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Sensors and Actuators A 162 (2010) 116–129

Contents lists available at ScienceDirect

Sensors and Actuators A: Physical

journa l homepage: www.e lsev ier .com/ locate /sna

Implementation of wireless body area networks for healthcare systems

Mehmet R. Yuce ∗

The School of Electrical Engineering and Computer Science, University of Newcastle, University Drive, Callaghan, NSW 2300, Australia

a r t i c l e i n f o

Article history:Received 4 June 2009Received in revised form 22 May 2010Accepted 8 June 2010Available online 12 June 2010

Keywords:Medical sensor networkBody area networkTelemetryBody sensor networkMedical monitoring

a b s t r a c t

This work describes the implementation of a complete wireless body-area network (WBAN) system todeploy in medical environments. Issues related to hardware implementations, software and wirelessprotocol designs are addressed. In addition to reviewing and discussing the current attempts in wirelessbody area network technology, a WBAN system that has been designed for healthcare applications will bepresented in detail herein. The wireless system in the WBAN uses medical bands to obtain physiologicaldata from sensor nodes. The medical bands are selected to reduce the interference and thus increase thecoexistence of sensor node devices with other network devices available at medical centers. The collecteddata is transferred to remote stations with a multi-hopping technique using the medical gateway wirelessboards. The gateway nodes connect the sensor nodes to the local area network or the Internet. As suchfacilities are already available in medical centers; medical professions can access patients’ physiologicalsignals anywhere in the medical center. The data can also be accessed outside the medical center as theywill be made available online.

© 2010 Elsevier B.V. All rights reserved.

1. Introduction

As portable devices like cellular phones, pagers and MP3 play-ers become popular; people start to carry such devices aroundtheir bodies. In 2001, Zimmerman [1] studied how such electronicdevices operate on and near the human body. He used the termwireless personal area network (PAN). He characterized the humanbody and used it as a communication channel for intra-body com-munications. Later around 2001, the term PAN has been modified tobody area network (BAN) to represent all the applications and com-munications on, in and near the body [2]. One of the most attractiveapplications to use BAN is in the medical environment to monitorphysiological signals from patients.

Wireless body-area network (WBAN) is a special purposewireless-sensor network that incorporates different networks andwireless devices to enable remote monitoring for various environ-ments [3,4]. One of the targeted applications of WBAN is in medicalenvironments where conditions of a large number of patients arecontinuously being monitored in real-time. Wireless monitoringof physiological signals of a large number of patients is one ofthe current needs in order to deploy a complete wireless sensornetwork in healthcare system. Such an application presents somechallenges in both software and hardware designs. Some of themare as follows: reliable communication by eliminating collisions oftwo sensor signals and interference from other external wireless

∗ Tel.: +61 2 49215204; fax: +61 2 4921 6993.E-mail address: [email protected].

devices, low-cost, low power consumption, and providing flexibil-ity to the patients [5,6]. A WBAN-based wireless medical sensornetwork system when implemented in medical centers has sig-nificant advantages over the traditional wired-based patient-datacollection schemes by providing better rehabilitation and improvedpatient’s quality of life. In addition a WBAN system has the poten-tial to reduce the healthcare cost as well as the workload of medicalprofessions, resulting in higher efficiency.

There is already a number of monitoring systems developed orbeing used in medical centers [7–17]. The available medical moni-toring systems are generally bulky and thus uncomfortable to becarried by patients. Most of the current effort has mainly beenfocused on the devices that are monitoring one or few physiologicalsignals only. When multiple sensors are involved, wires are used toconnect the sensors to a wearable wireless transmitter. Wired sys-tems restrict patients’ mobility and comfort level, especially duringsleep studies. Future implementation of medical monitoring neces-sitates the use of small, low-power sensor nodes with wirelesscapability [17–19].

Thus far there is no available standard for a wireless body-areanetwork specifically targeting health care. Most popular wirelesscommunication technologies and protocols proposed or used inmedical monitoring systems are listed in Table 1. Existing moni-toring systems use the short-range wireless systems such as ZigBee(IEEE 802.15.4) [9–11], WLANs [5,8], GSM [12] and Bluetooth (IEEE802.15.1) [13–15]. To make the power consumption and the size ofthe device low, short-range devices like Bluetooth and ZigBee aremostly used with sensors to collect medical data from a patientbody. Especially WLAN technologies are avoided for low power

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M.R. Yuce / Sensors and Actuators A 162 (2010) 116–129 117

Table 1Wireless technologies used in medical monitoring.

MICS [6] WMTS [6] UWB IEEE(802.15.6) [28]

IEEE 802.15.4(ZigBee)

IEEE 802. 15.1(Bluetooth)

WLANs(802.11b/g)

Frequency band 402–405 MHz 608–614,1395–1400,1429–1432 MHz

3–10 GHz 2.4 GHz(868/915 MHzEur./US)

2.4 GHz 2.4 GHz

Bandwidth 3 MHz 6 MHz >500 MHz 5 MHz 1 MHz 20 MHzData rate 19 or 76 kbps 76 kbps 850 kbps to

20 Mbps250 kbps(2.4 GHz)

721 kbps >11 Mbps

Multiple access CSMA/CA, polling CSMA/CA,polling

Not defined. CSMA/CA FHSS/GFSK OFDMA,CSMA/CA

Trans. power −16 dBm (25 �W) ≥10 dBm and<1.8 dB

−41 dBm 0 dBm 4, 20 dBm 250 mW

Range 0–10 m >100 m 1, 2 m 0–10 m 10, 100 m 0–100 m

sensor nodes because of their large size and power consumptionused to provide longer ranges (i.e. 100 m). As these technologiesmay most probably be installed in medical environments due toother applications, medical gateway devices should be designed ina WBAN to interface with these wireless systems to provide a wire-less link between the control unit and a mobile device (i.e. PALM)or between the control unit and Internet via a Wi–Fi link.

The low-data rate IEEE 802.15.4 technology (ZigBee) has beenthe most popular short-range standard used recently in medicalmonitoring systems due to its low transmitter power [20,21]. Sys-tems using Zigbee wireless platform may however suffer from thestrong interference by WLANs which share the same spectrum andtransmit at a larger signal power [22]. Installing an interference freemedical network in a hospital may thus be quite challenging sincethere exist a lot of other wireless systems and equipments using the2.4 GHz band. The device technologies operating at the 2.4 GHz ISMband should thus deal with the interference and coexistence issueswhen they are located in the same environment [23]. As can be seenin Table 1, in addition to unlicensed ISM bands, there are medicalbands such as MICS (Medical Implant Communication Service) andWMTS (Wireless Medical Telemetry Service) that are specificallyregulated for medical monitoring by communication commissionsaround the world [24–26]. The recent short-range, low-data rate,ultra-wideband (UWB) technology is another attractive technologythat could be used for body-area network applications because ofits regulated low transmitter power [7].

