Development of wireless Body Area Network based on J2ME for M-Health applications

Transcription

1 Development of wireless Body Area Network based on J2ME for M-Health applications M. J. MORÓN*, J. R. LUQUE*, A. A. BOTELLA*, E. J. CUBEROS*, E. GALLARDO*, E. CASILARI*, A. DÍAZ-ESTRELLA*, J. A. GÁZQUEZ** *Dpto. Tecnología Electrónica-University of **Dpto. Arquitectura de Computadores y Malaga, ETSIT, Campus de Teatinos, Electrónica-University of Almería SPAIN Abstract: - This paper presents a prototype of a medical Bluetooth Body Area Network (BAN). The central node in the BAN was developed using Smartphones (mobile phones offering advanced capabilities) and Java 2 Micro Edition (J2ME). The utilization of smartphones can take advantage of the user familiarity with cellular devices. The J2ME implementation for monitoring applications is also advantageous since its portability is greater than any application developed with another programming language (e.g.: C++) over Symbian. In the architecture, a Java midlet in the smartphone receives information about patient s location and health status. The midlet encrypts and transmits the data to a server through or GPRS/UMTS. In case of detecting a medical alert, the midlet sends a MMS and a SMS. Additionally, the system enables the remote configuration of the BAN from a PC or even a smartphone. In order to study the scalability of this type of telemedicine networks a software for sensor emulation has been incorporated to the architecture. The emulator is prepared to retrieve and transmit realistic signals from a medical Internet data base. Key-Words: - m-health, Telemedicine, Body Area networks, Smartphones, Biosensors, J2ME 1 Introduction The increase in the processing and integration capacity of electronic devices, as well as the advances of low power wireless communications and, in general, wireless networking has enabled the development of unwired intelligent sensors for a wide set of applications. One of the most promising application fields is the medical telemonitoring of patients. Even a new concept: M-Health (or Mobile Health) has been proposed for the integration in a sanitary system of both mobile technologies and wireless sensors [1]. The generic goal of an M-health system is to increase the patient mobility and allow the sanitary agents to access the medical information seamlessly and with independence of the physical location of both (patient and sanitary staff). The incorporation of mobile technologies to the medical care has diverse benefits: mobile solutions have been shown to improve patient safety, decrease the risk of medical errors and increase physician productivity and efficiency. Similarly, wireless sensors enable the patients freedom of movements and promote new ways of patient monitoring such as home monitoring [2]. This clearly improves patients quality of life. Missed days of school or work are reduced and health related restrictions on normal daily activities are minimized. Moreover, it allows the service continuity of the healthcare attention, as it defines a constant link to medical professionals who are able to assist in the disease management process. Patient telemonitoring facilitates the extension of healthcare services to remote and sparsepopulated areas avoiding the need of expensive medical premises. On the other hand, providing physicians and clinicians wireless access to patient information and medical references largely eliminates the need to locate and read these data through patient charts or search for lab results from other departments. Telemonitoring also increments the medical presence in emergency scenarios and makes possible a remote diagnosis. As a consequence of the advances in wireless technologies, the architecture of a classical telemedicine service has strongly evolved [3]. Initial telemetry systems just contemplated the simple retransmission of the biosignals captured by wired sensors via a POTS modem or ISDN. Today new networking technologies as well as new communication paradigms (such as Context Aware or Always Best Connected ) enable new possibilities in telemonitoring services, ranging from limited indoor scenarios (e.g.: a care unit) to outdoor applications without any mobility restrictions. Present medical monitoring systems are based on W- PANs (Wireless Personal Area Networks) or W- BANs (Wireless Body Area Networks). The PAN (or BAN) must integrate a set of wearable wireless devices capable of sensing and transmitting biosignals. In most cases, the PAN/BAN is ISSN: ISBN:

