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2017 | OriginalPaper | Buchkapitel

Smartphone Based Real-Time Health Monitoring and Intervention

verfasst von : Daniel Aranki, Gregorij Kurillo, Ruzena Bajcsy

Erschienen in: Handbook of Large-Scale Distributed Computing in Smart Healthcare

Verlag: Springer International Publishing

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Abstract

Smartphones are often dubbed as “a doctor in your pocket” as they have in recent years become one of the most notable platforms for health management and monitoring. In this chapter we discuss the potentials for real-time health monitoring of chronic health conditions and data-driven intervention that aim to improve patient care at a lower cost. We outline several challenges that developers, patients, and providers face with respect to this new technology. We then review several commercial platforms for health monitoring and discuss their pros and cons. Furthermore, we present Berkeley Telemonitoring Framework, a recently developed Andorid-based open source solution for development of health-monitoring applications with security and privacy in mind. In particular, our framework offers an easy-to-use API for building client apps, deploying data-hosting servers, fault-tolerant data retrieval and storage, access to event-based Bluetooth and BLE stacks with standards for personal health devices, access to phone sensors, implementation of several vital signs estimators, gait analysis, etc. We demonstrate the use of the framework on an example fitness application MarathonCoach. We further discuss several challenges facing real-time telemonitoring. In particular, we focus on privacy and propose a novel information-theoretic framework called Private Disclosure of Information (PDI). The PDI framework formalizes a scheme for encoding the collected health data in a manner that minimizes the ability of an adversary from gaining knowledge about the patient’s diagnosis (or other information private by implication) through statistical inference, while allowing the authorized provider to use this information with no loss in utility.

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Fußnoten
3
Samsung renamed “S Health” to “Samsung Health” in April 2017.
 
17
We say that \(g : D_2 \rightarrow D_1\) is a left inverse of a function \(f : D_1 \rightarrow D_2\) if for all \(x \in D_1\) we have \(g\left( f\left( x\right) \right) = x\).
 
18
BMI is a measure of relative weight based on an individual’s mass and height. Defined as \(BMI \triangleq \frac{mass(kg)}{height(m)^2}\).
 
21
The adopted total accuracy measure is trace(M) / N where M is the confusion matrix and N is the cardinality of the test set. This is the percentage of true classifications over the test set.
 
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Metadaten
Titel
Smartphone Based Real-Time Health Monitoring and Intervention
verfasst von
Daniel Aranki
Gregorij Kurillo
Ruzena Bajcsy
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-58280-1_18