Appropriate teaching–learning strategies lead to student engagement during learning activities. Scientific progress and modern technology have made it possible to measure engagement in educational settings by reading and analyzing student physiological signals through sensors attached to wearables. This work is a review of current student engagement detection initiatives in the educational domain. The review highlights existing commercial and non-commercial wearables for student engagement monitoring and identifies key physiological signals involved in engagement detection. Our findings reveal that common physiological signals used to measure student engagement include heart rate, skin temperature, respiratory rate, oxygen saturation, blood pressure, and electrocardiogram (ECG) data. Similarly, stress and surprise are key features of student engagement.

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Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson’s disease, while tremors predominate in epilepsy and Alzheimer’s disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.

The aim of this work is propose and describe ImagIngDev, a new approach for developing automatic cross-platform mobile applications using image processing techniques. As proof of concept, we presented ImagIng Tool and compared its performance with respect to similar cross-platform application development tools. Our main contribution to software development is ImagingDev, a new, intuitive and agile approach aimed at novice developers for automatically developing cross-platform mobile applications. Our proof of concept, ImagIng Tool, can generate mobile applications on four different software platforms: Android™, Windows Phone™, iOS™ and FirefoxOS™. Also, it relies on image processing techniques to recognize user interface design patterns (UIDPs) inside user interfaces, and generates the source code for cross-platform and multi-device applications. As proof of concept, we developed ImagIng Tool for automatic generation of cross-platform mobile applications by using image processing techniques. Results from the evaluation demonstrated that ImagIng Tool has attractive benefits if compared to similar cross-platform application development tools. Such benefits include higher learnability and usability. ImagIngDev and ImagIng Tool can effectively solve current problems in mobile applications development, such as automatic code generation for cross-platform mobile applications, since they allow users to generate cross-platform and multi-device mobile applications in an easy and intuitive way.

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