In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors for recognition of human activities in AmI is a good practice because the information missed by one sensor can sometimes be provided by the others and many works have shown an accuracy improvement compared to single sensors. However, there are many different ways of integrating the information of each sensor and almost every author reporting sensor fusion for activity recognition uses a different variant or combination of fusion methods, so the need for clear guidelines and generalizations in sensor data integration seems evident. In this survey we review, following a classification, the many fusion methods for information acquired from sensors that have been proposed in the literature for activity recognition; we examine their relative merits, either as they are reported and sometimes even replicated and a comparison of these methods is made, as well as an assessment of the trends in the area.

Multi-sensor fusion refers to methods used for combining information coming from several sensors (in some cases, different ones) with the aim to make one sensor compensate for the weaknesses of others or to improve the overall accuracy or the reliability of a decision-making process. Indeed, this area has made progress, and the combined use of several sensors has been so successful that many authors proposed variants of fusion methods, to the point that it is now hard to tell which of them is the best for a given set of sensors and a given application context. To address the issue of choosing an adequate fusion method, we recently proposed a machine-learning data-driven approach able to predict the best merging strategy. This approach uses a meta-data set with the Statistical signatures extracted from data sets of a particular domain, from which we train a prediction model. However, the mentioned work is restricted to the recognition of human activities. In this paper, we propose to extend our previous work to other very different contexts, such as gas detection and grammatical face expression identification, in order to test its generality. The extensions of the method are presented in this paper. Our experimental results show that our extended model predicts the best fusion method well for a given data set, making us able to claim a broad generality for our sensor fusion method.

The adoption of Agile software development approaches has been widespread. One well-known Agile approach is extreme programming, which encompasses twelve practices of which pair programming is one of them. Although various aspects of pair programming have been studied, we have not found, under a traditional setting of pair programming, studies that examine the impact of using a tool support, such as an integrated development environment (IDE) or a simple text editor. In an attempt to obtain a better understanding of the impact of using an IDE in this field, we present the results of a controlled experiment that expose the influence on quality, measured as the number of defects injected per hour, and cost, measured as the time necessary to complete a programming assignment, of pair and solo programming with and without the use of an IDE. For quality, our findings suggest that the use of an IDE results in significantly higher defect injection rates (for both pairs and solos) when the programming assignment is not very complicated. Nevertheless, defect injection rates seem to decrease when pairs work on more complicated programming assignments irrespective of the tool support that they use. For cost, the programming assignment significantly affects the time needed to complete the assignment. In relation to the programming type, pairs and solos performed in a similar way with regards to quality and cost.

Sensors are becoming more and more ubiquitous as their price and availability continue to improve, and as they are the source of information for many important tasks. However, the use of sensors has to deal with noise and failures. The lack of reliability in the sensors has led to many forms of redundancy, but simple solutions are not always the best, and the precise way in which several sensors are combined has a big impact on the overall result. In this paper, we discuss how to deal with the combination of information coming from different sensors, acting thus as “virtual sensors”, in the context of human activity recognition, in a systematic way, aiming for optimality. To achieve this goal, we construct meta-datasets containing the “signatures” of individual datasets, and apply machine-learning methods in order to distinguish when each possible combination method could be actually the best. We present specific results based on experimentation, supporting our claims of optimality.

Multi-sensor fusion intends to boost the general reliability of a decision-making procedure or allow one sensor to compensate for others’ shortcomings. This field has been so prominent that authors have proposed many different fusion approaches, or “architectures” as we call them when they are structurally different, so it is now challenging to prescribe which one is better for a specific collection of sensors and a particular application environment, other than by trial and error. We propose an approach capable of predicting the best fusion architecture (from predefined options) for a given dataset. This method involves the construction of a meta-dataset where statistical characteristics from the original dataset are extracted. One challenge is that each dataset has a different number of variables (columns). Previous work took the principal component analysis’s first k components to make the meta-dataset columns coherent and trained machine learning classifiers to predict the best fusion architecture. In this paper, we take a new route to build the meta-dataset. We use the Sequential Forward Floating Selection algorithm and a T transform to reduce the features and match them to a given number, respectively. Our findings indicate that our proposed method could improve the accuracy in predicting the best sensor fusion architecture for multiple domains.

El artículo presenta un experimento controlado en el que se explora la bondad de utilizar la Teoría de Roles de Belbin para la integración de equipos de desarrollo software. El estudio se desarrolla en un entorno académico con estudiantes de la carrera de Ingeniería de Software y compara la calidad de la legibilidad del código generado por equipos integrados con roles Compatibles —de acuerdo con la Teoría de Belbin— y equipos Tradicionales, en nuestro caso, equipos integrados con estudiantes seleccionados de manera aleatoria. Los resultados obtenidos aportan evidencia positiva en torno al uso de dicha teoría y motivan a los investigadores a continuar estudios en otras actividades vinculadas al proceso de desarrollo software; por otro lado, desde la perspectiva pedagógica, las resultados obtenidos con el experimento, permiten proponer como alternativa para integración de equipos de trabajo, en escenarios de aprendizaje para la Ingeniería de Software, la Teoría de Roles de Belbin.