Wireless Sensing Systems in Clinical Environments

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Wireless Sensing Systemsin Clinical Environments

Multiple studies suggest that the level of patient caremay decline in the future because of a larger agingpopulation and medical staff shortages. Wirelesssensing systems that automate some of the patient

monitoring tasks can potentially improve the efficiency of patientworkflows, but their efficacy in clinical settings is an open ques-tion. In this article, we introduce the challenges that such wirelesssensing systems must overcome and provide insights on the tech-niques and features that system designers should consider for suc-cessful deployments in clinical settings. We do so throughMEDiSN, a wireless sensor network (WSN) designed to continu-ously monitor the vital signs of ambulatory patients. We validatethe usefulness of MEDiSN with test bed experiments and resultsfrom a pilot study performed at the Emergency Department,Johns Hopkins Hospital. Promising results indicate that MEDiSNcan tolerate high degrees of human mobility, is well received bypatients and staff members, and performs well in real clinicalenvironments. We leverage our experience from this hospital pilotstudy to outline outstanding issues and argue about the steps nec-essary to bring wireless sensing applications to commercial use.

Wireless Sensor NetworksAn increasingly aging population combined with nursing staffshortages [1] and decreasing hospital capacities [2] suggest thatmaintaining the current level of hospital care is becoming anincreasing challenge. Inefficient and labor-intensive procedures,such as recording the patients’ vital signs periodically, included incurrent hospital workflows are some of the underlying causes forthe mismatch between health-care capacity and demand. Therefore,mechanisms that can automate some of these manual tasks havethe potential to improve the efficiency and quality of patient care.

WSNs, comprising small, low-power, and wireless devicesequipped with sensors and actuators, are ideal candidates forautomating the repetitive procedures currently performed byhospital staff. Some of the clinical tasks that WSNs can performinclude continuous monitoring of unattended patients duringroutine and extreme event surges, as well as monitoring of intra-hospital transports and pediatric patients. Although the need forthese applications is widely accepted, there has been little prac-tical experience with deploying them in clinical environments.

In an effort to address this disconnection between technol-ogy and practice, we assembled an interdisciplinary team ofacademic researchers, industrial developers, and medicalpractitioners to develop the MEDiSN wireless monitoring sys-tem for tracking the vital signs of ambulatory patients. Ratherthan an end product, MEDiSN is a research vehicle for under-standing the challenges associated with deploying wirelesssensing applications in the hospital. At the same time, MED-iSN is a fully functional system whose goal is to showcase thebenefits of wireless sensing to physicians.

We deployed MEDiSN as part of an IRB-approved study at theEmergency Department, Johns Hopkins Hospital, to monitorpatients waiting to be seen at the emergency room (ER). Resultsfrom this deployment combined with findings from a controlledenvironment indicate that wireless sensing applications can suc-cessfully meet many of the challenges related to automated patientmonitoring, including reliable data delivery in the face of adverseradio-frequency (RF) environments and mobility support. At thesame time, the lessons we learned from this exercise, including thefeedback we collected from patients and medical staff, have helpedus to identify outstanding issues that need to be resolved beforesuch systems can be commercialized and used on a daily basis.

WSNs in Clinical EnvironmentsWSNs consist of low-power, embedded computing devices,known as motes, that use sensors to collect measurementsfrom the physical world and its inhabitants. Wireless sensinghas been successfully employed in a variety of applicationsincluding environmental monitoring [3], structural monitoring[4], and surveillance [5]. These early successes have moti-vated researchers to apply the same technology in clinicalenvironments in an effort to increase the quality of care on aday-to-day basis as well as during extreme events. The subse-quent sections outline a few potential applications and identifythe novel challenges these medical applications introduce.

Medical Sensing Applications

Monitoring Unattended PatientsBecause of space and manpower limitations, many patientsvisiting a hospital’s ER spend multiple hours in waiting areasbefore they are admitted to care units. Although wait times are

BY JEONGGIL KO, TIA GAO,RICHARD ROTHMAN,AND ANDREAS TERZIS

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Improving the Efficiency of thePatient Monitoring Process

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shorter for patients with urgent needs and waiting patients areperiodically retriaged, there have been reports of patientswhose condition deteriorated while waiting in the ER [6], [7].Patient monitoring, whereby motes continuously record andtransmit the patients’ vital signs, can prevent tragic incidentsby alerting health-care providers of patient deterioration.