2 coordinated by a node which in turn may retransmit the signals to a remote central monitoring unit [4]. This article presents an architecture that defines a monitoring network of Bluetooth biosensors connected to a commercial 3G cell phone with a WLAN interface. The main goal of this architecture is to evaluate in the future the performance of smartphones when used as the gateway/master in a PAN of Bluetooth sensors. The evaluation especially will take into consideration the limitations of the extended J2ME (Java Mobile Edition) as a designing tool for this type of monitoring applications. This paper is structured as follows: Section 2 summarizes related works. Sections 3 and 4 describe the general structure of the prototype and its implementation, respectively. Section 5 presents a biosignal emulator that is being incorporated to the architecture. Section 6 comments the conclusions. 2 Research on medical wireless PANs The first projects that introduced the concept of Personal Area Networks employed proprietary wireless transmissions systems: The work in [5] described a PAN that integrated the information generated by different intelligent sensors. The wireless interface of the PAN utilized a 916 MHZ RF transceptor which provided a bi-directional bit-rate of 33.6 Kbps in a range of 50 m. Similarly, in [6] authors develop a PAN which interconnected lowpower sensors to PDAs and PCs by means a radio transceptor that operates at 916 or 433 MHz with a bit rate of 76.8 kbps and a coverage radio of m. In spite of these initial works, the present tendency in the field of medical PANs (and telemedicine in general) is the utilization of standards for the different wireless communications that the PAN requires. The use of standards notably reduces the development cost while easing the deployment of telemedicine systems and product interoperability [7]. In case of requiring to reduce as much as possible the sensor consumption (a basic aspect when dealing with implanted sensors whose batteries cannot be easily replaced), Zigbee/ based transmission may be the best choice [8]. However, when battery restrictions are not so exigent, Bluetooth (BT) is by far the most utilized technology by designers to interconnect PAN sensors with the monitoring system unit (and/or signal gateway). Among the main reasons that make this popular standard an attractive candidate to monitor physiological parameters dynamically, we can mention the small size, reduced cost and low power consumption of the BT radio modules, the BT technique of frequency hopping (which increases security and privacy in radio transmissions) [4], the capability of BT to conform scatternets and ad hoc networks, the potentiality of BT for wearable systems [7] [9], and specially the present penetration of BT in the market (and its related commercial support). Additionally the bandwidth supported by BT (up to 1 Mb/s) is enough to convey and multiplex any biosignal. Diverse prototypes of BT sensors have been developed for different biosignals, including glucometers, tensiometers, electrocardiograms (ECG), pulse-oximeters and even stethoscopes. In fact, during last three years several vendors have launched numerous homologated wireless biosensors with BT interfaces which can be easily integrated in a BAN. These commercial terminals permit a straightforward integration of general purpose devices (PDAs, embedded PCs, Mobile Phones, etc) in the design of the PAN/BAN networks. So, the system can benefit from the computing power of these devices just by simply programming Bluetooth communications through conventional programming libraries (e.g. BlueZ). This increases the versatility and reconfigurability of the network, reducing its deployment time and its final cost. Different examples of networking architectures have been proposed in the literature to solve the problem of medical telemonitoring. The AMON Project [10] implemented a portable device to be worn in the wrist, capable of measuring several biosignals simultaneously. The developed equipment processes the signals and, in case of medical alert, communicated with the medical center by means of the cellular network (sending a SMS or with a GSM connection). However, as it is difficult to integrate multiple biosensors in a single device, today most medical telemetry systems include some kind of wearable piconet of independent wireless sensors. In most cases, the architectures consist of a (wearable or not) multiplexing PAN node which collects the biosignals from one or more BT (or wired) biosensors. Once the signals are received, the node directly shows them on an embedded display or (more commonly) it retransmits them to a central node (normally located in medical premises) by means of a WLAN or cellular transmission technology. For example, in [11] an ECG signal is transmitted via BT to a PALM device and a smartphone, respectively. These devices in turn forward the signal to a server through GPRS. A similar system for ECG monitoring, presented in [12], adds to the GPRS transmitted flow the compressed information from a GPS. Analogously, the architecture in [4] allows a wearable unit (PDA or cell phone) to receive via BT the signals from a ECG but also from a pulse monitor and a tensiometer. In ISSN: ISBN:

