Maria Cristina Canavarro Teixeira

The detection of the environment where the user is located, is of extreme use for the identification of Activities of Daily Living (ADL). ADL can be identified by use of the sensors available in many off-the-shelf mobile devices, including magnetic and motion, and the environment can be also identified using acoustic sensors. The main objective of this study is to recognize the environments and some standing ADL to include in the development of a framework for the recognition of ADL and its environments. The study presented in this paper is divided in two parts: firstly, we discuss the recognition of the environment using acoustic sensors (i.e., microphone), and secondly, we fuse this information with motion and magnetic sensors (i.e., motion and magnetic sensors) for the recognition of standing ADL. The recognition of the environments and the ADL are performed using pattern recognition techniques, in order to develop a system that includes data acquisition,data processing, data fusion, and classification methods. The classification methods explored in this study are composed by different types of Artificial Neural Networks (ANN), comparing the different types of ANN and selecting the best methods to implement in the different stages of the system developed. Conclusions point to the use of Deep Neural Networks (DNN)with normalized data for the identification of ADL with 85.89% of accuracy, the use of Feedforward Neural Networks (FNN) with non-normalized data for the identification of the environments with 86.50% of accuracy, and the use of DNN method with normalized data for the identification of standing activities with 100% of accuracy.

Android Library for Recognition of Activities of Daily Living: Implementation Considerations, Challenges, and Solutions

The polemic fusion of the Schools in mega-clusters has been the focus of many stakeholders in the process, interested mainly in understanding the underlying criteria to this merger. In Castelo Branco there were four groups taking the name of the school that became their headquarters: Afonso de Paiva, Faria de Vasconcelos, Cidade de Castelo Branco and João Roiz. Out of this process there were two secondary schools in the city: Nuno Alvares and Amato Lusitano. These schools were georeferenced in the city road network, previously scanned. The ArcGIS software and Network Analyst extension were used. They allowed addressing the issue of the formation of mega-clusters by analyzing the network, namely by solving coverage problems with time and distance criteria. Heuristically, a found solution consisted in grouping three schools belonging to the west axis of the city and in the creation of a second mega-group for the remaining three schools. This solution presents a balanced distribution of the school population, since the difference between these two mega-groups corresponds to about 2% of the total population.