Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.

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 E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home 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 higher-level 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 ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

Automated planning (AI planning) solves complex sequential decision-making problems by searching for action sequences given a domain definition that models the actions a system can take. However, hand-crafting action models for complex problems is often challenging and cumbersome even for experts. To address this, we present Lifted Action Model Learning (LAML), a novel approach for learning planning domains from plan traces obtained by noisy observations of environment states. LAML integrates data lifting, decision-tree learning, and logic induction to derive abstract action models and generate planning domain representations. We evaluate our approach on 23 benchmark domains from the International Planning Competition, comparing its performance to state-of-the-art methods. Our evaluation considers multiple criteria, including domain reconstruction through comparison with reference domains, plan generation feasibility, comparison with historical plans, and plan validation success rate. Experimental results demonstrate that LAML not only reconstructs more accurate action models but also exhibits strong robustness to noise.