Towards adaptive control in smart homes: Overall system design and initial evaluation of activity recognition

This paper proposes an approach for adaptive control over devices within a smart home, by learning user behavior and preferences over time. The proposed solution leverages three components: activity recognition for realising the state of a user, ontologies for finding relevant devices within a smart home, and machine learning for decision making. In this paper, the focus is on the first component. Existing algorithms for activity recognition are systematically evaluated on a real-world dataset. A thorough analysis of the algorithms’ accuracy is presented, with focus on the structure of the selected dataset. Finally, further study of the dataset is carried out, aiming at reasoning factors that influence the activity recognition performance

The Shifting Sands of Labour: Changes in Shared Care Work with a Smart Home Health System

Whilst the use of smart home systems has shown promise in recent years supporting older people's activities at home, there is more evidence needed to understand how these systems impact the type and the amount of shared care in the home. It is important to understand care recipients and caregivers' labour is changed with the introduction of a smart home system to efficiently and effectively support an increasingly aging population with technology. Five older households (8 participants) were interviewed before, immediately after and three months after receiving a Smart Home Health System (SHHS). We provide an identification and documentation of critical incidents and barriers that increased inter-household care work and prevented the SHHS from being successfully accepted within homes. Findings are framed within the growing body of work on smart homes for health and care, and we provide implications for designing future systems for shared home care needs

Multi-Resident Human Behaviour Identification in Ambient Assisted Living Environments

Multimodal interactions in ambient assisted living environments require human behaviour to be recognized and monitored automatically. The complex nature of human behaviour makes it extremely difficult to infer and adapt to, especially in multi-resident environments. This proposed research aims to contribute to the multimodal interaction community by (i) providing publicly available, naturalistic, rich and annotated datasets for human behaviour modeling, (ii) introducing evaluation methods of several inference methods from a behaviour monitoring perspective, (iii) developing novel methods for recognizing individual behaviour in multi-resident smart environments without assuming any person identification, (iv) proposing methods for mitigating the scalability issues by using transfer, active, and semi-supervised learning techniques. The proposed studies will address both practical and methodological aspects of human behaviour recognition in smart interactive environments