Research On Remote Nursing Of Elderly People Falling Detection

With the development and progress of society, especially social ageing and empty nest family are expanding swiftly; we recognize the importance of the people’s health care. But in the traditional care model, the user cannot be nursed in a real-time manner.With the ever-increasing computer technology, communications technology and other technologies, remote nursing has become a strong demand for medical units at all levels. The fall is one of the important factors of hazarding the elderly and other special human beings, particularly when one falls down in the ground with no help for a long time, which may lead to life-threatening. Promptly fall detection and rescue can gain precious time for treatment and salvage, and it is very important to secure health of people and improve quality of medical care.In order to offer better medical care for the elderly and reduce the harm of the accident fell down, falling among elderly people will be focused on in this article. The main work of this thesis is as follows:Firstly, since there is no authoritative data, data acquisition scheme is designed based on Android sensors, and acceleration sensor embedded in the Android system are used to collect the acceleration information of human body. The collected data is transferred to the Matlab to analysis and storage based on Java Message Services (JMS) queue middleware.Secondly, the fall and ADL patterns are defined, and the different pattern’s acceleration data, which are recorded in the laboratory-based experiments, is analyzed and compared. And based on the analysis result, the classification algorithm is extracted to distinguish fall and ADL, finally, the solution is verified by the experiment.Thirdly, a fall detection system based on Android phones is designed and implemented. The system has functions like collecting real-time sensor data, real-time monitoring the people falls or not by the fall detection model. If the person fall down, mobile phone with the him will alarm with buzzer sound locally, and obtains the location information of the people by localization module, and automatically edit the fall information and location information to create messages, and then sent it to the specified contact in order to get timely rescue, and finally transfer the personal data information to the Health Monitoring Platform for analysis and storage. The system we designed has the low cost and good expansibility, and also it can be transplanted easily and can be applied to the field of telemedicine. It will make the modern remote monitoring more humane, intelligent, and real-time.