WIRELESS SENSOR NETWORKS FOR MEDICAL SERVICE Tsenka Stoyanova1 and George Papadopoulos2 1

Applied Electronics Laboratory, University of Patras, 26500 Rion-Patras, Greece 2

Industrial Systems Institute, Platani, Patras, Greece, {tsstoyanova, papadopoulos}@ee.upatras.gr

Abstract: The present work surveys and classifies various applications of the Wireless Sensor Networks (WSNs) technology in bio-medical service. A review of the most popular hardware platforms and frequently-used sensor nodes is presented, as well. A discussion of the most promising research directions concerning the involvement of WSNs in bio-medical service is offered. I.


WSN became an important technology, which is being widely used in variety of applications such as: environmental monitoring, public safety, healthcare, security, transportation, etc. The WSN technology gained special attention in bio-medical applications because it opened a new ways to improving services, such as eldercare, healthcare, health monitoring, life rescue, etc, which are important acquisition of the modern society. By definition a WSN is a large number of sensor nodes, which are spread over the region of interest. The sensor nodes combine capabilities to sense the environment, process the collected data and communicate with other nodes, creating a self-organized network. In addition, the WSNs are capable to interconnect with already existing infrastructure (such as: Internet, LANs, GPS, cellular phone networks, etc) when processing beyond the capabilities of the sensor network is required, or when storage of all observation history is necessary. On-line data collecting is frequently used for continuous monitoring and assessment of the events of interest, which enables delivering the collected information on time, and reacting fast when it is necessary. In the following sections, we offer an overview, classification and brief description of the application scenarios and hardware platforms, and discussion of the impact that the WSN technology will have on the biomedical services in the near future. II. WSNS IN BIO-MEDICAL APPLICATIONS Although the contemporary technology for biomedical smart sensors is in an early stage of development, a number of real-world applications and research projects have already emerged. As it is presented in Figure 1, the variety of bio-medical applications using WSNs can be categorized in the following five groups: 1. Patient Monitoring in hospital and home environment – A straightforward advantage of the wireless sensors is to replace existing wired telemetry systems for the specific clinical applications, where long-term moni-

toring is required. • Tele-monitoring and diagnostic of human vital signals and health indicators – In a hospital or home healthcare scenario, every patient is outfitted with tiny, wearable wireless vital sign sensors, which would allow doctors and nurses to continuously monitor the status of their patients and to react on changes, such as: respiratory failure, cardiac arrest, etc. There are number of projects directed towards remote patient monitoring, such as: MobiHealth [1][2], Hourglass [3], CodeBlue[4], VitalDust [5], Personal Care Connect [18], BSN [36], etc. • Tracking of doctors and patients inside a hospital – The MoteTrack project [20] offers the opportunity for accurate location tracking of mobile objects via appropriate deployment of wireless sensor nodes in an indoor environment. 2. Disability assistance – This group of applications consider scenarios where smart sensors operate within the human body to counterpoise some disabilities of human organs or to monitor particular organ viability. • Artificial retina – The goal of the “Smart Sensor and Integrated Microsystems” project [6] is development of implanted retina prosthesis for persons without or with limited eyesight. The implant consists of smart sensor chips, each with 100 micro-sensors small and light enough to be placed upon the retina. The sensors produce electrical signals, converted by the underlying tissue into chemical response and thus substitute the real retina behaviour. The external image catching and wireless data transmission to the implanted sensor nodes is done by a CCD camera combined with real-time DSP. • Glucose level monitoring for diabetic patients [6][18] – wireless bio-medical sensors offer new and effective treatment of diabetes, by providing more reliable, accurate, and less stressing method for monitoring the blood glucose level. A wireless sensor could be implanted in the patient’s body under the skin, and then would monitor the glucose level and transmit the data to a wristwatch receiver to display the results. • Organs and cells monitoring [6] – Applications for wireless sensors directed to monitoring human organs and cells in order to examine viability of the transplant organs, monitoring and diagnosing of the digestive system problems, and early cancer detection to prevent later stage complications. 3. People rescue • Locating victims or members of rescue teams in emergency situations and disaster areas – In an emergency or disaster scenario the people are outfitted with tiny wireless badges, which could guide the rescue team

