Handling uncertainty in eHealth sensors using fuzzy system modeling
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ORIGINAL PAPER
Handling uncertainty in eHealth sensors using fuzzy system modeling Atrayee Gupta1
· Nandini Mukherjee1
Received: 18 May 2020 / Accepted: 21 July 2020 © IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In remote health multiple sensors are attached to a patient and collected data is transferred to the cloud, which is used by the physician for diagnosis. These sensors are prone to random errors. Challenge is to provide correct information since any inaccuracy in the information leads to the incorrect diagnosis of the patient. Any abnormal condition recorded by a single sensor can be cross-checked by matching it against another sensor recording different vital signs. The decisions of the doctors rely upon whatever the sensor is streaming currently, because matching against other vitals has to be done at real-time. Since fuzzy-based system works with the imprecise dataset and merges them in ranges, it favours the decision of a medical practitioner in remote health. Therefore, in this paper, we discuss how to reduce uncertainty from the remote health sensors using fuzzy modelling system. We discuss some use cases to simulate remote health scenario with fuzzy inferencing system and obtain acceptable output in presence of random errors. We also compare our proposed model with basic (statistical) and advanced (context-aware) models to show its performance exceeds the other two. First, the statistical model needs more data set than our proposed model and second context-aware model may not correctly detect random error from the current context. Keywords Smart health · Virtual sensors · Random errors · Fuzzy systems · eHealth
1 Introduction Modern sensor cloud technology uses virtual sensors on top of physical sensor infrastructure to facilitate shared use of remote sensors and to manage heterogeneity. In a remote health-care scenario, use of virtual sensors (VSs) [34], [6] has been proposed to incorporate an abstraction of remotely located health sensors in the cloud environment. Here, eHealth physical sensors (PS) are attached to the patient’s body. When a remote physician asks for vitals, such as blood pressure of a patient, physical sensors are acquired by the applications for collection of clinical data. Exact replica of the set of sensors, for example, Singular(Systolic) and Singular(Diastolic) [16] are made available to the physician Atrayee Gupta is the co-author of this paper Atrayee Gupta
[email protected] Nandini Mukherjee [email protected] 1
Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
through cloud. This not only eases the medical examination of the remotely located patients who are several kilometres away from the urban clinic, but also extends support for monitoring from the physician’s end. Considering the diversified applications of virtual sensing, it can become a supportive technology in modern medical field. However, errors in data generated from erroneous physical sensors also gets transmitted to vi
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