Surge Capacity MonitoringSurge events during which a rapid influx of patients over-whelms the hospital’s capacity exacerbate the problem ofmonitoring unattended patients. Systems that automate thepatient triage and monitoring processes can save people’slives during these critical times.

Monitoring of Intrahospital Patient TransportsExisting devices for monitoring patients’ vital signs are bulkyand cumbersome to use during patient transports within thehospital. Therefore, a wireless vital signs monitoring systemwith the added ability to track the location of patients insidethe hospital will improve patient safety during such transports.

Pediatric Patient MonitoringPediatric patients using patient-controlled analgesia (PCA)devices are prone to overdose [8]. However, patients whooverdose can be automatically identified by continuouslymonitoring their blood oxygen levels and the flow of analgesiacan be immediately discontinued while physicians are alertedof the emergency.

Challenges of Medical Sensing Applications

Supporting Large-Scaleand Easy to Deploy NetworksIn many scenarios, including natural and man-made disasters,wireless sensing systems should be able to collect data from alarge number of patients dispersed around the hospital. There-fore, the system should be able to scale to wide areas and beeasy to expand even to areas without preexisting wireless net-work infrastructure.

High Data-Rate NetworksIn addition to supporting large number of users, medical sens-ing applications should be able to support high volumes oftraffic generated from high data-rate sensors such as electro-cardiogram (ECG) and respiratory sensors.

Patient MobilityPatient mobility is very common in many clinical scenariosincluding patients in the ER and intrahospital patient transports.For this reason, medical sensing applications should providehigh reliability without restricting the patients’ movements.

Patient LocalizationTracking patient locations is not only important during thetransport of patients admitted to the hospital but also simpli-fies the process of locating patients in the ER waiting room.Doing so would reduce staff workload and improve the effi-ciency of patient-care workflow.

Soft Real-Time Data Deliverywith Any-to-Any RoutingMedical applications require low latency and high reliabilityto ensure that medical staff can make clinical decisions basedon up-to-date patient information. Furthermore, the measure-ments must be reliably delivered to all the caregivers who areassociated with a certain patient.

SecurityAccording to U.S. law, medical devices must meet the privacyrequirements of the 1996 Health Insurance Portability andAccountability Act (HIPAA). To meet these requirements, thesystem must never broadcast identifiable patient data andguarantee the authenticity of the data it delivers.

MEDiSNWe developed MEDiSN [9] as an experimental vehicle forinvestigating the feasibility of wireless vital signs monitoringin clinical environments. In collaboration with our partners atJohns Hopkins Hospital, we selected the task of monitoringunattended ER patients as the first application for MEDiSN.From a technical standpoint, MEDiSN must securely and reli-ably deliver the vital signs of ambulatory patients. Further-more, MEDiSN must be able to easily extend to a larger area(e.g., tents outside the hospital) during a surge event.

Figure 1 shows the three-core network components ofMEDiSN. Figure 1(a) is a picture of a patient monitor (PM),known as a miTag. The miTag’s processing core is the SentillaTmote Mini, which combines a Texas Instruments MSP430microcontroller with a TI/Chipcon CC2420 IEEE 802.15.4[10] radio in a mini-secure digital input/output (SDIO) formfactor [11]. The miTag includes an off-the-shelf sensor thatrecords pulse rate and blood oxygen levels [12] and integratesactuators including a buzzer, a 1.8 in liquid-crystal display(LCD) and five light-emitting diodes (LEDs). It collects apatient’s vital signs through a finger clip shown in Figure 1.Before transmitting these measurements using its on-boardradio, the miTag encrypts and signs them using the securityprimitives that the CC2420 radio offers [13].

The PMs transmit the samples they collect to one of the net-work’s relay points (RPs), shown in the center of Figure 1.Unlike previous medical sensing designs (e.g., [14]), MED-iSN includes a dedicated wireless backbone that forwardsthe PMs’ data. Specifically, the RPs use the collection-tree

MEDiSN is a fully functional system whose

goal is to showcase the benefits of wireless

sensing to physicians.

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protocol (CTP) [15] to self-organize into a routing tree thatrelays packets to the gateway [Figure 1(c)], where the data canbe delivered to any destination via the Internet.