In addition to review and discuss the current attempts in body-area network technology, a WBAN system that has been designedfor healthcare applications will be presented in this paper.1 Theproposed WBAN is a multi-hopping wireless medical network thatuses the MICS band to obtain physiological data from sensors placedon or in the body and the WMTS band as an intermediate nodefor a longer wireless communication. The data is transferred toremote stations through the local area network or the Internetalready available in medical centers as a part of their ICT (Infor-mation and communication technologies) infrastructure. Unlikethe other medical sensor networks (they usually use 2.4 GHz ISMband); we use medical standards occupying the frequency bandsthat are mainly assigned to medical applications.

The paper is organized as follows. Section 2 describes anoverview of WBANs for the medical environment. It gives the con-cept of WBAN applications with their important design features.Section 3 presents a complete WBAN implementation. Differentmedical scenarios are defined for a real implementation in hospitalenvironment. This section also discusses the hardware implemen-tation details for the proposed prototype system. The multi-accessprotocol used for multi-sensor and multi-patient scenario is alsogiven here. Section 4 presents computer programs used for record-

1 This work is an expanded version of the conference paper given in Ref. [6].

ing, monitoring and processing the medical data captured throughthe prototype system. An overall system performance evaluationas well as comparisons with the recent attempts of WBAN-basedpatient monitoring systems have been given in Section 5. Andfinally Section 6 concludes the paper.

2. Wireless body-area networks in medical environment

The application of WBAN in a medical environment may con-sist of wearable and implantable sensor nodes that sense biologicalinformation from the human body and transmit over a short dis-tance wirelessly to a control device worn on the body or placed at anaccessible location. The sensor electronics should be miniaturized,low-power and detect medical signals such as electrocardiogram(ECG), photoplethysmogram (PPG), electroencephalography (EEG),pulse rate, pressure, and temperature. The collected data from thecontrol devices are then transferred to remote destinations in awireless body-area network for diagnostic and therapeutic pur-poses by incorporating another wireless network for long-rangetransmission (see Fig. 1).

The monitoring devices currently used in medical centers arenot completely wearable because their electronics are bulky andwires are used for connections to multiple sensors. Fig. 1(a) showsthe current application of a sensor network used in some mod-ern medical centers. A wireless control unit (i.e. CCU) is collectinginformation from sensors through wires and transmits to a remotestation for monitoring. In this implementation, the control unitis cumbersome and using wires is not advised for the comfort ofpatients. As shown in Fig. 1(b) the future medical sensor networkrequires miniaturized and wearable sensor nodes that can commu-nicate with the receiving device wirelessly. The system will consistof individual wireless sensor nodes that can transfer a person’sphysiological data such as heart rate, blood pressure, ECG via awireless link, without the need of any wired connection. Each sen-sor will have wireless capability and its design will be optimizedin terms of the physical characteristics of the physiological signal.Having individual wireless nodes is also very beneficial since notall patients require all the physiological parameters for diagnosis.In the future, sensor node electronics could be designed using flex-ible and stretchable technology so that sensor nodes can easily beembedded in textile (i.e. patient clothes) to improve the patient’scomfort in a healthcare environment [27].

Low power operation and miniaturization are two essentialphysical requirements of sensor nodes as they determine the life-time of the devices and their suitability to be worn by patients.Power consumption of sensor nodes is dominated by the operationof the wireless chip, RF (radio frequency) transmission and recep-tion. Thus it is desirable to use a wireless platform that will providelow power consumption and has a minimum transmitted powerwhile still meeting the required range of a body-area network.


118 M.R. Yuce / Sensors and Actuators A 162 (2010) 116–129

Fig. 1. A wireless sensor network system detecting and transmitting signals from a human body: (a) current application of medical sensor network and (b) future applicationof medical sensor network targeted by wireless body area network.

A study group of IEEE has been launched in November 2007to work on the WBAN standardization [4]. One of the main tasksof the group is to investigate a secure wireless platform to bededicated to a WBAN application only as the existing wirelessstandards are employed in many different applications and arenot exactly optimized for healthcare systems. UWB is one of thewireless candidates considered by this new standard. One bigadvantage of UWB wireless technology is that its data rate rangesfrom 850 kbp to 20 Mbp which can be used for simultaneous mon-itoring of many continuous physiological signals such as ECC, EEGand EMG [7,28]. In addition, UWB wireless technology does notalso present an EMI (electromagnetic interference) risk to othernarrow band systems and medical equipments in healthcare sinceits transmitter power is low and the frequencies used are notcrowded.

Although the WBAN standard is still working on the develop-ment of transmission band for the body-area networks, the UWBwireless chips are not available commercially to apply in a WBANat the moment. Although UWB claimed very low power initiallyin the literature, the attempts of such technology in the integratedcircuits have exhibited power consumption more than that of theconventional narrowband short-range wireless chips [29]. Highpower consumption mainly comes from the design of the receiveras higher RF and analog gains are required at the front-end toreceive the very low level transmit power. For an implant node inWBAN, UWB will cause high penetration loss because it operatesat high frequencies (3–10 GHz for medical applications) [28]. Thismay significantly affect the performance and size of the implantablenodes. UWB receivers are already more complex than the narrow-band system ones due the low transmitted signal. The additionalloss from the skin will necessitate a higher gain at the front-end ofthe receiver. Providing high gain at high frequencies is not feasi-ble in RF technology. One method to overcome this problem is touse a transmit-only (Tx-only) UWB sensor node [37]. In a transmitonly WBAN system, the sensor nodes contain a transmitter withouta receiver and should be programmed to send sensor data using aspecial multi-access protocol to reduce collisions in order to realizea multi-patient monitoring.

It is envisioned that the sensor nodes of a WBAN will use a ded-icated wireless link which will most likely be a narrow ISM bandconsidering the current wireless technology developments. How-ever the UWB technology could still be incorporated in a WBAN forthe applications that require higher data rate as well as in case acoexistence issue arises.

In the future, a WBAN system, in order to be applied in a medicalenvironment, should incorporate the following significant designimprovements and features [3,5,8–10,12]:

• Low-cost and low-size sensor node electronics design with wire-less capability. A sensor node will be able to transfer date over adistance up to a few meters. And sensor nodes should be minia-turized so that they can be easily wearable.

• Energy optimization techniques should be developed by the com-bination of the link- and physical-layer functionalities of thewireless devices, leading to increase in the battery life and thusthe lifetime of the sensor nodes. Sleep mode technique should beused so that the sensor nodes will spend most of their time in thelow-power state when data transmission is not required.

• Physiological data should be categorized as crucial and non-crucial data for each patient. As an example, although it couldbe different with different patients, vital signals like ECG can bemore crucial than temperature for certain patients. Thus a WBANsystem should prioritize the crucial data.

• High gain miniaturized antennas should be developed for sensornodes to increase the transmission reliability and to minimizeinterference hence reducing power consumption.

