caused by a series of challenges encountered by this population in HE. This paper reports on research studies which identified the top challenges faced by HE students with ASD and explored how a Skill (a custom application) for Amazon Alexa Echo Show (AAES) can be designed to support them in overcoming these challenges. A Skill named MotivateMe! was implemented in two iterations for the AAES. During the evaluation study of MotivateMe!, the Skill was perceived by students and experts as being appropriate for students with ASD to deal with distractions and to motivate them to study (which were identified as being the top challenges).

Additional file 2: Supplementary Data. Results of LD-RT-PCR for EGFRvIII detection.

Additional file 1. Provides the following survival curves: PFS according to cfDNA level at baseline. OS according to cfDNA level at baseline. PFS according to CA-15.3 level at baseline. OS according to CA-15.3 level at baseline.

Additional file 4: Supplementary Material: cost evaluation of FISH method.

Additional file 1: Supplementary Table. Details of EGFR amplicons by NGS.

Additional file 1. Supplementary methods.

Children with Autism Spectrum Disorders (ASD) have difficulties with social communication and interaction. Social StoriesTM are a well-known intervention to help them overcome these diculties. During these interventions, practitioners must check children's understanding of various concepts that are taught. However, this activity - comprehension checking - is often skipped as practitioners nd it dicult and time consuming. Our project explores how a technology-based tool (the S2C2 app) can be designed and developed to support Social StoryTM comprehension checking in children with ASD (aged 7-12) by involving typically developing children (TD) and experts in ASD and HCI. An initial pilot evaluation with sixteen TD children and ve experts in ASD and HCI suggested that the S2C2 app provides appropriate engaging activities for children and facilitates Social StoryTM comprehension checking. However, caution must be taken in extending the results and more studies involving children with ASD are planned to be conducted in the future.

Despite its inherent challenges, participatory design (PD) has unique benefits when designing technology for children, especially children with special needs. Researchers have developed a multitude of PD approaches to accommodate specific populations. However, a lack of understanding of the appropriateness of existing approaches across contexts presents a challenge for PD researchers. This workshop will provide an opportunity for PD researchers to exchange and reflect on their experiences of designing with children with special needs. We aim to identify, synthesize and collate PD best practices across contexts and participant groups.

Previous work suggests involving autistic children in participatory design (PD) benefits them. However, express thoughts are two of the most acknowledged difficulties autistic children face during the PD process, particularly during the stage of idea generation. These are linked to the disorder-specific impairments in social interaction and rigidity in thought and behaviour. Recent work recommended technology as a tool to support children during idea generation. Building up on these efforts, this paper proposes using crowdsourcing with a team of selected online workers, as a way to generate child-specific hints to help autistic children unlock their creativity. Moreover, machine learning techniques and the majority decision approach are suggested to remove noise, redundancy, and select the child-specific hints.

National audience; Vocabulary acquisition is considered one of the most important aspects of a language because it defines and constraints communication. In order to overcome this, students and teachers use various techniques meant to improve the learning process and facilitate word acquisition. Using conceptual networks that establish links to prior knowledge is an approach with proven good results. The game presented in this paper, Semantic Taboo, is a serious game that helps users enlarge their conceptual network. The game has two modes, one for teachers and one for students. In the teacher module, users have the option of adding playing cards. The theme of the card (the seed) is taken as input, while the list of taboo words is selected from the list of semantically related words provided by the ReaderBench framework. The student model has the same game principle as the traditional game, but the list of taboo words and the description of the seed help the learner acquire new words. The preliminary validation consisted of 15 users who were impressed by the game concept and its educational value.

The rapidly expanding field of Big Data is a result of the data deluge generated by the rapid increase in the use of mobile devices and social media. It has provided tools to collect, store, manage, and analyze huge volumes of data (either structured, semi-structured or unstructured) produced by current healthcare systems. Big Data Analytics has started to play a crucial role in the evolution of healthcare practices and research, being now applied towards aiding the process of care delivery and disease exploration. Cloud computing enables all Big Data operations through the provision of large storage and processing power. This paper introduces all of these notions in the context of healthcare. It discusses the advantages, but also challenges brought by Big Data to this field. It also proposes a Big Data Architecture for healthcare. Finally, it presents some of the areas of application of Big Data to healthcare in Romania.

International audience

Several European countries, including the UK, are investing in large-scale telehealthcare pilots, to thoroughly evaluate the benefits of telehealthcare. Due to the high level of risk associated with such projects, it becomes desirable to be able to predict the success of telehealthcare systems in potential deployments, in order to inform investment and help save resources. An important factor for the success of any telehealthcare deployment is usability, as it helps to achieve the benefits of the technology through increased productivity, decreased error rates, and better acceptance. In particular, efficiency, one of the characteristics of usability, should be seen as a central measure for success, as the timely care of a high number of patients is one of the important claims of telehealthcare. Despite the recognized importance of usability, it is seen as secondary in the design of telehealthcare systems. The resulting problems are difficult to predict due to the heterogeneity of deployment contexts. This thesis proposes the automation of usability evaluation through the use of modelling and simulation techniques. It describes a generic methodology which can guide a modeller in reusing models for predicting characteristics of usability within different deployment sites. It also describes a modelling approach which can be used together with the methodology, to run in parallel a user model, inspired from a cognitive architecture, and a system model, represented as a basic labelled transition system. The approach simulates a user working with a telehealthcare system, and within her environment, to predict the efficiency of the system and work process surrounding it. The modeller can experiment with different inputs to the models in terms of user profile, workload, ways of working, and system design, to model different potential- real or hypothetical- deployments, and obtain efficiency predictions for each. A comparison of the predictions helps analyse the effects on efficiency of changes in deployments. The work is presented as an experimental investigation, but emphasises the great potential of modelling and simulation for helping to inform investment, help reduce costs, mitigate risks and suggest changes that would be necessary for improving the usability, and therefore success or telehealthcare deployments. My vision is that, if used commercially, the approaches presented in this thesis could help reduce risks for scaling up telehealthcare deployments.

The success of telemedicine systems requires sufficiently high productivity, satisfaction and low error rates: in short, usability. However, the costs associated with performing pre-deployment usability studies often make them infeasible. This results in usability problems which may manifest differently in different deployment contexts and cause different levels of inefficiency and discomfort to their users, potentially even leading to errors which may threaten patients' lives. This paper shows by means of examples how experimentation with different workloads, user profiles and system characteristics can suggest problems that may arise in deploying a telemedicine system into new contexts. Such approaches might support the roll-out of large telemedicine implementations, as the one currently planned in the UK.