A healthcare real-time monitoring system formultiple sensors data collection and correlation

Luigi Romano, Luigi Coppolino, Ivano Alessandro Elia, and Gianluigi Spagnuolo

Abstract Telecare and home healthcare services are an increasing healthcare re-search sector. In this field a novel approach is based on wearable sensor deviceswhich provide a user-friendly acquisition of vital signs and allow the implemen-tation of pervasive and continuous healthcare monitoring systems. The amazingamount of data continuously provided by sensors, poses challenging issues to thesystems which are in charge of their collection and processing.In this paper we present a platform allowing real-time monitoring of biomedicaland environmental parameters able to collect, store and process data gathered froma wide variety of sources. The platform presents a modular architecture that easilyallows its extension with additional sensors. The main feature of the proposed sys-tem is the adoption of a complex event processor to correlate and manage eventsextracted from the collected data. This allows a wider and more accurate knowledgeof the patient’s health status. A proof-of-concept implementation of the proposedplatform has been realized and its implementation is detailed in this paper.1

Luigi Romano, Luigi Coppolino, Ivano Alessandro EliaDipartimento per le Tecnologie(DiT), Universita degli Studi di Napoli “Parthenope”, CentroDirezionale di Napoli, Isola C4, 80143 Napoli, Italye-mail: luigi.romano,luigi.coppolino,ivano.elia@uniparthenope.it

Luigi Romano, Luigi Coppolino, Gianluigi SpagnuoloCINI - ITeM Laboratory, Napoli, Italy 80126e-mail: gianluigi.spagnuolo@gmail.com

Luigi RomanoInstitute for High Performance Computing and Networking (ICAR) of the Italian NationalResearch Council (CNR), Italy

1 The research leading to these results has received funding from the the Regione Campaniawithin the framework of the PHDS Project. Authors wish to thank the technical staff ofall partners participating in the PHDS project (namely: Synclab, Intesis , HAL Softwaresrl, CINI, and ICAR): their contribution was key for properly steering the direction of theresearch activity, as well as for improving the quality of this paper.

E. Damiani et al. (Eds.): New Direct. in Intel. Interactive Multimedia Sys., SCI 226, pp. 453–462.springerlink.com c© Springer-Verlag Berlin Heidelberg 2009

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1 Introduction

Telemedicine and Telecare use advanced telecommunication technologies to ex-change health information and provide healthcare services across geographic, time,social and cultural barriers.[7] [8]The healthcare industry is facing various challenges that include increasing costs,the incidence of medical errors and the lack of qualified medical workers. For thesereasons there is the need to provide a better quality of service using limited finan-cial and human resources. The use of Telemedicine technologies and in particular of”Pervasive Healthcare” is considered a promising opportunity to address this kindof issues. Pervasive Healthcare or ”healthcare to anyone, anytime, and anywhere”is an approach that can remove locational, time and other restraints and in this wayincrease both the coverage and the quality of the healthcare services and so helpreduce costs.Many type of powerful medical applications can be developed in the field of per-vasive healthcare: miniaturized biological sensors can be connected though increas-ingly widespread Wireless LANs and personal area networks, along with a widerange of different environmental sensors, and data gathered can be combined withthe help of middleware for a wide range of possible medical purpose.[9]Despite these opportunities there are still challenges to the wide acceptance and dif-fusion of these technologies. One of the main limit for this kind of applications is thelack of standardization resulting in the impossibility of interoperation for applica-tions developed by different producers. Another problem is related with middlewarecapability of handling the possibly huge amount of data gathered from the sensorsand the addition of new sensors to the systems.[7] [6]

This paper presents a proposal for an extensible medical application platform forreal-time monitoring and correlation of biomedical and environmental parameters.The remainder of the paper is organized as follows. Sect. 2 is an overview of moni-toring systems for telemedicine, along with description of related works and of thetools used in our platform. In Sect. 3 the platform’s architecture is presented. Thesection focuses on the data correlation capability and on the extensibility of the pro-posed platform. In Sect. 4 details are given about the actual implementation of aprototype.

