Abstract
The peripheral arterial disease (PAD) is characterized by leg pain during walking, and a recommended treatment for this disease is to perform supervised physical activity. In this chapter, a system that monitories the physical activity containing one application for smartwatch, one application for smartphone, and a back-end webservice is presented. The applications collect heart rate, GPS locations, step count, and altitude data. The methodology used for the development of the system was based on the agile method with the production of prototypes. In this chapter, four development cycles, which cover the users' and researchers' needs, are presented. In this work, the main objective is to evaluate the current mobile technologies on the physical activity data collection and the development of a system that assists the users to maintain an active life. © 2018, IGI Global.

Abstract
In this review the objective is to search for technologies that supervise the exercise or physical activity of people suffering from Peripheral Arterial Disease (PAD) at home or in the community. Patients with PAD have walking limitations and their quality of life progressively deteriorates. The regular practice of exercise can help mitigate these effects and even improve their health status. The methodology used was to search for scientific articles published since 2008, with the final result of 18 articles. The results show the most frequent technologies used are based on the accelerometer device, with the tests being performed on a treadmill at a hospital. The hospital tests are expensive, so a useful and viable alternative is the usage of mobile devices to help the health professionals record the exercise performed by their patients suffering with PAD. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
For elderly people, social isolation is one significant factor in the deterioration of their life's quality. It has a profound impact on the general health and is produced by the diminution of social interactions. Nowadays, there is technology that can retrieve contextual data from the user's environment and interact with him in some simple, yet effective manners. The intelligent personal assistants can interact with the person by means of natural voice language. Previously, it was created a model for the adoption of electronic intelligent assistants by the elderly, as well a preliminary evaluation of the features of the intelligent personal assistants, currently available in the consumer market. In this article, it is evaluated the option of using the current consumer digital assistants to implement the proposed model. Several assistants were examined (Amazon, Google, Microsoft, and Apple), and their functionalities evaluated by creating four interaction scenarios and assessing the assistants' compliance with these scenarios.

Abstract
This study has the objective of select the best service at image processing and recognition, running in the cloud, and best suited for usage in systems to aid and improve the daily lives of blind people. To accomplish this purpose, a set of candidate services was built, including Microsoft Cognitive Services and Google Cloud Vision. A test mobile app was developed to automatically take pictures, which are sent to the online cloud services for processing. The results and the functionalities were evaluated with the aim to measure their accuracy and relevance. The following variables were registered: relative accuracy, represented by the ratio of the number of accurate results vs. the number of results shown; confidence degree, representing the service accuracy (when provided by the service); and relevance, identifying situations that can be useful in the daily lives of the blind people. The results have shown that these two services, Microsoft Cognitive Services and Google Cloud Vision, provided good accuracy and significance, in supporting systems to help blind people in their daily tasks. It was chosen some functionalities in two APIs of services running in the cloud like face identification, image description, objects, and text recognition. © BEIESP.

Abstract
Peripheral artery disease (PAD) main symptom is intermittent claudication, causing pain and limiting the walking abilities of patients, forcing individuals to temporarily stop walking. One treatment advised to counteract the effects of this disease is the practice of physical exercise with monitoring. Currently the monitored exercise programs are applied at the hospital, so some patients have to travel long distances three times a week, with high costs and low adherence of the patients. This paper presents the cocreation process of a mobile application for quantified supervised home-based exercise therapy on PAD patients. The study aimed to design a solution adapted to users' needs, which collects the necessary information for the therapy supervision by health professionals. The users' behaviour with the application allowed the assessment to a set of limitations and potential sources of noise in the supervision data that suggest the evolution to a pervasive solution, by minimizing, or even eliminating, the interaction with the users. The developed tool is a first step towards the creation of a technological ecosystem for the prescription of supervised therapeutic physical exercise, which leverages self-care and allows access to this type of therapy to the entire population. Cardiovascular disease represents a considerable economic burden to society, therefore effective preventive measures are necessary.

Abstract
The harsh impacts of extreme weather events like cyclones or precipitation extremes are increasingly being felt with hazardous consequences. These extreme events are exceptions to well-known weather patterns and therefore are not forecasted with current automatic computational methods. In this context, the use of human computation to annotate extreme atmospheric phenomena could provide novel insights for computational forecasting algorithms and a step forward in climate change research by enabling the early detection of abnormal weather conditions. However, existing crowd computing solutions have technological limitations and show several gaps when involving expert crowds. This paper presents a research approach to fulfill some of the technological and knowledge gaps for expert crowds’ participation. A case study on expert annotation of extreme atmospheric phenomena is used as a baseline for an innovative architecture able to support expert crowdsourcing. The full stack service-oriented architecture ensures interoperability and provides an end-to-end approach able to fetch weather data from international databases, generating experts’ visualizations (weather maps), annotating data by expert crowds, and delivering annotated data for processing weather forecasts. An implementation of the architecture suggests that it can deliver an effective mechanism for expert crowd work while solving some of the identified issues with extant platforms. Therefore, we conclude that the proposed architecture has the potential to contribute as an effective annotation solution for extreme weather events. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.