3 [13] a pulse-oximeter and a tensiometer are integrated in the same device which transmits the signals to a mobile phone via BT. A J2ME application in the phone processes the signals and detected medical alarms (sending a a SMS to the medical staff). If the mobile service is not operative, the sensor (which can be remotely configured) is able to store the signals temporally until GPRS service is recovered. Authors in [14] developed a piconet of BT smart sensors. The sensors can be programmed by a control node which is remotely accessed by GPRS/UMTS. The same authors presented in [15] another piconet of wearable sensors. The control node is situated in a cell phone or PDA with WiFi, GPRS and UMTS interfaces. A similar system is described in [16]. In this case, the piconet is substituted by a set of wired sensors connected to a microprocessor board specifically designed for this application. The board also includes a BT module to intercommunicate with the hospital through a GPRS mobile phone. In the reception part medical staff can receive the information of the patients in portable PDAs. In contrast with previous works (in which BT communications are programmed at HCI or L2CAP layers), the main novelty of the BT piconet described in [17] is that, in order to ease the interoperability among different vendors, BT sensors are accessed by using Bluetooth Serial Port Profile. The work in [18] introduces a PAN architecture comprising a set of plug and play BT sensors controlled by a wearable data logger. By using BT the logger connects to an Internet-attached base station which stores the signals in a distributed data base. The interoperability of the sensors is achieved by means of the ISO/IEEE (Medical Information Bus) standard. Authors in [19] suggest that the main goal of a telemonitoring system is to transmit eventual medical alarms. As these systems are aimed at patients and elder people who spend most time at their homes, the work proposes the use of not-wearable (fixed) BT access points that will be responsible for collecting the signals and transmitting them to a central unit (a PDA) in a hospital. From these experiences, we can remark the growing use of general purpose mobile devices (mainly PDAs or Cell Phones) to build the PAN node which acts as the gateway between the wireless sensors and the final remote control node. This can be justified by the increasing computing power, storing and communicating capabilities (BT is included in many models) as well as by the decreasing costs of these devices. Moreover, due to the massive use of cell phones in developed countries, the implementation of the medical BAN central node can be performed without introducing any new wearable hardware and just with a reconfiguration of a familiar and quotidian element in the everyday life of the patients (the mobile phone). As it refers to the programming language that is normally chosen to build the software in the phones (or PDAs), initial architectures utilized C or C++, which permitted an optimized design for real time processing and a better interaction with the underlying operative system (e.g.: Symbian in the case of most phones). However, since the apparition of J2ME (Java version for mobile devices) and in spite of its limitations (for example, with the APIs to incorporate BT), the portability of Java is notably stimulating its adoption in the field of medical BANs. 3 System description The developed system (sketched in figure 1) consists of three components: 1) An iban (intelligent BAN) worn by the patient to be remotely monitored. 2) A Central Control Server (CCS). 3) A set of remote monitoring units. These units could be located in Internet connected PCs or in handheld devices acting as Mobile Control and Monitoring Units (MCMU) carried by the physicians. The iban prototype is based on BT technology. Currently it comprises commercial medical sensors (a pulse-oximeter and a ECG sensor), a GPS device and an Intelligent Node (IN) (which will be deployed on a Smartphone). The IN integrates two wireless interfaces, and BT, as well as a connection to the cellular network. BT is the technology used for communications between the devices and the IN, which plays the master role in the BT piconet. Wifi and/or GPRS/UMTS are employed to send information from the IN to the CCS and the MCMU. An application in the IN manages the BT piconet and the CCS connections. As the IN receives data from iban nodes, it stores them during a period which can be remotely configured. After every period, the IN sends the data to the server through the interface if a Wifi access point is available. Otherwise, the data is transmitted through GPRS/UMTS. The BT connections are kept active while the devices are in the coverage area of the IN. Thus, as soon as an event occurs, the IN can notify it to the server. However, the main drawback of this policy is the increase in power consumption. The information received from the pulse-oximeter is processed in order to verify that the oxygen saturation and the heart rate are not out of the security range established for the patient. If the IN detects a value ISSN: ISBN:

