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Data Reduction Techniques for Near Real-Time Decision Making in Fall Prediction Systems

Data Reduction Techniques for Near Real-Time Decision Making in Fall Prediction Systems
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Author(s): Masoud Hemmatpour (Politecnico di Torino, Italy), Renato Ferrero (Politecnico di Torino, Italy), Filippo Gandino (Politecnico di Torino, Italy), Bartolomeo Montrucchio (Politecnico di Torino, Italy)and Maurizio Rebaudengo (Politecnico di Torino, Italy)
Copyright: 2018
Pages: 13
Source title: Big Data Management and the Internet of Things for Improved Health Systems
Source Author(s)/Editor(s): Brojo Kishore Mishra (C. V. Raman College of Engineering, India)and Raghvendra Kumar (LNCT Group of Colleges, India)
DOI: 10.4018/978-1-5225-5222-2.ch004

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Abstract

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.

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