2 Background and related work

Monitoring systems for telemedicine. Early signs of disease can only be detectedby hospital examinations but it’s difficult to perform screening tests very frequently.Continuous monitoring of patient’s health status is a very powerful diagnostic toolfor early detection of diseases. An approach like this can allow immediate inter-vention, before the appearance of subjective symptoms, which is a key feature todecrease related costs.

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This kind of monitoring is very suitable for the assistance of elderly patients athome, and this is particularly interesting in advanced countries where the incidenceof elderly people living alone is increasing.Another important filed of application is for the monitoring of patients in postoper-ative care, in chronic condition or that undergo supervised recovery [9].As an example of existing implementations of this kind of technologies we herebriefly point at two monitoring systems for telemedicine:

• In [10] is presented a system architecture proposal named Ubimon, for an ubiq-uitous monitoring environment for wearable and implantable sensors based onbody sensor network (BSN). It addresses issues related to the use of wearable orimplantable sensors for distributed mobile monitoring. A prototype was devel-oped for patients with ischaemic and arrhythmic heart disease.

• In [11] is presented an open platform for the remote monitoring of patients devel-oped by IBM named Persona Care Connect (PCC). It aims at giving caregiverstimely access to a patient’s health status, and the possibility to provide patientswith appropriate preventive interventions, helping to avoid hospitalization and toimprove the patient’s quality of care and quality of life. The platform containscore functions needed for the collection of biomedical sensor data and stores thedata on a server for long-term persistence and analysis. The system is focused onthe architecture’s integration, extensibility and interoperability issues and a keygoal is the use of standards and open system APIs at as many points as possible.

In both of the former systems the aim is to provide highly trained medical staffwith a intuitive and context-aware visualization of the collected data, but no mean isprovided for configuration and automatic detection of complex patterns of parame-ters. No complex detection and reaction is possible without the presence of a humanoperator visualizing the data.

Wearable sensors. Remote and continuous health monitoring systems require sen-sors easy to use and provided with comfortable sensing interfaces. Measurementshave to be carried on in a ”natural” environment and while the patient performs hisdaily activities without interfering with them. A class of sensors that fits for thispurpose is that of wearable sensors that are non-invasive devices for the monitoringof biological parameters. Wearable sensors are either supported directly on the hu-man body or a piece of clothing, and are designed to be worn in comfort enablingprolonged use [12].

In our prototype a wearable health monitoring system named WEALTHY is used.It is based on a textile wearable interface implemented by integrating sensors, elec-trodes, and connections in fabric form provided with signal processing features, andtelecommunication systems. WEALTHY is able to acquire simultaneously severalbiomedical signals: respiration, electrocardiogram, activity, temperature and oxy-gen saturation. Sensors, electrodes and connections are realized with conductiveand piezoresistive yarns. The main functions of the wearable modules are sensing,

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signal conditioning, signal processing, and data transmission. The sensing moduleconsists of a patented interface connected with a device called the Portable PatientUnit (PPU), where local processing as well as communication with the network isperformed. A miniaturized shortrange wireless system is embedded in and can beused to transfer signals to a PC, PDA, or mobile phone. [5]

Complex-Event-Processing. Complex-Event-Processing is a powerful computa-tional paradigm which ideally suits for real time processing of continuous datastreams to enable applications to monitor multiple streams of event data. In Com-plex Event Processors (CEP) sensory information is mapped as event data, or eventattributes, and encapsulated in messages that travel over a digital nervous system,constituting an electronic messaging infrastructure. This technology allows the ex-traction of an higher level knowledge from situational information by means of cor-relation of events from multiple data stream sources.CEP are based on continuous queries —i.e. queries that are evaluated continuouslyas data streams continue to arrive— that are performed on the data flows and trig-ger reactions to the matching of specific complex event patterns. At the core of acomplex event processing system there is a complex event processor, that is the in-terpreter of an event-driven processing language which allows one to describe thecomplex event to be analyzed as a space-temporal relationship among elementaryevents.