• Each sensor should be optimized according to its characteristicsusing a variable sampling rate.

• Unlike other sensor network systems, in a WBAN system, eachsensor signal has a different frequency (i.e. not uniform) and thuseach sensor node should be optimized according to its sensor’sfrequency band. In addition, considering that some non-crucialphysiological signals like temperature information may only bemeasured for a longer period of time (e.g. every hour), a WBANwill result in better performances if an “adaptive” communica-tion protocol is utilized to accommodate such differences in thesystem which will clearly make the system power efficient.

• In order to provide a long-range remote monitoring, severalgateway devices should be developed to interface with the exist-ing wireless systems in healthcare. These gateway devices willmainly be used to provide communications between the CCUsand the remote computers or mobile devices.

• Handover mechanism should be integrated in a WBAN that couldbe useful for free movement of patients in a large medical envi-ronment. It can be used to track patient throughout the hospitalor can track patient locations when they are outdoor doing theirdaily activities. An alarm option may be included if a patientleaves a room or goes out of the range of a CCU. This can givethe last location of patients that would allow staff to easily trackpatient location. Such features can be included in the systemby detecting signals through several remote stations, which areusually known as soft handover.

• Another necessary component in a WBAN is the wireless-network security. Key software components should be definedand developed to accommodate secure and effective wireless



Fig. 2. Targeted three different scenarios in our wireless body area network system for multi-patient monitoring in medical centers, (a) when the device is used individually,or (b) and (c) for multi-patient monitoring in a medical center, representing one room and one floor respectively.

networking. Data should be only accessible by an authorized per-son at remote destinations. Hardware components and softwareprograms should be coordinated together to provide secure andreliable communications.

• Lastly, a WBAN system should have its own standards for datacollection and storing techniques, and also for the wireless linksto eliminate coexistence issues.

3. A body-area network implementation for healthcare

Currently we are developing a complete wireless body-area net-work that is based on different frequencies in order to eliminateinterference issues as well as to apply to different environments.We use MICS, WMTS and 433 ISM bands to detect signals from thesensors on the body. The goal in a WBAN application is to dedicateone sensor node to one physiological signal as described in Fig. 1(b)to eliminate placing wires on the patient body. However, theremaybe some clinical applications that requires the monitoring ofmore channels of the same physiological signal simultaneouslyto provide a good quality screening [43,44]. More channels maybe required when considering EEG signal for brain activities. Forthe applications that require monitoring of more channels such asECG/EEG/EMG, we incorporate the UWB technology to achieve ahigh data rate wireless link [28]. We also like to point out that weare working to interface our devices with IEEE 802.15.4 (ZigBee)

and Wi-Fi links to cover a large area of body-area network. Theselection of wireless schemes for sensor nodes will depend verymuch on the environment that the sensor nodes will be used.

The body-area network prototyping system presented in thispaper uses a multi-hopping structure where the MICS band is usedfor gathering signals from sensors and WMTS is used to transmit thesensor data to remote stations allowing a longer range monitoring.These frequency bands are internationally available and are permit-ted for a remote monitoring of several patients simultaneously. TheMICS band has a low emission power (25 �W, comparable to UWB)leading to a lower power consumption, and will thus provide oneof the most suitable transmission bands for medical sensor nodes[26,30]. Although a few incidents have been reported due to theinterference from some local TV channels in USA, WMTS is still themost popular band for wireless telemetry used in hospitals [31].

Hardware electronics and software programs are developed forthree scenarios in the proposed WBAN as shown in Fig. 2. The firstscenario targets individual use in the medical center or can be usedin home care. This wireless body-area network comprises of sen-sor nodes, a CCU that transmits data to a local PC and then to areceiver station (i.e. remote PC) at a medical center. After obtainingthe physiological data from a human body, sensor nodes transmitthose data to the CCU via the RF link using the MICS band. The CCUthen re-packages the data and transmits to the local PC. The datacollected at the local PC is transferred to a remote PC across the



Fig. 3. A multi-hopping WBAN prototyping system for multi-patient monitoring.This prototyping system uses two medical standards: MICS for short distance andWMTS for long distance wireless transmission respectively.

network in a medical center or through Internet if the patient is ata different location than the medical center.

In the second and third scenarios, more than one patient sharea CCU box. The CCU can either be connected to a local PC in theroom (Scenario-2) or it can transmit data wirelessly to a remote CCUbox that is attached to a PC via another wireless link (e.g. WMTS(600 MHz)). In the latter case, the CCUs act as portable wirelessgateway devices (e.g. intermediate devices) and hence the systemwill form a multi-hopping wireless networking. The arrangementin the last scenario can be used for one or more rooms in a medicalcenter. A hardware setup to realize the Scenario-2 and -3 is givenin Fig. 3. Patients’ physiological parameters are sent to the interme-diate CCUs and then to the base station (a remote PC). The systemconsists of three networking structures. A network between sen-sors and CCUs, another network between CCUs and a base station,and the last communication is between base stations (This is mainlya LAN connection).

Deploying a complete wireless medical system in a hospitalenvironment requires monitoring of a few hundred patients. Withthis article we discuss issues related to the implementation ofsuch a large-scale body-area network and introduce techniquesthat suit well for an implementation in hospitals. The existing andreported body-area network projects have mainly focused on thescenario given in Fig. 2(a) mostly with the wired sensors’ connec-tions described in Fig. 1(a) [3,9–12,14]. We have particularly beeninterested in a WBAN system which deals with the implementa-tions shown in Fig. 2(b) and (c), Scenarios 2 and 3 respectively.

Two pieces of software are created in this project. The softwareresiding at the local PC is named GATEWAY. The job of GATEWAYis to gather data from the CCU through RS232/USB2 cable and for-wards it to one or more remote PCs using TCP/IP sockets over anEthernet network. The software residing at the remote PC is namedas BSN application. The BSN application collates data from the localPC, interprets and stores them onto the remote PC to be analyzedlater by health professionals. The base station (i.e. the remote PC) iscapable of displaying all the received data on a graphical user inter-face (GUI) and is also capable of storing all the data in the databasesystem of a medical center (see Section 4).

The MICS regulations require that the output power of anyterminal must be kept under −16 dBm and is not intended forlong-range wireless connections [24,26]. To facilitate a long-rangecommunication, a WMTS link operating in the 608–614 MHz band

2 We have used both wired USB and RS232 connections. USB has a faster datacommunication and removes the necessity of holding data in the base station.

Table 2Physiological parameter ranges and signal frequencies.

