Smart Homes and Assisted Living as an Additional Service Offered to the Users For several years, ENEA has been conducting a research project funded by the Ministry of Economic Development, within a broader context that envisages the development of an integrated Smart Urban District model. Among the objectives of the project, there is the development of a replicable Smart Home model capable of stimulating energy savings and, at the same time, offering innovative services that aim to improve the quality of life of end users, in particular fragile users as well as older people. This paper presents the integration on the Smart Home developed architecture of a useful additional assisted living service, namely MySignals service, which is able to monitor the vital parameters of home users.

OpenSource Data Collection and Data Sets for Activity Recognition in Smart Homes As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the Ecarehome system, its installation in a real home setting, and a series of data sets that were collected using the Ecarehome system. Our first contribution, the Ecarehome system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higherlevel Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with groundtruth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.