3 System design

This section provides an overview of the proposed platform. We focused our ef-forts on two themes: extensibility and data correlation. Our system can continuouslymonitor a wide variety of sensors, perform a cross-device correlation between datacollected and react in a proper way when significant changes in functional healthstatus are observed. The considerable amount of data collected by the sensors willallow quantitative analysis of various conditions and patterns.

The presented system, as shown in Fig.1, includes seven components deployedacross two main elements: Home Station and Event Server.

3.1 Home Station

In addition to collect all the sensor data, the Home Station sends the normalizeddata to the Event Server via Internet. It is also designed to detect some configurableevents and act accordingly. Home Station is structured in three layers: data acquisi-tion, data normalization and local data processing

Data Acquisition. The Data Acquisition layer presents a modular architecture thateasily allows its extension with additional sensors. Every sensor data stream format

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Fig. 1: System’s Architecture.

is handled through an adapter that provides features for data parsing. In this wayevery kind of additional sensor can be easily plugged in the system only generatinga new adapter with the specific data parsing capability. This sensor adapters are thenconnected to the Data Acquisition layer through a plug-in framework. They are spe-cific to each type of sensor device and gather incoming data stream; each adapterimplements proper methods to acquire and parse measured data.The collected data are parsed and stored in a common, consistent format, indepen-dent from the sensor type. This layer also handle the initialization, configuration,and synchronization of the sensor devices.

Data Normalization and communication. The foremost task of the Home Stationcommunication system is to transmit reliable medical data through a secure networkfrom the Home Station device to the remote server and to handle replies from themedical staff to the user.The health monitoring devices produces an amazing amount of network traffic de-pending on the number and frequency of vital signs monitored. The network per-formance can be improved by sending minimal real-time data, such as differentialsignals or changes since the last transmission [6]. The traffic generated can be fi-nally compressed.The delivery of medical information could be negatively affected by the failure ofthe network [4]. To address this issue a network monitor has been implementedto monitors the state of the network connection between Home Station and EventServer. The impact of network failures can be minimized by caching data for latertransmission.

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Local Data Processing. The core of the Home Station is the Local Data Processorthat presents a minimal event handler based on a configurable monitoring systemare designed. For each monitored vital sign multiple thresholds and a set of actions[6]; if the received measurement are out of range an emergency alert can be notified.For this purpose this layer provide an audio and graphical user interface that can beused for the first medical aid.

3.2 Event Server

Our challenge was to implement an Alarms and Event Server that correlate andmanage events extracted from the collected data. In this way if there is a significantcompromise in the patient’s vital signs, and event is raised an alarm is sent to thepatient’s medical staff.Our system is structured in four layers: data collection, data processing, alert sub-system and user interface. A detailed schema for the Event Server is shown in Fig.2.

Fig. 2: Event Server Schema.

Data Collection This layer provides a service to collect data in real-time from in-dividual patients. Apart from receiving real-time data from the Home Station via

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Internet this layer also store this data in a database which is used by the User Inter-face for the data visualization and management, and the Complex Event Processorto perform a long-term analysis and detect undesirable patterns. Patient database isa valuable resource to predict potential risk factors through various data correlationtechniques.

Data Processing In addition to the Local Data Processing this layer extends thenumber of detectable events and actions. It also produces a log file used by the AlertSub-system to perform a interrelationship between multiple vital signs.

Alert Sub-system In addition to simple events monitor, a complex event proces-sor adds the capability to manage and correlate data from the log file and patientdatabase to perform advanced short-term and long-term analysis. In this way eventsfrom multiple sources can be correlated to identify problems that previously re-quired operator analysis. To handle outputs received from the CEP, a configurableevent handling subsystem is provided. A consumer must be able to receive eventfrom the Event Handler service.In order to receive events, a client application (consumer) can subscribe for one ormore event notification. Users can customize their preferences for receiving eventnotification messages through a web based user interface.A message priority based mechanism is used to control the transmission of emer-gency alert messages onto the network; this feature optimizes bandwidth utilizationwhile minimizing message delay.

