Abstract-Recent years have witnessed major advances in technologies to support children diagnosed with Autism Spectrum Disorders (ASD). A number of applications have impacted their daily lives, with the goal of enhancing their abilities to understand, interact and communicate with others. The usability of mobile applications developed for people with ASD is important because this group is usually lacking in experience and familiarity with many aspects of new technologies. This paper focuses on comparing the usability of two Arabic mobile applications available in the Apple Store (iPad). Usability of these applications is analyzed using eye tracking and Morae ® . Measurement tools are used to collect qualitative and quantitative data and determine the participants' satisfaction with products. At the end, we presented proposed recommendation for optimal design based on the result of this study. I. INTRODUCTIONAutism is known as a complex developmental disability. People with autism have issues with verbal communication, a limited range of social interactions, and activities that include an element of play and/or banter [1]. ASD stands for Autism Spectrum Disorder, a set of situations that affect the daily functions of people's lives [2]. They include impairments in social interaction, communication, repeated behavior, interests, and activities [3]. The Autism Society of America defines autism as a -complex developmental disability that typically appears during the first 3 years of life and is the result of a neurological disorder that affects the normal functioning of the brain, impacting development in the areas of social interaction and communication skills‖ [4].With increasing numbers of children, being diagnosed with Autism Spectrum Disorders (ASD) [5], a variety of mobile applications have been developed to enhance social skills for children with ASD. Our research has concentrated on studying the usability of two Arabic mobile applications designed to enhance the social skills of children with ASD.The use of the multi touch tablet, such as the iPad, is increasing at an exponential rate [6]. This is likely due to the cost effectiveness and market penetration of the iPad device The authors are with King Saud University, Saudi Arabia (e-mail: lalwakeel@ut.edu.sa, amjad.alghanim@gmail.com, salzeer@su.edu.sa, kalnafjan@ksu.edu.sa).[6]. In addition, mobile applications are socially accepted and simple to use and hold for children with ASD. Mobile applications are software applications, usually designed to run on smartphones and tablet devices [7]. Mobile applications are available through distribution platforms, which are typically operated by the owner of the mobile operating system, such as the Apple App Store, Android Market, and BlackBerry App World. Some applications are free, while others require payment. Usually they are downloaded from the platform to a target device such as an iPad, BlackBerry, or Android phone [8].Today, there are a large number of new mobile applications but many of them are difficult ...
Background Although the past decade has witnessed the development of many self-management mobile health (mHealth) apps that enable users to monitor their health and activities independently, there is a general lack of empirical evidence on the functional and technical aspects of self-management mHealth apps from a software engineering perspective. Objective This study aims to systematically identify the characteristics and challenges of self-management mHealth apps, focusing on functionalities, design, development, and evaluation methods, as well as to specify the differences and similarities between published research papers and commercial and open-source apps. Methods This research was divided into 3 main phases to achieve the expected goal. The first phase involved reviewing peer-reviewed academic research papers from 7 digital libraries, and the second phase involved reviewing and evaluating apps available on Android and iOS app stores using the Mobile Application Rating Scale. Finally, the third phase involved analyzing and evaluating open-source apps from GitHub. Results In total, 52 research papers, 42 app store apps, and 24 open-source apps were analyzed, synthesized, and reported. We found that the development of self-management mHealth apps requires significant time, effort, and cost because of their complexity and specific requirements, such as the use of machine learning algorithms, external services, and built-in technologies. In general, self-management mHealth apps are similar in their focus, user interface components, navigation and structure, services and technologies, authentication features, and architecture and patterns. However, they differ in terms of the use of machine learning, processing techniques, key functionalities, inference of machine learning knowledge, logging mechanisms, evaluation techniques, and challenges. Conclusions Self-management mHealth apps may offer an essential means of managing users’ health, expecting to assist users in continuously monitoring their health and encourage them to adopt healthy habits. However, developing an efficient and intelligent self-management mHealth app with the ability to reduce resource consumption and processing time, as well as increase performance, is still under research and development. In addition, there is a need to find an automated process for evaluating and selecting suitable machine learning algorithms for the self-management of mHealth apps. We believe that these issues can be avoided or significantly reduced by using a model-driven engineering approach with a decision support system to accelerate and ameliorate the development process and quality of self-management mHealth apps.
Artificial intelligence (AI) techniques such as machine learning (ML) have wide application in medical informatics systems. In this chapter we employ AI techniques to assist in deriving software specifications of e-Health and m-Health systems from informal requirements statements. We use natural language processing (NLP), optical character recognition (OCR) and machine learning to identify required data and behaviour elements of systems from textual and graphical requirements documents. Heuristic rules are used to extract formal specification models of the systems from these documents. The extracted specifications can then be used as the starting point for automated software production using model-driven engineering (MDE). We illustrate the process using an example of a stroke recovery assistant app, and evaluate the techniques on several representative systems.
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