While the performance of CTP is not within the scope ofthis work, interested readers can find extensive evaluations ofthis protocol in [9], [15], and [16]. Having this separate infra-structure means that PMs do not need to relay other PMs’ data,as in other ad hoc networks. As a result, PMs can aggressivelyduty cycle (i.e., turn off) their radios to conserve energy. (Thecurrent miTag can be used for approximately five days whiletransmitting vital sign updates every 30 s without rechargingthe battery. Our clinician partners require this device lifetime.)Moreover, the dedicated backbone does not suffer from thefrequent path reconfigurations inherent in mobile ad hoc net-works and thereby offers better service to mobile PMs.Finally, having a multihop backbone simplifies the task ofexpanding the system’s coverage area. Doing so requires tosimply add more RPs to the existing backbone. These RPsautomatically join and seamlessly extend the existing MED-iSN backbone.

Rather than broadcasting packets to all RPs in its communi-cation range, each PM associates with one of the network’sRPs using a simple RP selection scheme (the interested readercan find more details about the association mechanisms thatthe PMs use in [9]). Doing so improves the network’s effi-ciency by suppressing the delivery of duplicate packets overthe RP backbone. The proposed RP selection scheme is robustto PM movements, promptly allowing a PM to associate withthe next best RP after it disconnects from its previouslyselected RP. To achieve high reliability, RPs and PMs inMEDiSN perform hop-by-hop retransmissions. We evaluatethe performance of this two-tier network and the robust RPselection scheme using an indoor test-bed experiment in the‘‘Supporting Mobile PMs’’ section.

The RPs not only forward the PMs’ data but also collect andperiodically report metadata regarding the quality of the back-bone’s wireless links. Thisinformation can be used to opti-mize the networks’ perform-ance by adjusting the numberand the locations of its RPs.

Deployment Results

Supporting Mobile PMsBefore deploying MEDiSN inan actual clinical environ-ment, we performed a test bedexperiment to validate its per-formance with mobile PMs.Since patient movement is

common in the clinical settings that MEDiSN targets, per-forming well in this experiment was essential for the system tobe deployed in hospitals. This experiment was conducted ontwo floors of a seven-story building. We deployed RPs on thefirst and fourth floor hallways, each with an approximatelength of 170 ft. The RPs on the two floors were connectedusing RPs on the building’s stairwell. In total, we used onegateway and 25 RPs to cover the entire area.

We tested two types of configurations in this test bed. First,we tested a two-tier MEDiSN network using RPs acting as thededicated wireless backbone network with PMs implementingthe RP selection mechanism described earlier. Second, a flatnetwork in which the PMs participate in the same ad hoc net-work with the RPs was tested. We used CTP to route packetsover this single-tier (i.e., flat) network for both RPs and PMs.The mobile PMs moved at a walking speed for approximately8 min over a fixed route, spanning approximately 1,000 ft thatcovered both floors. In each case, we formed a control groupby placing stationary motes at identical locations for 8 min.All PMs sent one packet per second.

Figure 2 presents the distribution of the times that the PMswere disconnected from the network. We determine that a PMis disconnected from the network if packets from the PM arenot received at the gateway when packets are expected (thegateway expects one packet each second from all PMs). In thecase of the MEDiSN network, there were ten PM disconnec-tions due to mobility. Furthermore, all disconnections lastedfor less than 10 s. On the other hand, the mobile PM in the flatnetwork experienced 20 disconnections that lasted as long as60 s. The overall packet reception ratio (PRR) was 96.06% forthe mobile MEDiSN PM and 62.45% for the mobile PM in theflat network. We note that the PRR has been computed withrespect to the application-level performance (number of validpackets received at the gateway divided by the total number ofpackets that the PM sent) in all cases. Finally, while the mobileMEDiSN PM made on average 1.9 transmission attempts per

MEDiSN is a research vehicle for

understanding the challenges associated with

deploying wireless sensing applications

in the hospital.

(a) (b) (c)

Fig. 1. (a) A medical information tag, or miTag for short, that collects patients’ vital signs, (b)relay point that forwards vital sign measurements over the MEDiSN wireless mesh, and (c) thegateway that collects all the measurements.

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successful packet delivery, the CTP-based mobile PMrequired an average of 2.41 transmissions for each successfulpacket delivery.