Parameter Range of parameter Signal frequency (Hz)

ECG signal 0.5–4 mV 0.01–250Respiratory rate 2–50 breaths/min 0.1–10Blood pressure (BP) 10–400 mmHg 0–50EEG 3–300 �V 0.5–60Body temperature 32–40 ◦C 0–0.1EMG (electromyogram) 10 �V to 15 mV 10–500GSR (galvanic skin reflex) 30 �V to 3 mV 0.03–20

is used between the CCU and the base station allowing for a muchlonger range. This implies that the intermediate CCUs must be ableto operate both at MICS and WMTS frequencies, providing a linkbetween the nodes and the computer. The CCU remains in closerange with the patient and may be attached to their belt for exam-ple when it is for individual use. The nominal wireless distance forthe MICS link is around 10 m. The WMTS link targets a distancemore than 100 m.

3.1. Sensor nodes and CCU hardware designs

We develop our individual sensor nodes to detect and transmitthe physiological signals listed in Table 2. Characteristics of thesephysiological signals are obtained from the public domain availableon the Internet. Most physiological signals have low amplitudes andfrequencies in nature, and occupy a small information bandwidth[43]. At such low frequencies and low amplitudes, some problemsinherent to circuits need additional attention. For a reliable infor-mation transfer it is necessary that the interface electronics in thesensor nodes detect the physiological signals in the presence ofnoise and increase the signal-to-noise ration (SNR) of the detectedsignal for a better processing by the subsequent blocks of the sensornodes.

Sensor nodes are designed to be small and power efficient so thattheir battery can last for a long time. They collect the signals froma human body which are usually weak and coupled with noise. Anamplification/filtering process is utilized first to increase the signalstrength and to remove the unwanted signals and noise. Then anAnalog to Digital conversion (ADC) stage is employed to convert theanalog body signals into digital for a digital signal processing. Thedigitized signal is processed and stored in a microcontroller. Themicrocontroller will then pack the data and transmit over the air viaa wireless transceiver. Fig. 4 shows the hardware implementationof our sensor nodes and the block diagram.

In our system we designed sensor nodes that can measure up tofour body signals for a single patient. One node is dedicated to oneof continuous physiological signals such as ECC, EEG or EMG. Foursensor nodes electronics (i.e. 4-channel) are built on a commonPCB board so that some electronics can be used interchangeably

Fig. 4. An example of sensor node hardware designed.



Fig. 5. Schematic of analog front-end for detection of continuous physiological parameters.

for testing purposes. Eventually each board will be used for onlyone sensor signal in a WBAN (explained in Fig. 1(b)). The antennasfor this project are designed as a loop printed around the proto-typing boards (see Fig. 4). An important feature in our design isthat the signals in Table 2 will be grouped into critical and non-critical data during the transmission. The wireless protocol will bedesigned such that the priority will be given to the critical data. Thedetail of this discussion is presented in Section 3.2.

Considering the signal bandwidths given in Table 2, except forthe EMG signal, a sampling rate of around 200–500 Hz will be nec-essary for the ADC in the microcontroller (the sampling rate shouldbe a minimum of twice the highest frequency in the signal that isdigitized). For an EMG signal, at least 1 kHz is required in order todetect the high frequency section of EMG waveforms. The trade-off between the reduction in sampling rate and the total powerconsumption of the ADC is determined by the choice of the spe-cific physiological parameter used in the sensor node. In our sensorboard, the PIC microcontroller is able to provide a sampling fre-quency up to 52 KHz. Thus we are able to set an optimum samplingfrequency for each individual physiological signal.

As shown in Table 2 the amplitude and frequency informationof the continuous physiological signals are low. They require anamplifier with a relatively high gain and a low-pass filter with a lowcut-off frequency. Thus the same analog front-end can be used todetect these continuous signals. The front-end of the sensor nodes(i.e. interface electronics) in our prototype uses an instrumenta-tion amplifier (INA321) and an active low-pass filter (LTC6081), asshown in Fig. 5 [32]. Both components are selected because of theirhigh common mode rejection ratio (CMRR) and low current con-sumption properties. INA321 has a CMRR of 94 dB, while LTC6081has a CMRR of 100 dB. The circuit in Fig. 5 is running from a 3.3 Vsource with virtual ground set at 1.25 V. The shutdown pins areconnected to the microcontroller. The active current consumptionis 40 �A and when operating in shutdown mode it consumes lessthan 1 �A. Typically ECG and EEG signals have amplitude of lessthan 500 �V with a usable frequency less than 100 Hz. The inputsignals are amplified by 60 dB, INA321 adds 14 dB, while LTC6081provides a gain of 46 dB with a cutoff frequency at 100 Hz. It isimportant to note that the required amplification and filtering caneasily be adjusted for a specific signal by modifying the capacitorsand the resistors in the circuit. As an example, in case of an EMGsensor node, the capacitor of the front-end should be modified tohave a cut-off frequency of 500 Hz. To save power and space in thesensor nodes, the required notch filter to eliminate the 50/60 Hz DCnoise is done at the remote PC via a software program.

We designed two different CCU boards to realize the scenariosgiven in Fig. 2. One CCU is designed to be connected to a com-puter via the USB port (Fig. 6(a)). The other CCU (CCU-2) given inFig. 6(b) functions as an intermediate device (i.e. wireless gateway)that presents the WMTS wireless link. Although both CCUs can be

used for multiple patients monitoring, the first CCU type (CCU-1)can also be useful for private usage at patients’ home or for a sin-gle patient monitoring in a hospital. It can receive the physiologicalsignals directly from sensors without using the gateway CCU.

The hardware for CCUs requires a microcontroller and a wire-less transceiver to coordinate all the activities, similar to the sensorboards. The CCU-1/sensor nodes consist of a transceiver (AMI52100IC) from AMI semiconductor used for the MICS band generation(we also used CC1000 in some sensor nodes to generate 433 MHzISM and WMTS bands for sensors-CCU wireless connections) andthe microcontroller-PIC16F887. In addition to these chips, we useanother transceiver-the CC1010 chip from Chipcon (this chip con-tains CC1000 and a microcontroller built-in) on the intermediateCCU board (CCU-2) to obtain a wireless transmission and network-ing with the WMTS band. The CC1010 and CC1000 transceiverchips can be configured to transmit anywhere within 300 and1000 MHz frequencies. They have been programmed to operatewith one of WMTS bands in our prototype system. The wirelesschips AMI52100 IC and CC1000 are selected in the project becauseof the following reasons: overall cost saving, low-power consump-tion, size, and the suitability of operating at the MICS, WMTS, and433 MHz ISM bands. AMIS has a data rate capability of 19 kbps while

Fig. 6. CCU boards. (a) CCU-1: central control unit connected to a computer via aserial cable USB, (b) CCU-2: Intermediate Central Control Unit (CCU). This device isshared by more than one patient and portable. It receives signals from patients viathe MICS wireless link and transmits to a base station via the WTMS wireless link.It uses two transceiver chips (AMIS and CC1010) to provide both wireless links.



Table 3Summary of devices used in sensor nodes and CCUs.