4 lower or greater than a predefined threshold, a SMS will be sent to the MCMU (a cell phone carried by a physician and/or by a predetermined list of patient s relatives). The thresholds can be set up by the physician from the CCS or from his own MCMU. Bluetooth ECG Belt SPO2 BT Pulse-oximeter Intelligent Node (IN) GPS 1. PATIENT intelligent BODY AREA NETWORK (iban) Wifi 2. CENTRAL CONTROL SYSTEM (CCS) GPRS/ UMTS Fig. 1. General architecture of the system. When a MCMU receives a SMS alert from a patient, the physicians can run a J2ME telemonitoring application. This program allows to select a patient and the sensor whose data the physician wants to display. In order to get the requested data, the application in the handheld device of the physician establishes a TCP connection with the CCS. Through this connection, the CCS retransmits the frames received from iban in quasi-real time. Additionally, the application provides the possibility of selecting the data to display, the values of the thresholds utilized to detect an emergency or even the transmission period. 4 System implementation Internet Internet 3. REMOTE MONITORING UNITS: fixed or MCMU Medical premises The next devices have been incorporated in the iban prototype: (1) a smartphone, initially a Nokia 9500 model and later, a Nokia N93 and Nokia E61, as hardware platforms for the IN, (2) a Bluetooth pulsioximeter, from Nonin Medical [20], a ECG sensor from Corscience [21] and a Bluetooth GPS receiver with SirfStarIII chipset. For the Mobile Control and Monitoring Unit (MCMU) a smartphone has been also employed. A J2ME application has been designed for the IN. The Java program (midlet), implemented for the Nokia N93 and E61 (Series 60 3rd. Edition), uses the following Application Program Interfaces (APIs): Bluetooth API (JSR-82) for managing Bluetooth connections; Location API (JSR-179) to get data from the GPS; Wireless Messaging API (JSR-205) for sending SMSs and MMSs; File Connection API (JSR-75) to record patient s information into the file system residing on the mobile device or on an external memory card. Security and Trust Services API (JSR-177) was also employed to guarantee the privacy of the data transmissions. To encrypt data, a symmetric DES (Data Encryption Standard) algorithm with a prefixed key is utilized. The control and monitoring application for the MCMU also has been developed as a Java midlet. When an alerting condition is detected, the application, making use of API JSR-205 (Wireless Messaging API 2.0), generates a MMS and a SMS to be sent to patients relatives and to physician, respectively. To get the phone numbers the API JSR- 75 (File Connection and PIM API) has been employed. The generated MMS contains a text with the patient identifier and the values of the sensed health parameter, a patient s photography and a sound clip with a message from the patient (to describe a sensation or any indication of disease). The three MMS s components are integrated in a SMIL (Synchronized Multimedia Integration Language) presentation, which is reproduced when the MMS is opened. The MMS sent by the IN are received as any ordinary MMS (directly in the input message box of the cell phone). However, a midlet to receive and process a SMS automatically has been developed for the MCMU. By the use of Push Registry Utility, provided by MIDP 2.0, the midlet is notified about the SMS reception in a specific port. So, the program is started without requiring any operation from the user. The midlet shows the SMS text, reproduces the sound and provokes the cell phone to vibrate until the user reads the message. The midlet invites the user to alert the patient or a medical centre. The CCS is implemented by means of two Java servelts in an Apache web server with Tomcat. One servlet is in charge of managing the connection requests from patients, compiling and processing medical data. The servlet also provides access to the IN capabilities through web applets. The other servlet serves the requests from the MCMUs carried by physicians, retransmitting the data from the IN to the MCMU through a TCP connection. The CSS and the patient iban can communicate with two protocols: (a) HTTP: data are sent in the HTTP requests. If the server has to send a command, it will use a HTTP response. (b) TCP-UDP: configuration commands sent to the IN as well as the responses to the commands and indications are transmitted through TCP, whereas the patient data are sent over UDP. Aiming at providing an easy access to physicians, two access mechanisms are included in the architecture: ISSN: ISBN:

5 1. Web interface: The physician can access the system using a navigator with RMI (Remote Method Invocation) support, such as Firefox, by means of a Java Applet. This Applet is structured in two main components: (a) A Configuration Interface to manage the iban configuration and operation mode, (b) A Data Display to show the health parameters and location information. The Configuration Interface communicates with the CSS by the RMI. The Data Display subsystem also uses RMI to get, in real-time, the parameters extracted from the decoded frames in the server. 2. Mobile Unit: Special midlets are installed on the MCMU. The physician only has to start the midlet, which will establish a TCP connection with the server to receive the encrypted frames from the IN which were redirected by the CSS. 5 Biosignal Emulator The evolution of the wireless communications technology towards a coexistence model (in which PAN, BAN, WLAN and cell networks can interact simultaneously) suggests a complex panorama with several challenges at different levels. The study of these problems in the specific ambit of telemedicine applications and, in particular, in scenarios with transmission of biosignals offers an emerging and novel field of great interest for the research [22]. In this sense, most advances in the redefinition of protocols and standards (e.g.: Bluetooth profiles, ZigBee operating modes, interoperability of ad hoc networks and Internet WAN, ) or even the scalability of medical BANs must be evaluated in realistic environments where a set of real biomedical connections are simultaneously running. The definition of these environments may require the utilization in parallel of dozens of biosensors. To reduce the cost and to increase the programmability and operability of the testbeds for this type of coexistence experiments, sensor emulators must be utilized. For this purpose, a software biosignal Emulator has been developed and integrated into the iban prototype presented above. Presently, the software emulates the behavior of a commercial ECG device by generating electrocardiogram (ECG) signals. These signals can be downloaded from a well known Internet Medical Data Base (PhysioBank). The PhysioBank database [23], which can be publicly accessed via Internet, provides a vast archive of formatted digital recordings of biomedical signals. The database, which is aimed for research purposes, incorporates a great variety of physiological recordings, captured from both healthy and pathological subjects. The records in Physiobank basically consist of binary files with digitized samples of one or more signals, as well as header files describing the properties (type, format, duration, sampling frequency, etc.) of the stored signals. Among the different formats utilized by the signals present in PhysioBank, "Format 212" and Format 16 have been selected to be implemented because of its popularity. In fact, there is a large amount of free and open-source software that processes these formats. This software can be utilized to test and verify the functionality of the developed applications. Apart from the emulator software, a server module and a reception program have been specifically designed to retrieve the Internet data and receive the Bluetooth signal of the emulator, respectively. All these software modules have been also developed with Java. In particular, the communication between the ECG emulator, the server and the receptor is implemented by means of a client-server architecture integrated by a J2EE server and two J2ME midlets. The emulator midlet, which can be installed on a Smartphone (or any J2ME enabled device), includes a simple graphical interface that allows the user to select one or more records corresponding to several cardiac pathologies. Once this selection has been completed, the emulator midlet waits for an incoming Bluetooth connection from the reception unit. When the Bluetooth connection with the receptor has been successfully established, the emulator midlet requests the server the record data previously selected via a wireless connection (WiFi/GPRS-UMTS). The server in turn is a J2EE Java program in charge of Internet communications so that the emulator can be released of this function. Thus, under a request of the emulator, the server accesses the Internet (Physiobank) data base, downloads the corresponding files associated to the requested record, processes them and streams the pre-encoded data to the midlet on the smartphone. As the midlet receives the stream information from the server, it builds the