These results support our intuition that having a wirelessbackbone improves data delivery as well as efficiency whenPMs are mobile. We also note that the reception ratios for thestationary PMs were 100 and 99.80% for the flat and MEDiSNnetworks, respectively. The discrepancy between the deliveryratios for the static and mobile PMs in the case of the flat net-work can be explained as follows. When the network is staticand CTP can collect all the link quality measurements for dif-ferent neighbors, a node can find alternative high-quality end-to-end paths when link failures occur [16]. On the other hand,when movements occur in a mobile network, topology recon-figurations happen faster than the time that CTP needs to col-lect neighborhood information and update routes, leading tosevered end-to-end paths and lower data-delivery ratios.

Low-Power Wireless Performance at the ERSince MEDiSN includes a dedicated wireless RP backbone, abackbone setup phase was necessary at the beginning of ourhospital deployment. Although the deployment site (see Figure3) is not large in terms of its total area, setting up a wirelessbackbone in the hospital can pose several challenges. First, thehospital is a clutter-rich environment with obstacles such aslarge glass and aluminum dividers, thick steel doors, and lead

painted walls (see Figure 4), which severely distort RF signals.Second, RP locations are constrained by the availability of wallplugs, as RPs are powered by the hospital’s electricity grid. Thealternative of using batteries to power the RPs would requirethe hospital personnel to replace batteries frequently, as the RPscannot duty cycle their radios. Third, given the fact that the ERis busy 24 h a day, movement by humans and equipment acrossthe deployment area leads to a highly variable RF environment.

To further evaluate the impact of these factors on the wire-less channel environment, we compared the RF environment atthe ER to the environment of an indoor test bed located at anoffice building. We performed tests for each of the two envi-ronments on IEEE 802.15.4 channels 22 and 26 over the same24-h period. (IEEE 802.15.4 channel 22 overlaps with WiFichannel 11, which is active in both environments. On the otherhand, channel 26 is free from WiFi interference.) We selecteda transmitter–receiver (T-R) pair in the ER approximately 64 ftapart from each other (see Figure 3). The transmitter broad-casted one 111-B packet every 500 ms without performing anyCCA checks. [Carrier sense multiple access protocols performclear channel assessment (CCA) checks to make sure the wire-less medium is idle before each transmission.] The receiverlogged the received signal-strength indicator (RSSI) values ofincoming packets. Furthermore, the receiver took signal-strength measurements between packet receptions to measurethe ambient noise level of the environment. We performed thesame test in an indoor test bed using a link with the same dis-tance and a line-of-sight path.

Figure 5 presents the 24-h plot of packet RSSI values andthe ambient noise levels collected at the ER [Figure 5(a) and(b)] and the indoor testbed [Figure 5(c) and (d)] for the twochannels we tested. While differences in channel characteris-tics from different environments are expected to a certaindegree, Figure 5 suggests that the ER environment exhibitssignificantly higher RSSI variation compared with the indoortest bed channel across both frequency channels. We conjec-ture that changes in the physical environment (i.e., movementof people and equipment) are the root cause of these variationsin signal strength.

Deploying a wireless network in an environment with suchvariations in link conditions requires careful consideration. Aswith any other wireless network deployment, a hospitaldeployment starts with a site survey of the deployment area.Through this process, we select the locations that minimize thetotal number of deployed RPs, while ensuring good connectivitythroughout the ER waiting area. To do so, we set up a gatewayand gradually add more RPs to the site as we find coverage deadspots. We use a combination of visual cues and networkmeasurements to identify these dead spots. The process of instal-ling the RP backbone takes less than 30 min for an area of thesize shown in Figure 3 (approximately 70 3 90 ft). Moreover,

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Fig. 2. Comparing the performance of the two-tier MEDiSNnetwork to a flat ad hoc network that uses CTP. In bothcases, mobile PMs follow the same path over a multifloortest bed, moving for approximately 8 min. While PMs thatuse MEDiSN experience a small number of short disconnec-tions, PMs in the flat network experience multiple and pro-longed disconnected periods. The static PM in the flatnetwork experienced no disconnections.



one can use the same methodto extend the network’s cover-age or to accommodate varia-tions in wireless link quality byadding more RPs to the back-bone. The fact that we caneasily improve network per-formance and extend the wire-less network’s coverage is abig advantage of having awireless backbone.

