A ubiquitous warning system for asthma-inducement - IEEE Xplore

A ubiquitous warning system for asthma-inducement Hsueh-Ting Chu1*, Chir-Chang Huang1, Zhi-Hui Lian2 and Jeffrey J.P. Tsai3 Department of Biotechnology and Bioinformatics, Asia University, Wufong 413, Taiwan, 2 Graduate Institute of Environmental Medicine, China Medical University, Taichung 404, Taiwan, 3 Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA [email protected], [email protected], [email protected], [email protected] 1

Abstract In this paper, we introduce a medical application of the Global Positioning System (GPS). For an asthmatic patient, he or she can carry a portable GPS device to prevent possible morbidity of asthma during his or her outdoor activities of daily livings. To reduce possible allergy asthma, the GPS-enable device continuously consults a remote server with sending user’s position to decide whether current ambient air quality will threaten user’s health. The server of the detecting system collects real-time data from the network of national air quality monitoring stations. For the response to a remote query, the server makes decision of warning messages according to a proposed asthma neural network model. The proposed system is hopeful for asthmatic patients. Keywords: Asthma, Air pollution, Global Positioning System (GPS), Geographic Information Systems (GIS), Decision Support System (DSS), Smartphone, Environmental health, Neural network

1. Introduction As the rapid development of mobile communication, people can almost have expectations for ubiquitous network services in the near future. The progressive developments of wireless techniques, like ad hoc network, WiMAX and sensor network, push the birth of the fourth generation (4G) mobile communications. Thus different applications of mobile communication can be widely considered for the coming 4G era. Accordingly, we study the medical application of advanced mobile communication in this paper. For millions of asthma patients in the world, sudden allergic morbidity may cause severe threat of their lives. They have to take terbutaline in minutes to ease symptoms of rapid-onset asthma attacks, or the attacks may become fatal [1-2]. In these years, telemonitoring systems were developed to take care of the asthma patients at home [3-5]. An asthma telemonitoring

system provides assistance to self-management interventions of asthma patients. It helps early recognition of potentially dangerous situations of asthma exacerbations. Such a monitoring system is generally used indoors. We consider outdoor activities of asthma adults and children, in particular. According to a report of Canadian Public Health Association (http://www.cpha.ca/cleanair/), 15.2% of Canadian children between the ages of four and eleven have been diagnosed with asthma in 1988-1999, and air pollution makes asthma symptoms worse. Children are especially affected by air pollution because their lungs are not fully developed and they are likely to be active outdoors. Air pollution and weather change are believed to link to asthma [6]. For investigation of the link between asthma and air pollution, J. Maantay introduced assessment methods with Geographic Information Systems (GIS) to study on the spatial correspondence between asthma and air pollution [7]. We also analyze the dynamic link between air pollution, weather and asthma. On the one hand, we got time series data of air pollution from air monitoring stations of Environmental Protection Administration (EPA), Taiwan, and on the other, we got clinic data of asthma from Department of Heath (DOH), Taiwan. We selected regions of the middle Taiwan to research the effect of air pollution and weather on asthma exacerbations, and developed a neural network model of a decision support system for asthma-inducement by air pollution and weather. The asthma-inducement decision support system can provide still geographic information about bad air pollution regions for asthma patients. Moreover, we exploit the system to provide real-time information about air pollution. We design a ubiquitous warning system for outdoor activities of asthma patients. The idea is to provide early warning for the patients to avoid possible asthma triggers of air pollution and weather change. The patients can be notified when they reach a region with pernicious air. Our system collects real-time data of air pollution and weather from the

Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC’06) 0-7695-2553-9/06 $20.00 © 2006

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network of air quality monitoring stations of EPA. The system makes decision whether the air is pernicious for the position of an asthma patient. That is, the air may induce the morbidity of his asthma. The client device has to recognize its location before querying the server. We consider the usage of the Global Positioning System (GPS) for the purpose. There are many affordable GPS-enabled devices nowadays. In our system, an asthma patient can take a smart handheld device which integrates GPS and wireless Internet capabilities such as advanced smartphones or PDA phones. There are new affordable GPS smartphones, e.g. Mio A700, which can connect the Internet via either GPRS or WiFi. Consequently, such ubiquitous warning system is hopeful for asthma patients in the coming years.

2. The system architecture We design the ubiquitous warning system for asthma-inducement with GPS and wireless Internet capabilities. In Fig. 1, we consider a framework consisting of a server and remote client devices in the proposed ubiquitous warning system. We discuss the components in this section.

