LAUSR

Many countries have increasingly aging populations. Many of these elderly people live alone and independently, but those suffering from chronic diseases or disabilities are at risk of accidents that will require assistance. Many commercially available homecare systems provide remote monitoring functionality, but these systems require someone on the other end of the remote connection to be paying attention. This raises the need for intelligent home monitoring systems to ensure continuous awareness of user safety. This article proposes a system using beacon technology to assess subject well being based on lack of movement, and automatically activates prepositioned cameras and send an alert to family members or caregivers in response to potentially high-risk activity, such as activating particular kitchen appliances. However, the use of such cameras raises privacy concerns. To improve object readability and privacy, our research uses federated learning to enhance the readability of fast regions with convolutional neural networks features (RCNN). The presented approach stores the personal information of the elderly in the local server, which avoids revealing their home information and secures the data transmission safety by privacy protection. The article has proved the feasibility and practicality by running the experiment; the system can genuinely operate in home health care.