Context: Ethics have broad applications in different fields of study and different contexts. Like other fields of study, ethics have a significant impact on the decisions made in computing concerning software artifact production and its processes. Hence, in this research, ethics is considered in the context of requirements engineering during the software development process. Objective: The aim of this paper is to discuss the investigation results regarding ethical problems of requirements engineering processes by taking sample software developing companies and exposing existing research gaps. Method: This research uses interviewing, focus group discussions, purposive sampling, and qualitative analysis research methods. Result: This research finds an absence of industry practices, professional responsibility code of conduct standards, and other guidelines within companies when integrating ethical concerns of software during requirements engineering. It also indicates that almost all companies have no identification methods and checking mechanisms for ethical concern considerations. Furthermore, the major identified ethical concerns are classified into six categories as requirements identification problems, quality-related problems, carrying out unpermitted activities, unwillingness to give requirements, knowledge gaps and lack of legal grounds/rules for accountability. Conclusion: From the findings of this research, it can be concluded that, in the case software companies, there is no specific method for identifying ethical concerns. Additionally, there are no standards and guidelines used within the companies. This implies the need to overcome the existing and emerging ethical issues of requirements engineering.

World-wide statistics show a considerable growth of the occurrence of different types of Diabetes Mellitus, posing diverse challenges at many levels for public health policies. Some of these challenges may be addressed by means of computerised systems which may pave the way to provide practitioners with insight on their patient’s conditions anywhere and at anytime, but also to empower Diabetes patients as managers of their health. These systems for disease management come in many shapes and sizes, being the most promising trends the ones that involve expert systems that comprise specialised knowledge, use predictive models, feature engineering and reasoning. This study presents the state-of-the-art on reasoning and prediction models related with either blood glucose level or hypoglycaemia events. The main findings revealed are that there is room for improvement on predictive models, namely to enhance its accuracy and ability to forecast future events into a wider time frame. On the other hand, reasoning models are understudied and its usage in Diabetes management is reduced. We discuss an architecture that combines a predictive model and a reasoning system, with the objective of alerting of impending occurrences and interpret the current situation to accurately advise the diabetic user.

Healthcare treatments might benefit from advances in artificial intelligence and technological equipment such as smartphones and smartwatches. The presence of cameras in these devices with increasingly robust and precise pattern recognition techniques can facilitate the estimation of the wound area and other telemedicine measurements. Currently, telemedicine is vital to the maintenance of the quality of the treatments remotely. This study proposes a method for measuring the wound area with mobile devices. The proposed approach relies on a multi-step process consisting of image capture, conversion to grayscale, blurring, application of a threshold with segmentation, identification of the wound part, dilation and erosion of the detected wound section, identification of accurate data related to the image, and measurement of the wound area. The proposed method was implemented with the OpenCV framework. Thus, it is a solution for healthcare systems by which to investigate and treat people with skin-related diseases. The proof-of-concept was performed with a static dataset of camera images on a desktop computer. After we validated the approach's feasibility, we implemented the method in a mobile application that allows for communication between patients, caregivers, and healthcare professionals.

In the last decade, we have seen an increase in the use cases of digital technologies in our daily lives, including advanced systems such as our mobile applications, smart digital kiosks for intelligent retailing, touch screens for ordering food, or simple implementations such as the ticket machines in the butchers. However, from the perspective of interaction design, those sensing systems suffer from a range of limitations like low usability and poor hygiene. For example, the elderly or the disabled normally have huge difficulties when interacting with these types of digital systems. Moreover, the recent SARS-CoV-2 pandemic has made us to rethink the contact manner for interaction with the digital devices. Hence, in this paper, we have presented a novel solution for digital interaction through a contactless manner. Such a system can provide human gesture recognition and therefore it can be integrated into others to achieve contactless control. We have implemented a prototype based on cost efficient sensors to validate the idea’s feasibility. A sequence of real world experiments has also been conducted to evaluate its performance. This system is composed with 1) piece of hardware–a grid of ultrasound sensors to capture the distance information from a human body; 2) a piece of software to analyse the data for gesture predictions. The analysis results then will be transformed into control commands to interact with the attached system. The experimental results have shown that the proposed system is capable of providing a contactless Human Computer Interaction (HCI), and also has a great opportunity to replace existing touching interaction manner with a remote control scheme.

Background and aim Hypoglycaemia prediction play an important role in diabetes management being able to reduce the number of dangerous situations. Thus, it is relevant to present a systematic review on the currently available prediction algorithms and models for hypoglycaemia (or hypoglycemia in US English) prediction. Methods This study aims to systematically review the literature on data-based algorithms and models using diabetics real data for hypoglycaemia prediction. Five electronic databases were screened for studies published from January 2014 to June 2020: ScienceDirect, IEEE Xplore, ACM Digital Library, SCOPUS, and PubMed. Results Sixty-three eligible studies were retrieved that met the inclusion criteria. The review identifies the current trend in this topic: most of the studies perform short-term predictions (82.5%). Also, the review pinpoints the inputs and shows that information fusion is relevant for hypoglycaemia prediction. Regarding data-based models (80.9%) and hybrid models (19.1%) different predictive techniques are used: Artificial neural network (22.2%), ensemble learning (27.0%), supervised learning (20.6%), statistic/probabilistic (7.9%), autoregressive (7.9%), evolutionary (6.4%), deep learning (4.8%) and adaptative filter (3.2%). Artificial Neural networks and hybrid models show better results. Conclusions The data-based models for blood glucose and hypoglycaemia prediction should be able to provide a good balance between the applicability and performance, integrating complementary data from different sources or from different models. This review identifies trends and possible opportunities for research in this topic.