GPS satellites

XML Web Service

Fig. 2. The client device of the system. It

2.1. The client device In the client side, we need a location-aware device. In the literature, Borriello et al. discussed different ways and problems of location-aware devices for ubiquitous computing [8-9]. Currently, GPS-enable PDAs are the most familiar location-aware devices. Such a PDA is usually sold with a bundled routeplanning kit including a GPS module and a GPS mapping software to provide navigation service. We exploit such a GPS-enable device for providing warnings about air pollutions. We developed the client application with the Microsoft .NET Compact Framework (Fig. 2). There are two reasons for us to adopt .NET Compact Framework. One reason is that it provides an easy approach to consume XML Web services. The other is its ability to integrate GPS and other facilities like Bluetooth and voice recorder.

GPS mobile smartphone

asthma-inducement detecting server GSM stations

The Internet

Air pollution monitor stations (APMS)

Fig. 1. Framework of an asthma ubiquitous warning system.

Fig. 3. The XML Web Service for providing Asthma warning at http://asiauniv.org/AsthmaWarn/Service.asmx.


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2.2. The server architecture In Taiwan, the real-time information of air quality for asthma patients can be acquired from the two websites of Environmental Protection Administration and Central Weather Bureau: 1. 2.

http://www.cwb.gov.tw/V4/weather/ob/2005-obnoscript.htm http://210.69.101.63/emc/default.aspx?mod=Psi AreaHourly

Medical decision support systems (MDSS) are usually developed to support medical diagnosis of different diseases [16]. The multilayer perceptron

The function that is used to derive the output value of a node j in the network is as follows [17]: n

X j = ∑ xij ⋅ wij − θ j

(2)

Yj = σ (X j )

(3)

i =1

where n is the number of inputs to node j; wij is the weight of the connection between each node i and node j; xij is the input i for node j; and Yj is the output value for node j. The MLP used in this paper consists of three layers includes an input layer, a hidden layer and an output layer as shown in Fig. 2. In this section, we present the input and output data of our MLP. Input layer

Hidden layer

Input A1

Input Am Air pollution indices

Input B1

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asthma-inducement level

Input Bn

Fig. 4. The model of multilayer perceptron neural network for the decision support system of asthma-inducement.

3.1. Input and output data In this paper, we assume that the levels of air pollution and weather may affect the morbidity of asthma patients. That is, the quantity of asthma outpatients in a control region may increase or decrease when the weather indices and the air pollution indices also changes.


Output layer

Weather indices

…

3. A decision support system by multilayer perceptron neural network

1 + exp(− x)

…

Site 1 provides Taiwan’s current weather report and Site 2 depicts the monitoring of air pollution hourly. There are also many different ways between a mobile device and a server in the Internet [11-12]. In [12], Cruickshank et al. introduced the different choices of portal technology for context-aware ubiquitous systems. They used MUD to implement their system. MUD provides them the capability of natural language interaction between users. Our system is less complex than the MUD-based system. In our system, the communication between the client and the server consists of short messages for reporting user locations and warnings of air pollution. Therefore, the communication way in our system is implemented with XML web services [13]. XML web services are popular for GIS applications. Advanced GIS service providers, e.g. Microsoft MapPoint [14] and ESRI ArcGIS [15], have built different XML web services for the public. The mobile devices can also connect to the Internet and directly interact with web services with J2ME or .Net framework [16-17]. A XML web service is a remote procedure f(x) via SOAP and HTTP (Fig. 3). The client device simply call the procedure to query whether the air is pernicious for asthma patients with the input parameter: client coordinates. In the server side, the procedure includes two steps. The first step is to make decision which control regions the client is located at. Then a decision support system is called. The decision support system is composed of multilayer perceptron (MLP) neural networks written in Matlab. In different control regions, different parameters of the MLP model are used. Also different input data from different air monitoring stations are used as well.

(MLP) neural network is one of the popular soft computing methods applied to medical decision support systems. We use such neural network for the proposed asthma warning system. A multilayer perceptron is a network of perceptrons. A perceptron computes a single output from multiple real-valued inputs by forming a linear combination according to its input weights and then possibly putting the output through some activation function. Commonly the sigmoid function is used as the activation function for easy computation of backpropagation as follows: 1 (1) σ ( x) =

where A is the predicting value of trained neural network

Therefore, three weather indices and five air pollution indices, measured by the air monitoring station in the control region, are used as the inputs of our MLP models. The weather indices are RH (relative humidity), PS (pressure) and TX (temperature). The air pollution indices are CO, NOX, O3, SO2 and PM10 (suspended particulates Q3 then I = 1 (dangerous) ⎪ ⎨ A Q1 then I = 0 ( warnning ) ⎪ A