In the last two decades, studies about using technology for automatic detection of human falls increased considerably. The automatic detection of falls allows for quicker aid that is key to increasing the chances of treatment and mitigating the consequences of falls. However, each type of fall has its specificities and determining the correct type of fall can help treat the person who has fallen. Although it is essential to use computational methods to classify falls, there are few studies about that in the literature, especially compared to the studies that propose solutions for fall detection. In this sense, we execute a systematic literature review (SLR) using the (Kitchenham et al., 2009) method to investigate the computational solutions used to classify the different types of falls. We performed a search on Scopus, Web of Science, and PubMed scientific databases looking for computational methods to fall classification in their papers. We use the grounded theory methodology for a more detailed qualitative analysis of the papers. As a result of our search, we selected a total of 36 studies for our review and found two different computational methods for classifying falls. Related to the steps used in each method, we found fourteen different types of sensors, four different techniques for background and foreground extraction of videos, twenty-one techniques for feature extraction, and seven different fall classification strategies. Finally, we also identified fifty-one different types of falls. In conclusion, we believe that the methods and techniques analyzed in our study can help developers to create new and better systems for classification, detection, and prevention of falls and falls database. Besides, we identified gaps that can be explored in future research related to the automatic classification of falls.

Internet access in both private and public environments allows users to broadly access their data what makes possible the deployment of new services based on Internet of Things. This fact created Smart Environments (SEs) that are composed of a huge amount of heterogeneous devices, for example, personal devices (smartphones, notebooks, tablets, etc) and IoT devices (sensors, actuators, and others). However, these environments can facilitate the action of malicious agents interested in promoting Distributed Denial of Service (DDoS) attacks to the network, and, when they are public places, it is challenging to locate these attackers. In this way, it is necessary to deploy solutions that can detect DDoS in SEs and to determine the physical location of the attacker, which is essential to prevent future attacks. Within this context, this article presents an Intelligent System for detection of DDoS and physical location of devices in SEs, applying Machine Learning (ML) and trilateration techniques. The experiments performed, using real network traffic and simulation, suggest that the proposed system is capable of detecting attacks and finding malicious devices.

The world population is aging and this motivates the development of diferent software solutions focused on the older adults' health. In the literature, however, there is a lack of a organization of the information used by these software. To address this gap, we present in this paper a taxonomy to support the development of software applications dealing with the older adults' health. This taxonomy is composed by 87 characteristics and 21 categories that organize the information that should be handled by this kind of application. The assessment of our taxonomy was performed with a group of five TIC experts with experience in digital healthcare and two healthcare professionals that work with older adutls. From this evaluation, we improve the taxonomy according to the inter-rater agreement among the experts. As a result, our taxonomy may be used to guide the development of healthcare applications for older adults.

Falls among older adults represent a critical public health issue, being one of the leading causes of severe injuries and accident-related mortality. because of this, Fall Detection Systems face significant challenges due to the complexity of environments and variability in movement patterns. On this matter, Data fusion, including method-level, sensor-level, feature-level, and decision-level approaches, offers a promising method to enhance detection accuracy. However, decision-level fusion remains under-explored, despite its potential to integrate outputs from multiple classifiers to address these limitations. This article presents a comparative study on the performance of decision-level fusion strategies in fall detection systems, employing different configurations of sensors, classifiers, and decision fusion algorithms. A dataset was collected using four sensor modalities: Accelerometer, Infrared Camera, Light Detection and Ranging, and Radar. Three classifiers were employed, and five decision-level fusion strategies were evaluated. Performance was assessed across three levels of simulated noise using four metrics: accuracy, sensitivity, specificity, and precision. The implementation of decision-level fusion strategies resulted in improvements in the performance of fall detection systems. Accuracy increased from 85% without decision-level fusion to 93% using Random Forest. Among deterministic strategies, Voting with Accuracy achieved competitive results. Light Detection and Ranging, demonstrated robustness under noise, while other sensors showed varying sensitivity to noise. These results demonstrate the importance of utilizing decision-level fusion strategies, as they lead to improvements in system performance. This enhancement is achieved by leveraging the complementarity of multiple technologies, allowing systems to adapt effectively to diverse and dynamic environments. This functionality is particularly useful in standalone or closed solutions, where such complementarity can address inherent limitations and enhance overall system capabilities.

A Dynamic Software Product Line (DSPL) allows the generation of products that can adapt dynamically according to changes in requirements or environment at runtime. This runtime adaptation is often made by the activation and deactivation of features, introducing a cost (e.g., an overhead regarding resource consumption). To reduce this cost, a solution is the partial product configuration at the static binding time. Thus, in DSPLs, one challenge is the feature selection to define which features should be bound permanently before the initial launch and which features should be bound at runtime. In this paper, we address this challenge presenting a graph model formulation to the feature selection problem for the initial launch in DSPLs that considers both static and dynamic binding. This model allows the application of efficient optimization algorithms to solve the problem. We also present a proof of concept showing that the model can be used to generate optimized solutions to the featur e selection problem for initial launch in DSPLs.

Sistemas autoadaptativos possuem a capacidade de adaptar seu comportamento durante sua execução em resposta às mudanças no ambiente onde está inserido. Para garantir o sucesso de uma adaptação, abordagens tradicionais como testes e verificações de software são insuficientes. Por isso, deve-se adotar abordagens de monitoramento e verificação em tempo de execução para assegurar que as adaptações tenham o resultado esperado. Neste trabalho, é proposta uma ferramenta para realizar, de forma integrada, o monitoramento, a verificação e a notificação de violações encontradas durante a execução de um sistema autoadaptativo.