Features DevicesAMIS (52100) CC1000 CC1010a PIC16F887

Size (mm) 7.5 × 7.8 9.7 × 6.4 12.9 × 12.9 17.53 × 17.53Modulation ASK/OOK FSK/OOK FSK –Sensitivity −117 dBm −109 dBm −107 dBm –

PowerTransmitter Tx: 25 mA Tx: 26.7 mA Tx: 26.6 mA <0.6 mA (active)Receiver Rx: 7.5 mA Rx: 7.4 mA Rx: 9.1 mAData rate 19 kbps 76.8 kbps 76.9 kbps –Sniffingb 500 nA 200 nA ∼0 mA –Memory (RAM) – – 2048 + 128 368 + 256Additional features – – 10-bit, 22.7 KHz sampl. freq. 10-bit, 52 KHz sampling

a CC1010 has a microcontroller built-in.b Sniff mode enables the receiver to wake up or operates at times to “sniff” received RF signals and then return to “sleep” or “wait” mode if a signal is not detected.

CC1010 provides 76 kbps. Summary of features for the devicesused are listed in Table 3. Our sensor board, which includes thetransceivers, the microcontroller and the sensor front-end elec-tronics, consumes a power of 90 mW for the transmit mode andless than 30 mW for the receive mode when operating with a supplyvoltage of 3.3 V.

For our testing scheme given in Fig. 3, the ASK (amplitude-shiftkeying) is selected for the communication between sensors andCCU. And for the communication between CCUs and the based sta-tion, a frequency-shift keying (FSK) mode is selected. Two differentmodulation schemes are configured from AMIS and CC1010 wire-less transceivers in order to avoid any possible interference effectsbetween two wireless links. The antennas for both WMTS and MICSbands are designed with coils as shown in Fig. 6. All the boardsare custom made except for the base station which is the CC1010evaluation module obtained from Chipcon.

Currently as a part of our wireless body-area network project,we design some CCU-2 devices in which the second wireless com-prises of the standards such as ZigBee and the 802.11 Wi-Fi in orderto accommodate and interface with different wireless platforms.This will extend the application environment that would be usefulfor patients. Especially a wireless gateway device where the secondwireless is a Wi-Fi link will allow some patients to visit differentenvironments (libraries, work places and houses, etc.) where the

device can connect with Internet wirelessly and thus will able tosend data to the medical center for remote monitoring. Meanwhilethe patient will have the opportunity to continue with his socialactivities.

3.2. Multi-access protocol for sensor nodes and CCU

A number of medium access control (MAC) protocols have beenproposed for medical sensor networks [17–21]. Main requirementsof a MAC protocol are reliability, flexible transmission mechanism,high channel efficiency and a low end-to-end delay time. Mainlythree classes of MAC protocols have been considered for wire-less medical applications. They are TDMA (time division multipleaccess), polling and the contention-based protocols also known asthe random access protocols [20,21,33]. The TDMA and polling pro-tocols are entirely contention free but centralized in nature. TheTDMA protocol introduces a strict synchronization requirementwhereas a polling network introduces a high overhead of pollingmessage transmission. TDMA and polling-based networks intro-duce a fixed delay due to use of the fixed frame structure and thecycle time respectively. The contention-based protocols such asALOHA and CSMA (carrier sense multiple access) have been pro-posed for some sensor network applications. These protocols aredistributed in nature and do not require any centralized control

Fig. 7. Frame structures for polling protocol and CSMA/CA communication protocol. �p is the time that a polling sensor wakes up before the polling signal arrives from theCCU. The polling frame includes information about the sensors (sensor-IDs) that needs to be polled to get a data.



signal from the CCU. They are also dynamic in nature and offer mini-mum packet transfer delays when operating under low to moderateload conditions. The performance of a contention-based protocolcould degrade when the total traffic load increases significantly,which is an unlikely scenario in a medical sensor network appli-cation. In addition to its carrier sense ability, CSMA can also use acollision avoidance (CA) mechanism (so called CSMA/CA) to allowonly one sensor node to communicate and send a packet at onetime to avoid the interference and collision between sensor nodes[20,21]. The CSMA/CA MAC protocol is used by the IEEE802.11based Wi-Fi and the IEEE802.15.4 based WPAN (wireless personalarea network) standards. The CSMA/CA protocol offers lower delayand reliable transmission of packets in small size networks like aWBNA-based medical network [16,35]. Thus the use of this mech-anism is attractive for the transmission of critical data in medicalcenters.

Unlike other sensor network applications, in a WBAN system,not all sensor signals are required to be monitored all the time.As an example, a temperature reading is usually taken every hourin hospitals and thus a schedule-based protocol would be moresuitable for such sensor signals. Therefore we have decided to usethe combination of a polling protocol and CSMA/CA MAC protocolto transmit the sensor data from multiple sensors to the CCU in ourWBAN application.

A critical data (e.g. ECG, it could change according to the statusof a patient) will particularly be used in a CSMA/CA-based sensornode. A packet can be transmitted immediately without waitingfor its turn eliminating any delay [34,35]. For non-critical data liketemperature where the data collection is not necessary all the time,the polling network will be used for these nodes. In this case, theinformation will be sent whenever it is needed as it is not critical.Sensor nodes that are configured in a polling scheme will enterthe CSMA/CA scheme during the data transmission so that it willnot affect the transmission of the critical data. In our system weuse a collision avoidance-based CSMA (CSMA/CA) by including theRTS/CTS (ready to send/clear to send) and ACK (Acknowledgement)signals to eliminate the collision probability (see Fig. 7) [16,21].

The sensor nodes in polling scheme will sleep most of the timeand wake up for a duration required for a reliable data transfer.The polling sensor node is basically programmed to go sleep modeafter a successful packet transmission as shown in Fig. 7(a). Whendata has been sent to the CCU successfully, then the CCU sends anacknowledgment of receiving data correctly (see ACK command inFig. 7(b)). The sensor node will then go back to the sleep mode untila pre-defined time arranged by the microcontroller. It will wake upbefore a polling signal. If the polling signal from the CCU is request-ing a transmission from a particular sensor node, that sensor nodewill then try to send data with the similar data format of CSMA/CA. Itis assumed that the polling sensor nodes have the knowledge of the“polling time” – the time that CCU will request data from a pollingsensor node. This is a valid assumption for medical applicationsbecause medical data like temperature can have a pre-defined datareading times assigned by medical professions. The polling frameincludes the information about all sensors (sensor-IDs) that needto be polled to get a data. In other words, they are triggered to senddata. The nominal active period of the polling nodes is 1 min (morethan enough until a successful packet is sent in CSMA/CA mode)while the sleep mode time could be up to half an hour.

