Context-Aware Elderly People Activity Recognition Using Dempster-Shafer Theory In Iot Environment
Presently, from the population's elderly people, the average is gradually increasing, so taking care of those people smart home plays a crucial role. To ensure safety, maintain their self-sufficiency, and healthy. In this paper, the smart home is used to increase the quality of the user's life. This paper provides a framework for implementing Dempster-Shafer's theory on the basis of context-aware rules that enhance the overall accuracy of Monitoring and recognition of activities by elderly people. A system, an Ambient Assistant Living (AAL), is used for elderly people's behavior and ability to understanding their context from the data, which is derived through sensor networks. The knowledge is inferred by the Dempster-Shafer Theory (DST) approach is used to remove uncertainty from collected data and used the Context-Aware Multi Layer Architecture (CA-MLA). AAL system is used to provide support for elderly people to be insecure and independent life by the novel system, detect the elderly people's health conditions, and predict the inaccurate behaviors in their activities. So we have to send alerts to caregivers or family members. To this end, the efficiency of the proposed experimental results is performed by the smart home.