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.

Data Reduction Techniques for Near Real-Time Decision Making in Fall Prediction Systems / Hemmatpour, Masoud; Ferrero, Renato; Gandino, Filippo; Montrucchio, Bartolomeo; Rebaudengo, Maurizio - In: Exploration of Healthcare Using Data Mining Techniques / Desarkar A., Das A.. - STAMPA. - [s.l] : IGI GLOBAL, 2018. - ISBN 1522552227. - pp. 52-64 [10.4018/978-1-5225-5222-2.ch004]

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

Masoud Hemmatpour;Renato Ferrero;Filippo Gandino;Bartolomeo Montrucchio;Maurizio Rebaudengo
2018

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.
1522552227
Exploration of Healthcare Using Data Mining Techniques
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2697935
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