Fig. 7(b) shows the details of the MAC protocol used to coor-dinate the transmission of all signals between sensors and theCCU-Computer. The MAC protocol uses the CSMA/CA packet trans-mission technique with the RTS/CTS messages. It is incorporated inthe firm wares at both the sensor nodes and the CCU to provide abi-directional communication, to control the wireless transmissionand to prevent collision between sensor nodes. When a sensor nodewants to transmit a packet to the station (i.e. CCU), first it checks

the status of the transmission channel as shown in Fig. 7(b). If thechannel is free i.e. no carrier signal is present, then the CCU waitsfor a certain time period and then transmits a packet if the channelremains free for a specified period as shown in the figure. If theCCU senses the channel busy then it backs off a random time andreschedules its transmission attempt at a later time.

The RTS packet contains the ID of the transmitter (sensor)and a particular receiver (CCU) so that the transmitter and thereceiver (CCU) can pair up for a packet transmission. After theRTS transmission, the sensor node moves into the wait stateexpecting a CTS packet from the CCU. The CCU will transmit aCTS signal after the short wait period known as the SIFS (shortinterframe spacing). Other sensor nodes in the network can readthe RTS/CTS packet transmissions and will refrain from furtherpacket transmission by calculating the channel occupancy period.This process can avoid collisions and can improve the packettransmission reliability. When a sensor node receives the CTSpacket, it initiates a data packet transmission which would befollowed by an ACK or a negative ACK (NACK) packet by thereceiver station (CCU). The ACK or NACK responses are gener-ated by the CCU after performing a packet error-check mechanismusing the checksum field of the data packet. If the checksumfails, the CCU will ask for retransmission using the NACK packetelse an ACK packet is transmitted. A packet might be corruptedeither due to the transmission error caused by low SNR or dueto a collision when two or more transmissions overlap in time.During a RTS/CTS packet transmission or a data packet transmis-sion if any collision happens the sensor node either receives aNACK packet or it times out initiating a retransmission using theRTS/CTS procedures. The time out procedure allows a sensor nodeto come out of the wait state if the ACK or NACK packet goesmissing due to transmission errors. Above discussions show thatthe CSMA/CA-based protocol can efficiently support transmissionof physiological data packets in a medical networking environ-ment.

In case of a large traffic like in a hospital emergency ward, asdescribed in Scenario 3 in Fig. 2, four or more patients will beassigned to a particular CCU unit. This way the number of sen-sor nodes assigned to a CCU is limited and thus the performanceof the WBAN system will not be affected due to a large volumeof people. When the patients are in action walking around, theneach patient can be connected to one CCU as described earlier. Inaddition, using polling in a CSMA-based system will allow certainsensor nodes to stay silent for a certain amount of time, and thusonly the important sensors’ data will be transmitted at a presenttime. This adaptive scheme also helps the WBAN to reduce thedelay in the system or to increase the number of sensors to bemonitored.

In terms of power consumption, sensors nodes with pollingmechanism have longer battery life comparing to CSMA/CA basednodes (the crucial signals). Sleep and wake up patterns can becontrolled by the acquired signal and by the application. Wakeup pattern could be periodic or it can be demand driven. Onlya small portion of the microcontroller (oscillator and counters),which operates with a very low clock rate, always stays awake inthe system and will trigger the awakening process. As a result ofthis process, significant power saving is achieved and hence batterylife is increased. The power consumption of the polling based sen-sor nodes can be kept significantly low and thus their battery lifecan last several years [6,16]. In case of CSMA/CA communication,the nodes are always active either in transmission (the transmitteris active) or reception (the receiver is active). A CSMA node is alsoconsuming power when its receiver is waiting to receive the ACKand the CTS packets. A continuously active CSMA-based node canlast about three days, a lifetime similar to current cellular phoneswhen using a battery of 300 mAh.



Fig. 8. Live monitoring of multi-patients (physiological data presented in a graphical form at the remote PC). SensorID is used to define each patient.

4. Database, software programs and monitoring

In order to monitor data, several computer programs have beendeveloped during this project. The necessary software programshave been identified in Fig. 2. The software called GATEWAY isdeveloped at the monitoring PC to control the communicationwith the CCU to get readings from sensors and then forward themthrough the network/internet to an application on a remote PC(at a medical profession center). While performing this task, theGATEWAY also verifies the data integrity and schedules retrans-mission if required. Another software program is developed atthe remote PC (called BSN) that gets readings from GATEWAY viathe network/internet. These readings are stored in the remote PCfor analysis. The BSN application can collect and store readingsautomatically so that no person is required to be stationed at theapplication. It can undertake the administration of patients’ par-ticulars such as assigning new sensor ID (i.e. userID) to patients,segregating sensor readings from different patients and storingthem into the database. Using sensorID for each patient will ensuresafety in healthcare environments when multiple patients are mon-itored.

A graphical user interface (GUI) at the local PC as well as atremote PCs displays medical data. The GUI also allows the medicalpersonnel to enter the patient’s information. Both the data receivedfrom the CCU and the data sent to the BSN can also been shown by

Fig. 9. A clean wireless ECG monitoring showing pulse rate as well.

the GUI in text or graphical formats. The physiological signals ofpatients can be accessed by medical staff anywhere in a medicalcenter as long as their computers are connected to the local areanetwork in the building. An example of live monitoring from twopatients scenario is shown in Fig. 8. It displays temperature andpulse rate information of two patients at the same time. Every sen-sor device has a unique Sensor ID and must be registered under apatient name before they are used [16]. In the event that an unregis-tered sensor node is used, all its readings received will be discardedby the BSN application. Although it is not implemented in the cur-rent prototype, a warning signal could be generated by the BSNapplication to warn the health profession to track the sensor nodetrying to send a signal. This feature will strengthen the reliabil-ity and safety in the implementation which would be useful forpatients.

The monitoring of the continuous signal like ECG, EMG and EEGis more complicated compared to parameters such as pulse rateand temperature signals. Unlike temperature and pulse rate, theinformation sent to the CCU requires a continual and undisturbedsampling period as high as 400 samples per second. Each samplewill generate a data rate of 4000 with a sampling size of 10 bits. Toallow for MAC overhead and packet retransmission, the baud rate(i.e. data rate) for the RF link should be at least twice this rate. Thetransceivers used in this prototype are able to transmit such datarates.

Fig. 9 is an ECG signal obtained from our set up. Each sensornode representing only one patient can only have one ECG. In orderto eliminate the DC noise (50 Hz/60 Hz interference) a recursivefilter taken from [36] has been software implemented to obtain anaccurate ECG signal. Shown in Fig. 10, by clicking on 50 Hz filter,the recursive notch filter operates on the received ECG signal.

A database server has been developed to maintain data integritywhich is necessary for big medical centers. A monitoring of ECGfor an individual particular is shown in the Fig. 10. In the GUI, amore detailed of patients’ particulars can be seen from the database.Clicking on a patient shows the sensors attached to them andtheir personal information/picture. Clicking on a sensor displaysthe sensor’s information (e.g. interval). It also depicts the number ofrecordings that are available. Single clicking a record shows whenthe record was made. Double clicking displays the record on thegraph. In the following figure (Fig. 10), it is arranged for an ECG



Fig. 10. An example of live monitoring of ECG via database. Note that ECG electrodes were reversely polarized during measurement taken.

monitoring. It is very handy to have a data file compatible withMatlab program for signal analysis as mentioned earlier.