Figure 1: Classification of bio-medical application scenarios and medics more effectively to manage a larger numbers of casualties. For example, in the application scenarios described in [2] and [7], the first rescuer who arrives in the disaster area or in the area of accident with a large number of victims, would place wireless, vital-sign and locationtracking sensors on each patient. These sensors would relay continuously data to nearby paramedics and emergency medical technicians, who would use mobile PDA or mobile PC-based systems (in ambulances) to capture all patient vital data. Thus, they could monitor and care for several patients at once and be alerted to any changes in the patient’s physiologic status. That information network includes not only the rescue teams, but the hospitals information system as well, allowing a better coordination between the emergency rescue teams and the hospitals with facilities and resources for takecaring the patients in critical condition. Rescue teams, such as fire-fighters, police or mountain rescuers, also could wear smart location tracking sensors to aid rescuers in determining their own location as well as that of other members of the team [8]. 4. Bio-surveillance – All wireless sensor systems created with purpose for bio-surveillance help public health experts to determine if there is a threat of deadly disease outbreak among the human population. Series of sensors can collect and examine samples from the air, soil, and water and the environment weather condition to predict the epidemiological dissemination of the disease. It allows federal, state and local officials to react with providing quickly emergency response, medical care and consequence management needs. • Biological attack warning system – The function of BioWatch [9] is to detect the presence of biological threat agents into the air, by collecting and analyzing air samples. If any such agents were to be detected the system provides early warning to the government and public health community of a potential threat of epidemic. Such a system could be used by public health agencies to warn citizens against the presence of biological agents in the environment. • Early disease prediction – The fight with some epidemic disease like Dengue fever, Hantavirus Pulmonary Syndrome (HPS), malaria, and Lyme disease could be supported by a sensor network early warning system. Spreading a large number of wireless sensors in risky regions could help to collect crucial ground-based in-

formation, such as the amount of rainfall and humidity, to localize and predict where the local dangerous disease-carrying insect population is likely to be found. Based on the gathered air and ground data and after analyzing epidemiological information, the system generates disease “risk maps” and provides early warnings about threat of epidemic, when an increase in the harmful insect population is detected. The early warnings would allow health authorities, governments and international organizations to prevent the spread of the disease and thus protect threatened human populations from a deadly epidemic [10]. 5. Smart surrounding • Helping elderly citizens in their homes for improving the quality of life – Sensor nodes could observe the daily living of the inhabitants, sharing data with one another and with external infrastructure, where the data will be processed and analyzed. These data can give clues about the state of health and self-dependence of an inhabitant, and would allow intervention and help when such help is needed. Some recent University-based smart home projects, such as the Smart Medical Home at the University of Rochester [11], Georgia Tech’s Aware Home [12], MIT’s House_n [13], etc, and other commercial research projects [14],[15], as well as the European project Amigo [16], aim at creating a smart home environment. Among the users, which would benefit from such an environment are elderly people, who will be able to live in their homes on their own for a longer period. III. THE WORLD OF THE MICROSENSORS In the present section, we offer a brief survey of the existing WSN hardware platforms, which could find application in the above described bio-medical services. Table 1 presents a possible association between the application scenarios outlined in Section II and the sensor nodes, presented in the following: 1. Smart Dust [21] – This project aims at reducing the nodes’ size to a sub-cubic-millimetre scale, as in the same time extends the nodes’ capability with embedding sensing, processing and communication units in single chip. The first test prototype, the single-chip “Spec” [22], has a footprint of around 2 x 2.5 mm, and carries a small microcontroller core and a simple radio. 2. Berkeley MOTEs [23] is the most used sensor node

Table 1: Sensor applications and sensor nodes association ApplicationScenario Patient Monitoring Disability assistance People rescue

Bio-surveillance Smart surrounding

Captured sensor data (1)

