With the popularity of Internet of Things, lots of resource constrained devices equipped with sensors and actuators are pervasively deployed to compose a smart environment, and Big Data are obtainable for a system to ...
详细信息
ISBN:
(纸本)9781479986965
With the popularity of Internet of Things, lots of resource constrained devices equipped with sensors and actuators are pervasively deployed to compose a smart environment, and Big Data are obtainable for a system to do further analytics thus to achieve human-centric purposes. One such human-centric system is a smart home which analyze Big Data to recognize contexts and their corresponding preferences for service configuration thus to provide context-aware services. However, since these Big Data are generated in real-time with huge amount, analytics based on conventional supervised way is not desirable due to the requirement of human efforts. In addition, there are usually multiple inhabitants with multiple combination of contexts in a home environment, and it is difficult to fully collect all these possible context combination as well as their corresponding preferences in advance. Therefore, this paper proposes an unsupervised nonparametric analytics method with a framework for human-centric smart homes to automatically discover contexts and their corresponding service configurations, and the models resulting from the proposed analytics can also be used to determine the preference for a context combination unseen before.
The high development of medicine causes the world's population aging quickly. To resolve the problem with limited medical resources, constant monitoring of elders' activity of daily living is important. We pro...
详细信息
ISBN:
(纸本)9781479986965
The high development of medicine causes the world's population aging quickly. To resolve the problem with limited medical resources, constant monitoring of elders' activity of daily living is important. We propose an activity recognition system for smart home, so elders can live alone and their children can monitor their parents' living activity to achieve the concept of "Aging in Place". The living activity monitoring model is powerful to recognize meaningful activities by using both ambient and wearable sensors. It's feasible to deploy in the real living environment because it's a non-parametric learning model. Elders need less effort to label activity in training part, and the model may have chance to find some special activities that the elders did not consider in the past. We demonstrate the living activity monitoring model is feasible to be deployed in a living home with high accuracy performance of the activity recognition result.
暂无评论