Activity Learning for Intelligent Buildings To be considered intelligent, buildings need to operate automatically in a usercentric fashion. Intelligent buildings enable and ensure a healthy, comfortable and productive working environment for their occupants. The transition to intelligent buildings requires systems that can recognise the needs and anticipate the behaviour of the users. We foster such a transition by learning the activities that occupants perform in buildings. This is done by first recognising the activities and then predicting the time of their next occurrences. The idea rests upon an existing formalism for a building environment and applies machine learning. We implement and deploy a system in a living lab environment, showing that it can learn diverse occupant activities with high accuracy, provided simple and unobtrusive sensors.

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.