Experiments in assisted living confirm that such systems can provide context-aware services that enable occupants to remain active and independent. They also demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a technique that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets that include multiple individuals, and show consistent rates of anomaly detection across different environments.

Recognising high-level human activities from low-level sensor data is a crucial driver for pervasive systems that wish to provide seamless and distraction-free support for users engaged in normal activities. Research in this area has grown alongside advances in sensing and communications, and experiments have yielded sensor traces coupled with ground truth annotations about the underlying environmental conditions and user actions. Traditional machine learning has had some success in recognising human activities; but the need for large volumes of annotated data and the danger of overfitting to specific conditions represent challenges in connection with the building of models applicable to a wide range of users, activities, and environments. We present USMART, a novel unsupervised technique that combines data- and knowledge-driven techniques. USMART uses a general ontology model to represent domain knowledge that can be reused across different environments and users, and we augment a range of learning techniques with ontological semantics to facilitate the unsupervised discovery of patterns in how each user performs daily activities. We evaluate our approach against four real-world third-party datasets featuring different user populations and sensor configurations, and we find that USMART achieves up to 97.5% accuracy in recognising daily activities.

Construct differs from other pervasive systems platforms in a number of key respects. It is completely standards-based, using RDF as its data exchange model and ZeroConf for resource discovery. It supports a knowledge-centric model of interaction where clients' actions are driven by queries and triggers about the context of the system. It uses gossiping to maintain a consistent state across a distributed data structure, which maximises robustness and scalability and avoids many problems with hot-spots and hot-paths in communications. Finally, it treats all information sources uniformly as sensors acting as inputs to uncertain reasoning algorithms.