After deploying the RPbackbone, we admitted a totalof 46 patients to a pilot study(see the ‘‘Patient and StaffAcceptance at the ER’’ sec-tion). During the deployment,a maximum of three patientswore miTags at any one time.An average of 5,341 packetswere transmitted from eachpatient’s miTag. Of these, anaverage of 244 packets werelost, leading to an averageapplication-level PRR of95.43% with a standard devia-tion of 5.41. We conjecturethat the high variation in PRRis due to the significantly dif-ferent behaviors among thepatients that wore the miTags.While most patients used the miTag properly, some held ittight in their hands or covered it with thick jackets. Changingthe antenna design to overcome these adverse conditions is partof our future work.

Patient and Staff Acceptance at the EROur IRB-approved pilot study took place at the ER of Johns Hop-kins Hospital, Baltimore, Maryland, spanning a period of tendays during November/December 2008. The objective of thisstudy was to measure the level of acceptance of a wireless sens-ing system such as MEDiSN by ER staff and patients. At thesame time, this study provided us with the opportunity to evalu-ate the application’s performance in a realistic environment.

All adult patients with triage-level assignments of 3–5 (on a1–5 scale, where 1 is the highest acuity), who were sent to theER’s waiting room after their initial triage were eligible to par-ticipate in the study. A total of 46 patients volunteered to par-ticipate. All of them had triage level 3, the mean age was 48.2,and 33% of the volunteers were male. Moreover, 33.3% ofthem were African American, 0.6% were Asian, and the restwere Caucasians or Latin Americans. Chest pain was the mostcommon chief complaint (52.2%) among the volunteers. Fig-ure 6 illustrates how the patients wore the device around theirneck using a lanyard. Patients wore the device throughouttheir stay in the waiting area. We collected pulse rate andblood oxygen level measurements every second using single-use, disposable pulse oximeter clips. At the end of their stay,volunteers were asked to rate their satisfaction level on a scaleof 1–4, 4 being most satisfied with MEDiSN. The averagepatient satisfaction level was 3.47, and 91% of them indicatedthat they would be willing to use the device in the future.

We also surveyed a total of 21 staff members, 38% of whomwere resident nurses and the average numbers of years thatthey worked at the hospital was 8.5 years. Computers on the tri-age desks and nursing stations were capable of displayingMEDiSN’s graphical user interface (GUI), and staff memberswere able to use the GUI to retrieve patients’ information. Wegathered staff-satisfaction surveys when staff members enteredthe rest area during the day. Overall, the average staff satisfac-tion score for our system was 3.11 on a 1–4 scale.

Fig. 4. View of the waiting area at Johns Hopkins Hospitaladult ER as seen from the triage desks. Aluminum dividers andglass walls act as obstacles, disrupting RF signal propagation.

Johns Hopkins HospitalEmergency Room

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Fig. 3. Floor plan of Johns Hopkins Hospital ER. RPs are placed in the waiting area, while thegateway is located at the nurse-triage desks. MEDiSN was deployed to cover an area of70 3 90 ft.



DiscussionNext, we present some of the comments provided by the staffmembers that we surveyed during the pilot deployment.

Work-Flow IntegrationMany staff members commented that the current ER workloadis already overwhelming. Thereby, any device introduced atthe ER should not impose additional burden to overworkedstaff members. One way to achieve this goal is by properlyintegrating new devices to existing workflows.

Staff EducationAnother common concern from the staff was that the GUI at themonitoring station was not intuitive. Some of these commentswere because staff members were not trained to use the inter-face. Nonetheless, developing easy-to-use interfaces for envi-ronments with high-cognitive overload is anongoing challenge.

LocalizationMany staff members indicated that a methodfor tracking the patients’ location would bebroadly useful. For example, nurses spend anontrivial amount of time trying to identifyand locate patients in the hospital’s waitingroom. While indoor localization is generallyconsidered a challenging task, ranging-basedapproaches using ultrawide band (UWB)transceivers offer promising results. Consid-ering that a recent amendment to the IEEE802.15.4 standard describes a radio thatcombines communication and UWB rangingand that early implementations of the newstandard are starting to emerge [17], practi-cal indoor localization may be within reach.

Avenues for CommercializationWe envision that hospitals will be the primaryusers of wireless vital sign monitoring systems,

such as MEDiSN, because of their potential for decreasing staffworkload, increasing patient safety, and decreasing the duration ofhospital stays. Furthermore, we expect that these systems will beused to monitor disaster victims at the scene and at field hospitalsin war zones.