5. Performance evaluation and discussion

One way to analyze the performance of a WBAN system is tomeasure the end-to-end delay during monitoring of patients. Theend-to-end delay performance herein refers to the time taken for apacket to be transmitted from the sensor nodes to the monitoringcomputer. In order to evaluate the performance of the proposedWBAN, we have analysed the data received from an active patient(subject) when moving from a CCU towards other patients, asshown in Fig. 11. To observe the end-to-end delay of sensor read-ings from this patient body, we checked the arrival times of the

Fig. 11. A block diagram of real-time multi-patient monitoring to measure the per-formance of the WBAN system.

Fig. 12. Monitoring screen: monitoring, text data at the remote PC, estimating data arrival times from stored data in database.



Fig. 13. Data arrival times at the remote computer.

stored data (i.e. the received data) at the remote PC. Fig. 12 showsan example of our monitoring signals and the stored data (as a text)where the arrival times can be observed. Difference between tworeadings in time will be the end-to-end delay performance of thesystem.

At some point, we could observe a couple of different datareadings in the same second, which indicates that the delay per-formance is less than one second. We have calculated the valueof the delay simply dividing one second (i.e. 1 s) by the numberof data readings seen in a second for such occasions. In our testenvironment, we have measured the delay performances for dis-tances of 1.5, 3, 5, 10 and 15 m. At these distances, the activesubject was very close to other patients, that is where the maxi-mum collision between sensor nodes could happen. The monitoringand the recording of the proposed WBAN system are done whenthe Wi-Fi link and a few Bluetooth devices are operating in theenvironment. These devices will not affect our prototype becausethe transmission frequency of our prototype is different. Selectingmedical bands for a WBAN application is one of the advantages toeliminate external interferences. The measurement is taken in aroom 15 m long and 4 m wide.

We have implemented error-checking code in our CCU whereonly a correct packet is accepted. An erroneous packet is discardedand resubmission is requested which is also part of the CSMA/CAprotocol explained in Section 3.2. In a CSMA/CA protocol, a sen-sor node sends an RTS signal to the CCU. The RTS signal includesthe ID of the sensor node. Once the CCU sends a CTS signal backto that particular sensor node, a communication link is then estab-lished. Other sensor nodes in the environment will also read theseRTS/CTS signals and will back off for a period of time3 if the channelis not free. We also used a packet error-check mechanism (using thechecksum field of the data packet) for the communication protocolbetween the gateway CCU and the local PC. If the checksum fails, thePC will ask for a retransmission using the NACK (negative acknowl-edgement) packet else an ACK packet is transmitted for a successfuldata receiving process, in the similar way done in CSMA/CA.

The medical signal in our WBAN system will not be recorded ormonitored unless it is a correct package. The packet error rate how-

3 During these backoff periods, the sensor nodes can operate in “sniff mode”(seeTable 3) to reduce power consumption. In sniff mode, the sensor node does not sendany signal, it is only in receive mode, by trying to receive a simple signal like ACK orNACK to follow the channel.

ever increases the number of retransmission and thus will increasethe end-to-end delay to display correct medical data [35]. A packetmight be corrupted either due to the transmission error caused bya low SNR (a parameter related to the distance) or due to a colli-sion when two or more transmissions overlap in time. When theactive subject moves close to other subjects (Fig. 11), the packetfrom the desired subject will be corrupted at a higher rate due tothe transmission channel condition which requires retransmissionsif there is overlap between two patient signals. Especially theseevents occur frequently when the sensor node is at a far distanceaway from the CCU. This has particularly been observed at the dis-tance of 15 m. At this distance, we were not able to see any databeing recorded, when the active sensor node is blocked by the activepatient’ body (when the patient turns his back to the CCU). This isexpected because at this distance, the received data power level isless than the receiver’s sensitivity given in Table 3.

In our test environment, when the active subject is around othersubjects, he has been asked to do few routine actions such as sitting,standing, facing, and turning back with the same amount of time atthe distance of 1, 1.5, 3, 5, 10 and 15 m. This routine action has beenrepeated at least ten times. And the average and worst case delayshave been recorded and plotted in Fig. 13. The worst case delaywas observed as 20 s when the distance between the active subjectand the gateway CCU is 15 m. A medical data can be monitored lessthan 300 ms when the active subject is within distance of 1m andfacing the CCU. When the active patient is doing all sort of activitiese.g. sitting standing, turning around the other patients, the averagedelay is 1 s up to 5 m and 2 s at the distance of 10 m. These valuesshow that our WBAN system exhibits a correct, timely and reliablecommunication performance up to 10 m with a time performanceless than 2 s for a multi-patient scenario. We have observed thatunder the worst condition the average retransmission rate is 5 fora distance up to 10 m. The distance of 10 m has been our initialtarget during the development of the boards. Such a distance issufficient for multi-patient monitoring. Usually the CCU is targetedto be placed within 2 m of the patients. However having 10 m willprovide patient more flexibility and free movements in hospitalenvironment.

5.1. Comparison of WBAN systems

This section discusses and compares the existing WBAN sys-tems in the literature in terms of categories given in Figs. 1 and 2.The goal here is to compare these existing systems in terms their



Table 4Comparisons of current implemented WBAN systems from literature.

WBAN systems Wireless device Sensor Categorya Comments

Jovanov et al. [3] ZigBee (2.4 GHz) Activity sensor Similar to Fig. 2(a) The control device connects with a computerfor data display directly or wirelessly using aWi-Fi link. It is a multi-hopping system withmulti-sensors; however one patient data hasbeen implemented. No internet transmissionhas been indicated

Gao et al. [9] IEEE 802.15.4(MICAZ)

Pulse oximeter,blood pressure

Fig. 2(a) Sensor nodes directly communicate with atabled PC. A secure web portal to allow sharingreal-time patient information (Internetconnection)

Anliker et al. [12] GSM Blood pressure,SpO2, one lead ECG

Figs. 1(a) and 2(a) Multiple sensors have been integrated in onehandheld device

Park et al. [17] nRF24E1 radio(2.4 GHz)

ECG Fig. 2(a) One of the smallest custom-made sensor nodesin the literature. The ECG sensor communicatesto a computer wirelessly or a base station withWi-Fi link. No multi-patient scenario

Espina et al. [38] IEEE 802.15.4(2.4 GHz)

ECG, PPG, bloodpressure

Fig. 2(a) Single user data is presented

O’Donovan et al. [42] ZigBee Motion, bloodpressure, ECG

Fig. 2(a) Data is sent to a base station (computer)wirelessly. No multi-patient data is presented

