Title: Generic processing of real-time physiological data in the cloud
Authors: Kevin Lee; Kiel Gilleade
School of Science and Technology, Nottingham Trent University, Nottingham, UK
School of Natural Sciences and Psychology, Liverpool John Moores University, Liverpool, UK
Abstract: There is an emerging market in the collection of physiological data for analysis and presentation to end-users via web technologies for applications including health and fitness, telemedicine and self-tracking. As technology has improved, real-time streaming of physiological data, providing end-to-end user feedback has become feasible, allowing for innovative applications to be developed. Currently, there is no standardised method of collecting physiological data over the web for analysis and feedback to an end-user in real-time; existing platforms only support specific devices and application domains. This paper proposes a generic methodology and architecture for the collection, analysis and presentation of physiological data. It defines a standard method of encapsulating data from heterogeneous sensors, performing transformations on it and analysing it. The approach is evaluated through an implementation of the architecture using cloud computing technologies and an appropriate case study.
Keywords: physiological computing; cloud computing; physiology; sensors; frameworks; generic processing; physiological data; real-time streaming.
Int. J. of Big Data Intelligence, 2016 Vol.3, No.4, pp.215 - 227
Submission date: 23 Jun 2015
Date of acceptance: 29 Jan 2016
Available online: 21 Oct 2016