Possible sensor nodes

vital sign (heart rate, oxygen saturation, blood (2) pressure, etc); location-tracking motion, picture; glucose level (1) vital sign (heart rate, oxygen saturation, pulse oximetry, respiration, blood pressure, etc); (2) location-tracking environmental condition (amount of rainfall, temperature, humidity, etc); insects movement (1) (2) vital sign; identity, location, 3D position and orientation, audio/speech, image/video, orientation, motion, acceleration, touch/pressure, light, temperature, etc

series, which includes the models: Mica2, Mica2dot and MicaZ [24]. These are 3rd generation, tiny, wireless smart nodes, equipped with external connectors. Through these connectors it is easy to be attached a large number of sensors which can detect light, temperature, barometric pressure, acceleration/seismic, acoustic, magnetic, etc. The possible application scenarios include: surveillance, environmental monitoring, smart homes, tracking, etc. 3. tMoteSky (formerly Telos revB) [25] – tMoteSky is a next-generation mote platform for extremely low power, high data-rate, sensor network applications. It offers 250 kbps, 2.4 GHz, IEEE 802.15.4 wireless radio, 8 MHz Texas Instruments MSP430 microcontroller, integrated onboard antenna with 50m/125m indoors /outdoors range and integrated humidity, temperature, and light sensors. Typical usage scenarios: environmental monitoring, smart buildings, tracking, etc 4. ScatterWeb platform [26] is a heterogeneous embedded platform collection, consisting of simple nodes, called embedded sensor board (ESB), and more powerful data sinks, called embedded web server (EWS). EWSs can act as gateways between the sensor field and external infrastructure (Ethernet, GPRS, Internet, etc). The ESB board integrates luminosity sensor, noise detection, vibration sensor, IR movement detection, microphone/speaker, IR sender/receiver. Typical application scenarios: environmental monitoring, smart buildings, tracking, etc. 5. uParts [27] (previously known as Smart-Its [28]) – The µPart sensor node comprises a PIC microcontroller, sensors, RF transmitter and a battery integrated on a 10x10 mm PCB footprint. The whole size of µPart sensor node is 1 cm³ including the battery. Current µPart sensor configurations are: light, tilt, temperature, motion and acceleration. The former Smart-Its board integrates sensors for audio, light level, acceleration, pressure and temperature. Typical scenarios: environmental monitoring, smart buildings, motion detection, tracking, etc. 6. BTNodes platform [29] [30] is an autonomous wireless sensor node based on a Bluetooth radio, a second low-power radio and a microcontroller. It serves as a demonstration and prototyping platform for research in mobile and ad-hoc wireless sensor networks.


VitalDust[34],BSNs[38],MIThril[32], ECOs[31] (2) ScatterWeb[26], uParts[27], BlueTag[37] Smart sensor chip [6], VitalDust[34] (1) VitalDust[34],Pluto[35],MIThril[32], MITes[36] (2)

MICAx[23], BlueTag[37]

MICAx[23], tMoteSky[25], uParts[27], (1)

MITes[36], MIThril[32], Pluto[35], BSNs[38]


tMoteSky[25], ScatterWeb[26], MICAx [23], BlueTag [37], uParts [27],

7. ECO [31] is an ultra-compact wireless sensor node – it is only 12 x 12 x 4.5 mm³ in volume and weights under 1.6 grams. Eco is initially designed to monitor the spontaneous motion of pre-term infants. 8. MIThril [32] – wearable computing research platform. MIThril 2003 system is comprised of the Zaurus PDA with Hoarder daughter boards. Two major types of Hoarder daughter boards are currently in use: (1) a multi-sensor board combining a digital 3-axial accelerometer and IR tag reader sensor with a microphone circuit, and (2) a physiological sensing board providing 2channel EKG/EMG, 2-channel galvanic skin response (GSR), and skin temperature sensors. Additional analogue ports of the Hoarder also allow connecting a wide range of commercially available sensors, including pulse oximetry, respiration, blood pressure, EEG, blood sugar, humidity, and CO2 sensors. 9. iMote [33] – The Intel’s node pilot platform iMote is built around an integrated wireless microcontroller consisting of an ARM7 core, a Bluetooth radio and industrial vibration sensors. Nevertheless variety of sensors can be connected to the Intel mote platform as well. 10. VitalDust [19][5] consists of a small, low-power computer connected to a sensor that fits over the patient’s fingertip. It is about six centimetres by three centimetres, or the size of a pack of chewing gum. VitalDust is based on the Mica2, MicaZ, and Telos sensor node platforms, runs on two AA batteries and includes an embedded microprocessor, memory, and a wireless communication interface. The device collects heart rate, oxygen saturation, and EKG data. All collected data can be relayed over a short-range (up to 100 m) wireless network to any receiving devices, including PDAs, laptops, or ambulance-based terminals. The sensor devices can be programmed to process the vital sign data, for example, to raise an alert and signalled to a nearby paramedic when vital signs fall outside the normal range. 11. The Pluto mote [19][4] is designed to be small, lightweight, and wearable (wristwatch like). The Pluto is based on Telos platform [25] and incorporates a tiny, rechargeable Li-ion battery, small USB connector, surface mount antenna and 3-axis accelerometer. Pluto motes can monitor motor functions, i.e. limb movement, in patient with Parkinson’s disease and stroke, and pa-

