Data Reduction Techniques for Near Real-Time Decision Making in Fall Prediction Systems
Masoud Hemmatpour, Renato Ferrero, Filippo Gandino, Bartolomeo Montrucchio, Maurizio Rebaudengo · Advances in healthcare information systems and administration book series · 2017
Unintentional falls are a frequent cause of hospitalization that mostly increases health service costs due to injuries. Fall prediction systems strive to reduce injuries and provide fast help to the users. Typically, such systems collect data continuously at a high speed through a device directly attached to the user. Whereas such systems are implemented in devices with limited resources, data volume is significantly important. In this chapter, a real-time data analyzer and reducer is proposed in order to manage the data volume of fall prediction systems.