Towards a Smart Home for Elder Healthcare Smart homes play an important role in ehealth monitoring, using the various components and devices that intercommunicate with the home system. Thus, it has a huge impact on medical processes, healthcare practices and safety of elder people. Existing ehealth monitoring systems do not include optimized techniques which learn the precise patientu0027s behavior to predict future important changes. In this work, we propose a new contextaware system, which monitors daily living activities and evaluates dependency based on the Functional Autonomy Measurement System (SMAF). In fact, our system evaluates a personu0027s dependency, predicts its health condition, detects abnormal situations and risky behavioral changes and offers adapted services.

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.