tient’s physical activity, as well. The data captured by these sensors can be uploaded to a PDA, laptop, or PC residing in the patient’s home. 12. The MITes [34] sensor nodes can be attached to almost any object in the environment, and humans. The MITes platform includes six environmental sensor types to detect: movement using ball, mercury, and reed switches; movement tuned for object-usage detection (using acceleration), light, temperature, proximity, and current consumption. The MITes platform also includes five wearable sensors which can detect: body motion, heart rate, ultra violet radiation exposure, an RFID reader in a wristband form factor, and location beacons. 13. BlueTags, which is the alpha-version of Bluetoothbased smart-tags [35], is equipped with an embedded microcontroller, Bluetooth radio chip and internal antenna. The transmission range is approximately 20 m. It is small and wearable with application in tracking the movement of mobile individuals. 14. BSN node [36] – Body Senor Network (BSN) hardware platform is equipped with Texas Instrument MSP430 16-bit ultra low power RISC processor, 250 kbps wireless radio with a range over 50m and a number of wireless biosensors including: 3-lead ECG, 2-lead ECG strip, and SpO2 sensors. IV. DISCUSSION AND CONCLUSION This paper surveys the contemporary applications of WSNs technology in bio-medical services. A review of the most significant research and technology development projects is offered. Outline of the most frequently used sensor nodes and hardware platforms is presented. An association between the described application scenarios and appropriate sensor nodes is given, as well. Although the WSN technology is in the dawn of its development, and the sensor nodes are not elaborated sufficiently to meet some specific requirements of the bio-medical applications, such as: node size, energyefficiency and robustness of the communication protocols, reliability of the hardware, etc, we witness a burst of worldwide research ideas and projects. Undeniably, the progress of WSN technology will face the challenges, and will bring enhanced functionality of the sensor nodes and reduction of the node’s size and costs. The smart networked sensors technology demonstrates the potential to impact a number of biomedical applications, such as: medical treatment, preand post-hospital patients monitoring, people rescue, early disease warning systems, etc. In particular, WSNs could contribute to solving some important social problems, such as caretaking for chronically ill and aged people, and people with mental and physical disabilities. This will not only improve their quality of life, but also will bring great benefit to the society as a whole. V. REFERENCES [1] MobiHealth project – Available: http://www.mobihealth.org [2] V. Jones, R. Bults, D. Konstantas, “Healthcare pans: Personal area networks for trauma care and home care”, in Proc. WPMC’2001. [3] J. Shneidman et al., “Hourglass: An Infrastructure for Connecting Sensor Networks and Applications”, Technical Report TR-21-04, Harvard University, Sept. 2004.

