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In ambient assisted living, one of the most concerning problems is how the living status of the elderly can be accurately judged through the data collected by ambient sensors. To solve this problem, environmental influences should be sufficiently considered. Some researchers have proposed collecting data comprehensively by distributed environmental sensors, but how the massive collected data were to be analyzed and transmitted was ignored. In this article, we propose a local data processing architecture on a local server to analyze collected data. In this three-layer architecture, the latest received data is stored in a data gathering layer. Afterward, a data filtering layer checks the efficiency of data. Also, this layer classifies the received data into static data reflecting sensors’ status and a realtime data stream reflecting quality of life. For static data, they are directly stored in a database, and the real-time data stream is divided into different levels. Based on these levels, a data analyzing layer reorganizes data into a neighborhood structure, which will be called RDAA. A risk factor is returned by RDAA and only abnormal data will be sent to a health care provider when its risk factor is larger than a given threshold. LDAP disperses the stress of remote centralized processing and data storage, which decreases the workload of the remote health care provider. Meanwhile, it also reduces network load and improves processing speed.
Wang et al. (Thu,) studied this question.