Chen et al. [41] IEEE 802.15.4(ZigBee)

8-Channel EEG Figs. 1(a) and 2(a) Single patient. Internet data transmission isprovided. No wireless gateways

Zhang and Xiao [40] Bluetooth (2.4 GHz) ECG Fig. 2(a) and (b) Although it mentions it is done for two personsECG, one continuous ECG monitoring data isshown. The control device is directlyconnected to a home computer via USB (nowireless gateways). No data transmissionthrough Internet

Huang et al. [39] ZigBee (2.4 GHz) No sensor Fig. 2 (a) Only one ZigBee device is used tocommunicate with an existing IEEE 802.11gdevice to measure interference in terms of BER.Does not provide monitoring signals

Yuce et al. [28] UWB (4 GHz) 8-ChannelECG/EEG

Fig. 2(a) The design targets monitoring ofmulti-channel continuous physiologicalparameters for implantable and wearablesystems. No wireless gateways

This work MICS/WMTS Pulserate/temperature/ECG/EEG

Fig. 2(a)–(c) The system has been designed to operateaccording to Fig. 1(b) and all the scenariosgiven in Fig. 2. Internet connection for longrange

a Fig. 2(a), single body monitoring; Fig. 2(b) and (c), multiple body monitoring.

suitability for a large-scale implementation in a medical environ-ment. Comparisons of some of WBAN systems from literature arelisted in Table 4. The column “Category” indicates whether the sys-tem demonstrates a multi-patient or a single patient monitoring.In Espina et al. [38] presents a IEEE 802.15.4-based wearable body-area network that monitors continuous cuff-less blood pressureand ECG signals. The sensor data was measured on a single user.Date taken from a sensor is displayed on a PDA or a wristwatchdevice. In Ref. [9] sensor nodes directly talk to a tablet PC, a designsimilar to Fig. 2(a), and the stored data can then be seen by a secureweb portal by medical professions. Anliker et al. [12] designed awrist-worn device to measure multiple physiological parametersfrom one person. Sensors like blood pressure, SpO2, one lead ECGall have been integrated in one handheld device. The data is thentransmitted using a GSM data link to a computer similar to Fig. 1(a).The device acts the same as a regular cellular phone since it oper-ates with the GSM network. However measurements more than onepatient at the same time have not been provided. Another system inRef. [17] designs very small wearable (probably one of smallest cus-tom made sensor nodes) ECG sensors that communicate wirelesslywith a base station connected to a computer. The system works sim-ilar to a multi-hopping if a Wi-Fi link is used. Similar to previousones, this system also does not present any data for multi-patientmeasurements and monitoring. Data presented is from a scenariosimilar to Fig. 2(a).

The available systems present medical monitoring mostly from asingle body which is more suitable for homecare. Although it is one

of the first and earlier WBAN implementations, E. Jovanov, 2005 in[3] has provided monitoring results for multi-sensors from a multi-hoping wireless link. To implement a WBAN system in a medicalenvironment, the future WBAN systems should make sure that theimplementation will operate the scenarios defined in Fig. 2(b) and(c). Real-time and simultaneous multi-sensor and multi-patientcommunication is required for future WBAN systems. In additionto real-time multi-patient monitoring capability, our system pro-vides data transmission through the Internet allowing access topatient data at remote stations. It will be important for futureWBAN systems to evaluate their design for multi-patient environ-ment especially devices using a wireless platform at 2.5 GHz ISMband, due to the existing of the other wireless devices and equip-ments at this frequency. Such a validation will be crucial. FutureWBAN systems should also develop small size sensor boards, asin Ref. [17], specifically targeted for WBAN applications to meetthe size and power constraints. Most of the current system usesthe commercial available ZigBee board which is designed for manyother applications and thus is not entirely wearable.

6. Conclusions

In this paper we present a multi-hopping network for a WBANsystem that can be used in medical environments for remote moni-toring of physiological parameters. The system is different than theexisting implemented ones in that we consider monitoring of phys-iological signals from many patients simultaneously to represent a



real implementation in hospital environments. Issues related to acomplete WBAN system to deploy in a medical center have beendiscussed. Portable and wireless gateway nodes are used to connectthe sensor nodes to the local area network or the Internet alreadyavailable in medical centers. This allows medical professions toaccess patient data anywhere in a medical center or even if they areaway. Such a wireless body-area network system is very suitableto be used in hospitals environments to reduce human errors, toreduce health care cost, to provide more time to health profession-als as well as to increase patients’ comfort level as they no longerneed to be wakening up for periodic checks.

A discussion of wireless technologies for use in a WBAN applica-tion has also been given in this paper. We are working to interfaceour MICS/WMTS devices with other wireless standards such asWLANs, ZigBee (IEEE 802.15.4), mobile networks (e.g. GSM) orBluetooth (IEEE 802.15.1) in order to extend the applications in dif-ferent environments. Such a heterogonous wireless network canhelp to coordinate collaborations across medical centers. Imple-menting a wireless body-area network will require integration ofexternal and implanted nodes. Miniaturization of the sensor nodeelectronics especially the sizes of the microcontroller, the wire-less chip, the battery and low power consumption are currenthardware related issues for small sensor nodes in a WBAN. Thesuccessful implementation of a complete WBAN system shouldoperate and co-exists with other network device and should pro-vide: wearable, wireless (no wire connections), easy to remove andattach sensor nodes, leading to increased mobility of patients andflexibility.

Acknowledgements

This work was supported in part by the Australian ResearchCouncil (ARC) under Discovery Projects Grant. I like to thank the fol-lowing students Anthony Bott and Ng Peng Choong for their help inthe developing boards. I also like to thank the anonymous review-ers for their comments and suggestions to strengthen the qualityof the paper.

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Biography

Mehmet Rasit Yuce received the M.S. degree in Elec-trical and Computer Engineering from the University ofFlorida, Gainesville, Florida in 2001, and the Ph.D. degreein Electrical and Computer Engineering from North Car-olina State University (NCSU), Raleigh, NC in December2004. Currently he holds senior lecturer position in theSchool of Electrical Engineering and Computer Science,University of Newcastle, New South Wales, Australia.

Between August 2001 and October 2004, he has served as a research assistantwith the Department of Electrical and Computer Engineering at NCSU, Raleigh, NC.He was a post-doctoral researcher in the Electrical Engineering Department at theUniversity of California at Santa Cruz in 2005. His research interests include wirelessimplantable telemetry, wireless body area network (WBAN), analog/digital mixedsignal VLSI for wireless, biomedical, and RF applications. Dr. Yuce has publishedabout 50 technical articles and received a NASA group achievement award in 2007for developing an SOI transceiver. He is a senior member of IEEE.

Documents

Author's personal copy - Electrical and Computer … low-cost, low power consumption, and providing exibil-ity to the patients [5,6]. A WBAN-based wireless medical sensor network system