[4] T. Gao et al., “Vital Signs Monitoring and Patient Tracking Over a Wireless Network”, in Proc. IEEE EMBS’2005. [5] D. Myung et al., “Vital dust: Wireless sensors and a sensor network for real time patient monitoring”, in Proc. 8th Annual New England Regional Trauma Conference, 2002. [6] L. Schwiebert, S. Gupta, J. Weinmann, “Research Challenges in Wireless Networks of Bio-medical Sensors”, Mobile Computing and Networking, pp.151-165, 2001. [7] K. Lorincz et al., “Sensor Networks for Emergency Response: Challenges and Opportunities”, Pervasive computing, IEEE, 2004, pp.1536-1268. [8] F. Michahellesa et al., “Applying wearable sensors to avalanche rescue” Computers & Graphics 27, pp. 839–847, 2003 Elsevier. [9] D.A. Shea, S.A. Lister, “The Biowatch Program: Detection of Bioterrorism”, Congressional Research Service Report RL 32152, Science and Technology Policy Resources, Science and Industry Division, November, 2003. [10] E. Anderson, T. Girard, G. Ottavianelli, “A Micro-Satellite And in Situ Ground Sensor Network For Combating Malaria”, in Proc 54th International Astronautical Congress, 2003. [11] Smart Medical Home at the University of Rochester – Available: http://www.futurehealth.rochester.edu/smart_home/ [12] J. Sanders, “Sensing the Subtleties of Everyday Life”, Research Horizons Magazine 17, no.2, 2000. Georgia Institute of Technology. [13] S.S. Intille et al., “Designing and evaluating technology for independent aging in the home,” in Proc. International Conference on Aging, Disability and Independence, 2003. [14] S. Consolvo et al., “Technology for Care Networks of Elders”, Pervasive computing, pp.1536-1268, 2004. [15] E. Dishman, “Inventing Wellness Systems for Aging in Place”, IEEE Computer, May 2004. [16] Amigo project – Ambient intelligence for the networked home environment – IST-004182 – Available: www.amigo-project.org [18] IBM Personal Care Connect mobile health monitoring solution – Available: http://www-03.ibm.com/industries/healthcare/doc/content/ resource/business/1537659105.html [19] CodeBlue Project website – Available: http://www.eecs.harvard. edu/~mdw/proj/codeblue/ [20] K. Lorincz, M. Welsh, “MoteTrack: A Robust, Decentralized Approach to RF-based Location Tracking”, in Proc. LoCA 2005. [21] B. Warneke et al., “Smart Dust: Communicating with a CubicMillimeter Computer”, IEEE Computer 34, no.1, 2001, pp. 44-51 [22] Single-chip “Spec” – http://www.jlhlabs.com/jhill_cs/spec/ [23] J. Polastre et al., “The Mote Revolution: Low Power Wireless Sensor Network Devices”, in Proc. Hot Chips 2004. [24] Crossbow Technology Inc., MICA2, MICA2DOT, MICAz, – Available: http://www.xbow.com. [25] “tMote Sky: Ultra Low Power IEEE 802.15.4 Compliant Wireless Sensor Module”, 2005. Available: http://www.moteiv.com/ products/docs/tmote-sky-datasheet.pdf. [26] J. Schiller et al., “ScatterWeb – Low Power Sensor Nodes and Energy Aware Routing,” in Proc. HICSS 2005. [27] M. Beigl et al., “uParts: Low Cost Sensor Networks at Scale,” in Proc. UBICOMP '05 [28] M. Beigl, H. Gellersen, “Smart-Its: An Embedded Platform for Smart Objects,” in Proc. SOC '03. pp.15–17. [29] J. Beutel et al., “Prototyping Wireless Sensor Applications with BTnodes,” in Proc EWSN'04, pp. 323-338. [30] BTNode project – A Distributed Environment for Prototyping Ad Hoc Networks. Available: http://www.btnode.ethz.cz/ [31] C. Park, J. Liu, and P.H. Chou, “Eco: an Ultra-Compact LowPower Wireless Sensor Node for Real-Time Motion Monitoring,” in Proc. IPSN'05, pp. 398-403. [32] R. DeVaul et al., “MIThril 2003: Applications and Architecture,” in Proc. ISWC '2003. [33] R. Kling et al., “The Intel iMote: Using Bluetooth in Sensor Networks,” in Proc. 2nd International Conference on Embedded Networked Sensor Systems, 2003. [34] E.M. Tapia et al., “MITes: Wireless Portable Sensors for Studying Behavior”, in Proc. UbiComp, 2004. [35] A. Beaufour, M. Leopold, P. Bonnet, “Smart-Tag Based Data Dissemination”, in Proc. WSNA’2002, (BlueTags Home Page Available: http://www.bluetags.com) [36] B. Lo et al., “Body Sensor Network – A Wireless Sensor Platform for Pervasive Healthcare Monitoring”, in Adjunct Proc. PERVASIVE'2005, pp.77–80.


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