Nevertheless, a number of issues need to be resolved beforeWSNs for medical sensing can be productized. First, there iscurrently a lack of sensors (e.g., blood pressure and ECG) thatcan be used with low-power wireless devices. Existing sensorsare too bulky and power-hungry to be used with portable devi-ces such as the miTag. More importantly, longer clinical stud-ies are necessary to quantify the benefits that WSNs bring toapplications such as those described in the ‘‘WSNs in ClinicalEnvironments.’’ These studies should also produce amend-ments to existing clinical workflows that incorporate wirelessmedical sensing systems with the minimum level of disruption.

SummaryIn this article, we examine the potential ofWSN technologies to improve the effi-ciency of the patient-monitoring process inclinical environments. We describe severalapplications that can benefit from low-power WSNs and introduce the technicalchallenges that such systems should over-come before they are widely deployed.With these challenges in mind, we designMEDiSN, a WSN-based research vehiclethat monitors the vital signs of unattendedpatients and present performance resultsfrom both test bed experiments and a pilotstudy performed at Johns Hopkins HospitalEmergency Department. Despite the chal-lenging wireless channel conditions preva-lent in clinical environments, we show thatMEDiSN can successfully accomplish itstask of continuously monitoring unattendedpatients’ vital signs. These encouragingresults along with the positive feedback

Fig. 6. A triage nurse in the ER wear-ing a miTag. A lanyard connectsthe miTag to the patient’s neck anda single-use, disposable pulse oxim-eter clip is attached to the finger.

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Fig. 5. Packet RSSI and ambient noise levels collected at (a), (b) the ER and (c), (d) the indoor test bed.



from the user-satisfaction surveys suggest that WSN-basedsystems can contribute to the greater goal of improving theefficiency of clinical workflows.

JeongGil Ko received the B.Eng. degree incomputer science and engineering fromKorea University, Seoul, South Korea, in2007 and a M.S.E. degree in computer sci-ence at Johns Hopkins University, Baltimore,Maryland, in 2009. He is a Ph.D. student inthe Department of Computer Science at JohnsHopkins University. He is also a member of

the Hopkins Internetworking Research Group (HiNRG) lead byDr. Andreas Terzis. His research interests include wireless sensingsystems for health care, embedded network system design, andthe deployment of such systems to real environments.

Tia Gao received a M.S. degree in electri-cal engineering and expects a M.B.A.degree, both from Stanford University. Sheis the founder and president of Aid Net-works. Previously, she was an engineeringmanager at Johns Hopkins University Ap-plied Physics Laboratory and managedresearch projects in the field of patient

tracking solutions for mass casualty response. Before that, shewas at Medtronic’s Cardiac Rhythm Management divisionand developed implantable cardiac-monitoring devices. Sheserved as a program manager at Microsoft Corporation anddeveloped products for Windows Mobile and Office. She hasauthored more than 20 peer-reviewed technical publicationson the topic of wireless mesh sensor networks.

Richard Rothman received his M.D. andPh.D. degrees from Cornell University andthe University of California, respectively.He is an associate professor in the Depart-ment of Emergency Medicine, Departmentof Medicine and Division of Infectious Dis-eases at Johns Hopkins University School ofMedicine. He is the research fellowship di-

rector and chair of the research committee in emergency medi-cine. He has served as a consultant for the biotechnologyindustry in molecular assay development for infectious diseases.Currently, he is a principal investigator for the diagnostics pro-gram for the Mid-Atlantic Regional Center for Excellence forBiodefense and Emerging Infectious Disease Research. He hasreceived numerous awards including the Society for AcademicEmergency Medicine Young Investigator Award. His researchinterests include basic molecular innovations for rapid detectionof biologic agents in acute care settings.

Andreas Terzis received his Ph.D. degreein computer science from the University ofCalifornia at Los Angeles. He is an assistantprofessor in the Department of ComputerScience at Johns Hopkins University, wherehe leads the HiNRG Group. He is a recipi-ent of the National Science Foundation(NSF) CAREER award. His research inter-

ests include the broad area of WSNs, including protocol design,system support, and data management.

Address for Correspondence: JeongGil Ko, Department ofComputer Science, Johns Hopkins University, 3400 N. CharlesSt., 224 New Engineering Building, Baltimore, MD 21218,USA. E-mail: [email protected].

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Existing devices for monitoring patients’ vital

signs are bulky and cumbersome to use during

patient transports within the hospital.



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Wireless Sensing Systems in Clinical Environments