User Interface In [3] factors to consider when providing health information areidentified as: consistency of health-monitoring information; convenient user inter-face; universal access and universal design for the disabled and elder persons; aunified health-monitoring Web server for wired Internet and mobile Internet; health-information center accessibility for the doctor nurse, and the disabled and elderly;provision for different formats of health information .User Interface is provided by a web application that format the patient data accord-ing to user profile. Views for doctors, patients and system administrators are offered;the web application dynamically produces pages according who accesses a patientdata. For example a doctor will see all the patient information and may alter theconfiguration files, while a patient will see a limited set of data.All the collected data, and events, are available in an externally accessible format toallow interoperability and extensibility with other systems. For example, our plat-form can be seamlessly integrated with an automated prescription process and withthe patient electronic medical records. In this way the system has the opportunity toimmediately change prescriptions accordingly to the current health status of the pa-tient. Then a message is generated and sent to the Home Station in order to show theupdated prescriptions and remind the patients when they need to take medication.

Extensions and modification can be performed easily and with no modificationto the core of the system: the addition of a new sensor only implies the generation

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of a new adapter and an upgrade to the event detection logic is simply performedtrough the creation of a new query to be deployed on the CEP.

4 System implementation

Fig. 3: A screenshot of the platform’s user interface.

This section presents the implementation of the proposed system, with particularemphasis on the data correlation. The prototype of the proposed system is imple-mented using the Ruby language [2] on a Linux box. We chose Ruby because of itsgreater portability and extensibility.Preliminary evaluation of the prototype is carried out using WEALTHY [5], a wear-able system discussed in Sect. 1. The WEALTHY sensor data are gathered by theHome Station through a bluetooth connection. The specific plug-in has been imple-mented using BlueZ, the official Linux Bluetooth protocol stack, to acquire data viaBluetooth while the PPU packets parser has been written from scratch.A minimal Local Data Processing use a configuration file that contains an entry foreach measured parameter. Each entry is composed by a pre-configured range and alocal action; this action is accomplished if a measured value is out of range. Cur-rently, the action mechanism is limited to transmission of an alarm message deliv-ered to the patient in three different ways: via SMS, via email or through the Home

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Station GUI. As future work, we intend to add a more flexible and fine-grainedframework to define events and related actions; a domain specific language is alsoplanned.In the Alert Sub-system, to correlate and manage events extracted from the collecteddata we have used Borealis [1], a distributed stream processing engine developed atBrandeis University, Brown University, and MIT.A sample detection that could be performed using the correlation capability of theCEP is the detection of a Tachycardia, in this case the pulse rate exceeds the 110beats per minute while the oxygen saturation happens to be lower then 89%. Asshown in fig.4 the detection of this event can be performed with a query performedon the data streams on the CEP. The two filter blocks select only the anomalousvalues of oxygen saturation and pulse rate from the corresponding streams and then,in the join block, streams are correlated on the base of their timestamps, when forboth parameters, anomalous values are measured within the same time window,alerts are generated on an output stream.

Fig. 4: An example of event detection performed trough the use of CEP.

The prototype user interface is built upon the Ruby on Rails [13] web appli-cation framework on Apache/Passenger server. Some features are developed usingJavaScript and Adobe Flex technologies. In Fig.3 is shown a monitoring page duringa fall detection.

Conclusion

In this paper, we have described a platform to allow real-time monitoring of biomed-ical and environmental parameters to perform a cross-device correlation and havean accurate knowledge of the patient’s health status. We focused our efforts on ex-tensibility and data correlation in order to provide an highly configurable platform.

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A prototype of the platform has been implemented using Borealis to correlate datacollected from a wearable system.

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