One of the critical situations facing the society across the globe is the problem of elderly homecare services (EHS) due to the aggravation of the society coupled with diseases and limited social resources. This problem has been typically dealt with by manual assistance from caregivers and/or family members. The emerging Ambience Intelligence (AmI) technology suggests itself to be of great potential for EHS applications, owing to its strength in constructing a pervasive computing environment that is sensitive and responsive to the presence of human users. The key challenge of AmI implementation lies in context awareness, namely how to align with the specific decision making scenarios of particular EHS applications. This paper proposes a context-aware information model in a smart home to tackle the EHS problem. Mainly, rough set theory is applied to construct user activity models for recognizing various activities of daily living (ADLs) based on the sensor platform constructed in a smart home environment. Subsequently, issues of case comprehension and homecare services are also discussed. A case study in the smart home environment is presented. Initial findings from the case study suggest the importance of the research problem, as well as the feasibility and potential of the proposed framework.
Conflict resolution using enhanced label combination method for complex activity recognition in smart home environment
