People with disabilities sometimes have considerable difficulties, or even physical incapacities, performing daily tasks independently. Many research works have introduced home automation as a useful way to overcome these activity limitations. However, very few of these accomplishments have focused on the design of intelligent systems which would allow nonexperts to model and to adapt a home automation environment for the disabled. This design work is currently restricted to technicians, rather than occupational therapists or others who are able to best understand the needs of those with mobility or cognitive impairments. To take up these challenges, this paper proposes a design flow including a component approach for modeling system architecture and a range of services to meet the needs of both developers and users. Based on model driven engineering, it automates the control code generation for home automation systems.
Automation of smart home for ambient assisted living is currently based on a widespread use of sensors. As efficient as it seems to be, this solution can sometimes be problematic when one focus on user acceptability intimately related to cost and intrusivity. In this paper, we propose a context-aware system based on the semantic analysis of each user request at runtime. Our goal is to infer user data usually sensored by using advanced semantic web tools to adapt home automation services to people with special needs. To take up this challenge, an ontology, automatically derived from a model-driven process, firstly defines usersystem interactions. Then, the use of rules allows an inference engine to deduce user location and intention leading to adapted service delivery.
Automation of smart home for ambient assisted living is currently based on a widespread use of sensors. In this paper, we propose a monitoring system based on the semantic analysis of home automation logs (user requests). Our goal is to replace as many sensors as possible by using advanced tools to infer information usually sensored. To take up this challenge, an ontology, automatically derived from a model-driven process, firstly defines user-system interactions. Then, the use of rules allows an inference engine to deduce user location and intention leading to adapted service delivery.
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