packets to be transmitted, fragmenting the data according to a configurable and predefined format (in this case, a proprietary format from Corscience). The reformatted information is then sent to the midlet in the reception unit via Bluetooth, emulating the flow of an actual sensor. The reception software detects critical events, like bradycardia, tachycardia or arrhythmia and notifies the detected alerts graphically and audibly. The reception software can be easily incorporated in the IN of the developed iban. Consequently this scheme permits to substitute the Bluetooth ECG sensor by any J2ME capable device. Presently, the emulator is limited to ECG data but it will be extended to other types of signals soon. ISSN: ISBN:

6 6 General evaluation and conclusions The utilization of smartphones as the central node in medical BANs may be of interest because of the patient s familiarity with mobile devices. The main restriction from usability perspective is that the monitoring application carries out tasks considered by the operating system as risky for security. Therefore an acknowledgement is requested to user before continuing certain actions. This drawback could be avoided with a validated certificate, instead of the self certificate employed for our application. On the other hand, the J2ME implementation for monitoring applications is advantageous since its portability is greater than that of other programming languages (C++, Python) capable of working in Symbian. Even so, it has been detected that the problems occurred in the midlet implementation heavily depend on the model of the employed smartphone. References: [1] R.S.H. Istepanian et al. (Ed.), M-Health: Emerging Mobile Health Systems, Springer Science, [2] E Jovanov et al., A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation, Jour. of Neuroengineering Rehabilitation, vol. 2, no. 1. (March 2005), pp [3] H. S. Ng et al., Wireless technologies for telemedicine, BT Technology Journal, Volume 24 Issue 2 (April 2006), pp [4] K Hung et al., Wearable medical devices for tele-home healthcare, in Proc. of IEEE EMBS, (S. Francisco, Sept. 2004), vol.7, pp [5] E Jovanov et al., Wireless Personal Area Networks in Telemedical Environment, in Proc. of IEEE EMBS, (Arlington, Nov. 2000), pp [6] A D Malan et al. CodeBlue: An Ad Hoc Sensor Network Infraestructure for Emergency Medical Care, In Proc. of WAMES 2004, (Boston, June 2004), pp [7] S Warren et al. Wearable telemonitoring systems designed with interoperability in mind, in Proc. of IEEE EMBS 2003, (Cancun, Mexico, Sept. 2003), vol. 4, pp [8] N Timmons and W Scanlon. Analysis of the performance of IEEE for medical sensor body area networking, in Proc. of IEEE SECON 2004, (Sta. Clara, USA, Oct. 2004), pp [9] J Yao et al. A wearable point-of-care system for home use that incorporates plug-and-play and wireless standards, IEEE Trans. on Inf. Tech. in Biomedicine, vol. 9, no. 3 (Sept. 2005), pp [10] U Anliker et al. AMON: a wearable multiparameter medical monitoring and alert system, IEEE Trans. on Inf. Tech. in Biomedicine, vol.8, no. 4 (Dec. 2004), pp [11] J Dong and H Zhu. Mobile ECG detector through GPRS/Internet, In Proc. of CBMS 2004, (Bethesda, USA, June 2004), pp [12] K Liszka et al. Keeping a beat on the heart, in IEEE Pervasive Computing, vol.3, no.4 (Oct- Dec 2004), pp [13] R G Lee et al. A mobile-care system integrated with Bluetooth blood pressure and pulse monitor and cellular phone, IEICE Trans. on Information and Systems, vol.e89-d, no.5 (May 2006), pp [14] S. Krco, Implementation solutions and issues in building a personal sensor network for health care monitoring, in Proc. of Int. IEEE EMBS, (April, 2003), pp [15] S Krco and V Delic, Personal wireless sensor network for mobile health care monitoring, in Proc. of TELSIKS 2003, (October 2003), vol.2, pp [16] A Rasid et al., Bluetooth Telemedicine Processor for Multichannel Biomedical Signal Transmission via Mobile Cellular Networks, IEEE Trans. on Inf. Tech. in Biomedicine, vol. 9, no. 1 (March 2005), pp [17] D Wang et al. A wireless sensor network based on Bluetooth for telemedicine monitoring system, in Proc. of IEEE MAPE 2005, (Beijing, China, Aug, 2005), vol.2, pp [18] S Warren et al. Interoperability and Security in Wireless Body Area Network Infrastructures, in Proc. of IEEE EMBS 2005, (Shanghai, China, Sept. 2005), pp [19] D Park, S Kang. Development of reusable and expandable communication for wearable medical sensor network, in Proc. of EMBS 2004, vol.7, (S. Francisco, USA, Sept. 2004), pp [20] Nonin Medical, Inc. [21] Corscience: [22] C. Urdiales et al., On Practical Issues about Interference in Telecare Applications Based on Different Wireless Technologies, Telemedicine & e-health, vol. 13, no. 5, (October 2007), pp [23] PhysioBank (physiologic signal archives): URL: ISSN: ISBN: