Body Area Sensor Network based Health ... - MAFIADOC.COM

International Journal of Emerging trends in Engineering and Development

ISSN 2249-6149 Issue1, Vol. 3(November-2011)

Body Area Sensor Network based Health Monitoring System

P. N. Narendra Reddy*, P. I. Basarkod, S. S. Manvi Department of Electronics and Communication Engineering REVA Institute of Technology and Management, Katigenahalli, Yelahanka, Bangalore, Karnataka, India- 560064 [email protected], [email protected], [email protected]

Abstract The advancement in the field of wireless technology in recent years has led to a wide variety of applications. Wireless sensor networks have been an active research topic for around a decade now. Moving from early research in military applications, these are now widely deployed in diverse applications including the health monitoring system. Body area sensor network is one of those advanced applications which assist in monitoring of bio medical parameters of a person wearing it. The main purpose of this is to make it possible for a patient needing permanent monitoring yet to be fully mobile. This paper deals with most of the biomedical parameters of a patient like heartbeats, body temperature, glucose levels, respiratory conditions and so on. Body area sensor network basically consists of a set of light weighted sensors which monitor the various health parameters. All these monitored bio-signals are sent to a back-end system at a healthcare center with the help of radio frequency signals. A healthcare specialist retrieves this data over a reliable wired connection. To increase the freedom of movement of the patient, technologies like Bluetooth and general packet radio switching have been utilized. Global positioning system is used here to track the specific location of a patient. This location provision service makes it possible to locate patients even while they are outside the healthcare center. This system is cost effective and the results indicate that the system has a very good response time.

Keywords – WSN, BAN, BASN, Radio Frequency, Bio-sensors *Corresponding author

1. INTRODUCTION A. Sensors Sensing is fundamental to all sensor networks, and its quality depends heavily on industry advances in signal conditioning, micro-electro-mechanical systems (MEMS), and nanotechnology. Sensors fall into three categories. Physiological sensors measure ambulatory blood pressure, continuous glucose monitoring, core body temperature, blood oxygen, and signals related to respiratory inductive plethysmography, electrocardiography (ECG), electroencephalography (EEG), and electromyography (EMG). Bio-kinetic sensors measure acceleration and angular rate of rotation derived from human movement. Ambient sensors measure environmental phenomena, such as humidity, light, sound, pressure, and temperature levels.

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B. Body Area Sensor Network Coordinated and intelligent computing are enabling sensor networks to monitor environments, systems, and complex interactions in a range of applications. Body area sensor networks (BASNs) promise uses in healthcare, fitness, and entertainment. Each BASN consists of multiple interconnected nodes on, near, or within a human body, which together provide sensing, processing, and communication capabilities [1]. BASNs have tremendous potential to transform how people interact with and benefit from information technology, but their practical adoption must overcome formidable technical and social challenges. These challenges have farreaching implications but offer many immediate opportunities for system design and implementation [2]. Although BASNs share many challenges and opportunities with general wireless sensor networks (WSNs)— and can therefore build off the body of knowledge associated with them—many BASN specific research and design questions have emerged that require new lines of inquiry. For example, to achieve social acceptance, BASN nodes must be extremely noninvasive, and a BASN must have fewer and smaller nodes relative to a conventional WSN [3]. Smaller nodes imply smaller batteries, creating strict tradeoffs between the energy consumed by processing, storage, and communication resources and the fidelity, throughput, and latency required by the applications. Packaging and placement are also essential design considerations, since BASN nodes can be neither prominent nor uncomfortable [4]. As with any technology, economic concerns can affect BASN adoption. To amortize nonrecurring engineering costs, each BASN platform will require either significant volume in a single application or aggregate volume across several applications, creating design tradeoffs between application specific optimizations and general-purpose programmability.

2. RELATED WORKS Winters et al. proposed ―A wearable health-monitoring device using a Personal Area Network (PAN) or Body

Area Network (BAN)‖, which can be integrated into a user's clothing [5]. This system organization is unsuitable for continuous monitoring during normal activity [6], intensive training or computer-assisted rehabilitation [7]. Recent technology advances in wireless networking micro-fabrication, and integration of physical sensors, embedded microcontrollers and radio interfaces on a single chip, promise a new generation of wireless sensors suitable for many applications [8]. However, the existing telemetric devices either use wireless communication channels exclusively to transfer raw data from sensors to the monitoring station, or use standard high-level wireless protocols such as Bluetooth that are too complex, power demanding, and prone to interference by other devices operating in the same frequency range [9]. These characteristics limit their use for prolonged wearable monitoring. Simple, accurate means of monitoring daily activity outside the laboratory are not available. At present, only estimates can be obtained from questionnaires, measures of heart rate, video assessment, and use of pedometers [11] or accelerometers [12]. Finally, records from individual monitoring sessions are rarely integrated into research databases that would provide support for data mining and knowledge discovery relevant to specific conditions and patient categories.

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3. HEALTH MONITORING SYSTEM Wearable health monitoring systems integrated into a telemedicine system are novel information technology that will be able to support early detection of abnormal conditions and prevention of its serious consequences. Many patients can benefit from continuous monitoring as a part of a diagnostic procedure, optimal maintenance of a chronic condition or during supervised recovery from an acute event or surgical procedure. Important limitations for wider acceptance of the existing systems for continuous monitoring are unwieldy wires between sensors and a processing unit, lack of system integration of individual sensors, interference on a wireless communication channel shared by multiple devices, and nonexistent support for massive data collection and knowledge discovery. Traditionally, personal medical monitoring systems have been used only to collect data for off-line processing. Systems with multiple sensors for physical rehabilitation feature unwieldy wires between electrodes and the monitoring system. These wires may limit the patient's activity and level of comfort and thus negatively influence the measured results. The overview of our health monitoring system is as shown in figure 1.

Figure 1: Overview of Health Monitoring System

A. Bio-Sensors Wireless BASN can include a number of physiological sensors depending on the end-user application. Information of several sensors can be combined to generate new information such as total energy expenditure. An extensive set of physiological sensors may include the following:  An ECG (electrocardiogram) sensor for monitoring heart activity  An EMG (electromyography) sensor for monitoring muscle activity  An EEG (electroencephalography) sensor for monitoring brain electrical activity  A blood pressure sensor  A tilt sensor for monitoring trunk position  A breathing sensor for monitoring respiration  movement sensors used to estimate user's activity  A "smart sock" sensor or a sensor equipped shoe insole used to delineate phases of individual steps There are many numbers of sensors in the BASN as shown in Figure 2 [10]. BASN users are likely to tolerate and accept some degree of burden of using all if they perceive enough value in doing so.

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Figure 2: Body Area Sensor Network

B. Placement of Bio-sensors Sensors in typical WSNs are numerous, homogeneous, and insensitive to placement error. BASN sensors, in contrast, are few, heterogeneous, and require specific placement. Indeed, ineffective placement or unintended displacement from movement can significantly degrade the captured data quality. Such requirements call for strategies that will minimize and detect placement error, such as better packaging combined with on-node signal classification. Commercial sensors exhibit a wide range of power supply requirements, calibration parameters, output interfaces, and data rates. Engineering BASN nodes to accommodate this breadth of sensing requirements could necessitate an application-specific approach that minimizes the design space, improves efficiency, and amortizes cost over a single application. Likewise, BASN nodes designed with a high degree of configurability could amortize cost over a much larger range of applications, including those unforeseen.

C. Working principle BASNs must effectively transmit and transform sensed phenomena into valuable information and do so while meeting other system requirements, such as energy efficiency. A BASN’s value therefore rests in large part on its ability to selectively process and deliver information at fidelity levels and rates appropriate to the data’s destination, whether that is to a runner curious about his heart rate or a physician needing a patient’s ECG. The desperate application requirements call for the ability to aggregate hierarchical information and integrate BASN systems into the existing information technology infrastructure. Current work to address these challenges and realize these opportunities points to a critical need for collaboration between technologists and domain experts who can help define the specifications and requirements for BASN systems and applications. In applications targeting the aging population, for example, such collaboration could involve physicians, nurses, psychologists, and sociologists to ensure that a BASN provides valuable information while being usable

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by the elderly in a safe and socially acceptable manner. The need for such collaboration is only one of many requirements that research must satisfy to pave the way for practical, accessible BASN use.

4. IMPLEMENTATION DETAILS All patients have to be completely monitored under certified monitoring technicians. This could not be practically possible all the time. Therefore a health monitoring system has been designed in such a way to overcome such situations. It automatically monitors and if there is any problem relating to abnormality in heartbeat or so, it certainly buzzers an alarm which indicates that there is an urgency to monitor and treat that patient according to the situation. In this design, a micro controller is used to control the patient’s conditions automatically, depending upon the various biomedical parameters such as body temperature, heart rate, respiration rate etc. Some patients in hospital require constant attention when the patient’s health is in critical condition. Such patients have to be monitored all through the day so that even a small movement made by the patient is noticed by the doctors and nurses such that they can rush to that particular patient whenever required and take immediate care. Using this hardware, the doctors can monitor a patient even if they are far away from the patient. At certain emergencies, they can go to that patient or direct a nurse to attend that patient immediately. The patient bed has certain measuring instruments to send signals through the RF transmitter. The RF receiver and announcement section at the doctor’s room produces the announcement according to the sensor and at the same time it displays a message on the LCD display. Some features of E-life saver shown in figure 3 are as given below.  Automatic checking of heart pulses  Automatic check-up of human body temperature  Automatic checking of patient urine  Automatic checking of glucose level  RF transmitter and RF receiver is used for

communication

 Separate code for every sensor attached to the patient

Figure 3: E-Life Saver

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The heartbeat sensor shown in figure 4 converts the heart’s mechanical signals into electrical signals (analog to digital). It senses the increase or decrease in the rate of heartbeat of the patient and gives a signal through the pin 1 of the microcontroller 89s52. It consists of a 7 segment display, a microcontroller and an ADC.

Figure 4: Heartbeat Sensor The temperature sensor shown in figure 5 senses the variations in the body temperature of the patient. This block consists of a thermister, an ADC, a relay and a display which is connected to pin 4 of the micro controller 89s52. The temperature variations are signaled through the relay to the microcontroller.

Figure 5: Temperature Sensor Glucose level sensor shown in figure 6 indicates the glucose levels in the glucose bottle by passing an IR signal through the glucose bottle. This block consists of an IR transmitter and receiver. When the IR signal is received by the IR receiver at the other end it indicates that the glucose level has gone down and it must be quickly taken care of by the doctor. If there is enough glucose den the IR signal does not pass through the bottle. It is connected to pin 5 of the microcontroller.

Figure 6: Glucose level sensor Urine sensor shown in figure 7 is connected to the pin 8 of the microcontroller and it gives a signal through a relay to the microcontroller in case of bed wetting by the patient. It consists of a circuit where a short circuit occurs when liquid falls on it through the 3V supply and the sensor senses it and signals it to the microcontroller.

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Figure 7: Urine Sensor The RF transmitter shown in figure 8 is connected to pin 6 of the microcontroller that consists of a dual tone multiple frequency (DTMF) code generator which generates various codes for identification of the sensors sensing signals from heartbeat sensor, temperature sensor, glucose level sensor, etc. The DTMF encoder will encode the signals with different range of frequencies and are transmitted. These signals are then frequency modulated along with a carrier signal which is generated by the crystal oscillator and transmitted over the RF transmitter to the receiver in the doctor room. There are separate codes for each sensor which are transmitted when they signal the microcontroller. When any one sensor senses a negative voltage in the pin 1 of the DTMF coder it gets a negative voltage at pin 1 and it generates different frequencies. These frequencies are coupled to FM modulator which modulates the DTMF code signal with the crystal oscillator signal. After the frequency modulation, it is send to the RF amplifier. The RF amplifier amplifies and radiates the output through the antenna. A rod antenna is used here. The impedance of the antenna used is 75Ω. The output of this RF amplifier is 100mW of power and 27MHz of frequency.

Figure 8: RF Transmitter The modulated signal sent by the transmitter is received at the receiver end by an RF amplifier. This block amplifies the received signal and passes it on to the FM receiver. The DTMF codes sent by the sensors are decoded at the receiver end by the DTMF decoder and the output of the DTMF decoder is send to the driver section (transistor) to drive the relays. The relay provides low voltage to the announcement section and it reproduces the announcement. This announcement is also displayed on the LCD screen.

Figure 9: RF Receiver

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5. APPLICATION AREAS Because of demonstrated need and market demand, BASN research thus far has concentrated on healthcare applications, addressing the weaknesses of traditional patient data collection, such as imprecision (qualitative observation) and under sampling (infrequent assessment). In contrast, BASNs can continuously capture quantitative data from a variety of sensors for longer periods. By addressing challenges such as the energyfidelity tradeoff, BASNs will enable telehealth applications—medicine beyond the confines of hospitals and clinics—and, because of their human-centricity, will facilitate highly personalized and individual care. BASNs integrated with higher-level infrastructure will likely excel in healthcare scenarios, serving the interests of multiple stakeholders. In addition to delay-insensitive applications such as longitudinal assessment, BASNs that can offer real-time sensing, processing, and control will augment and preserve body functions and human life. BASN technology will also help protect those exposed to potentially life-threatening environments, such as soldiers, first responders, deep-sea and space explorers. Finally, BASNs are well positioned to benefit from the intersection of two formerly disparate application areas. Physiological and biokinetic sensing applications are increasing as athletes and fitness enthusiasts seek to improve human performance, while gaming systems are pushing their envelope by integrating more sophisticated interfaces based on human movement. With the crossing of these markets, BASNs are well positioned to deliver the biofeedback and interactivity necessary for next-generation fitness and entertainment applications.

6. RESULTS By using the above electrical circuits, various bio-medical parameters has been found. The output of the circuits is amplified by means of an amplifier and fed into an A/D converter. The digitized signal is then fed into the input port of the Microcontroller. The Microcontroller displays the parameters in digital value in the display device. If the level of temperature or respiration gets varied enormously, the buzzer will ring automatically. The health condition of a patient is continuously monitored by the health monitoring system and it keeps a track of all the changes observed. This data, which is of the form as shown in the table 1 is continuously forwarded to the concerned person. Whenever some abnormalities get detected, the doctor is alerted so that necessary actions are taken on time. Table 1: Testing results Urine albumin Urine microscopy Blood pressure Fasting blood sugar Post food blood sugar Haemogram Blood urea Body temperature Respiratory rate Heart beat

nil Normal Normal 80 110 Normal 20 95 85 75

BASN based health monitoring system take certain time in detection and transmission of the bio-medical parameters such as respiratory rate, heart beat, etc. The overall response time of detection and transmission with reference to area is as shown in the figure 10. Transmission requires less time when compared to detection as it requires more time to detect all the bio-medical parameters.

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Time in msec

Response Time Vs Area 100 90 80 70 60 50 40 30 20 10 0

200 400 600 800 1000

Detection Transmission

Area in sq. mtrs

Figure 10: Overall Response Time Vs Area

Conclusion A wearable Wireless BASN of physiological sensors integrated into a telemedical system holds the promise to become a key infrastructure element in remotely supervised, home-based patient rehabilitation. It has the potential to provide a better and less expensive alternative for rehabilitation healthcare and may provide benefit to patients, physicians, and society through continuous monitoring in the ambulatory setting, early detection of abnormal conditions, supervised rehabilitation, and potential knowledge discovery through data mining of all gathered information. Continuous monitoring with early detection likely has the potential to provide patients with an increased level of confidence, which in turn may improve quality of life. In addition, ambulatory monitoring will allow patients to engage in normal activities of daily life, rather than staying at home or close to specialized medical services. This system is also cost effective comparably and provide good response time, performing the process much faster.

Future work BASN detects most of the bio-medical parameters of a patient and alerts the concerned person at abnormal conditions. Certain specific actions have to be performed for different conditions by the caretaker. These actions should also be made to perform automatically using wireless technology. For example, switching ON the medical oxygen cylinder when the oxygen level goes down or injecting the glucose when required automatically. This system may further incorporate more advanced methods of diagnosis such as Gait analysis etc, which are helpful in early detection of abnormalities/disorders in the patients

Acknowledgement We would like to express our sincere thanks to professor R C Biradar, ECE department, Reva ITM for his continued support and guidance towards the concept. His continuous feedback has always been the strongest motivation behind this work.

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References [1] Park S, Jayaraman S: Enhancing the Quality of Life through Wearable Technology. IEEE Engineering in Medicine and Biology Magazine, 22(3):41-48 (2003). [2] Schull, M. J.: What are we waiting for? Understanding, measuring and reducing treatment delay for cardiac patients. In: Emergency Medicine Australasia 17:3, 191–192 (2005). [3] Agrawal, R., Arntz, H.R.: Sudden cardiac death does not always happen without warning. In: American Heart Association journal report (2006). [4] Martin T, Jovanov E, Raskovic D: Issues in Wearable Computing for Medical Monitoring Applications: A Case Study of a Wearable ECG Monitoring Device. Proc of the International Symposium on Wearable Computers ISWC Atlanta, Georgia 200:43-50 (2000). [5] Winters JM, Wang Y, Winters JM: Wearable Sensors and Telerehabilitation: Integrating Intelligent Telerehabilitation Assistants with a Model for Optimizing Home Therapy. IEEE Engineering in Medicine and Biology Magazine 22(3):56-65. [6] Moser, D.K., McKinley, S., Dracup, K., Chung, M.L.: Gender differences in reasons patients delay in seeking treatment for acute myocardial infarction symptoms. In: Patient Education and Counseling Vol 56 pp 45-54 (2005). [7] Istepanian RSH, Jovanov E, Zhang YT: Guest Editorial Introduction to the Special Section on M-Health: Beyond Seamless Mobility and Global Wireless Health-Care Connectivity. IEEE Transactions on Information Technology in Biomedicine, 8(4):405-414 (2004). [8] 21st IEEE International Symposium on Computer-Based Medical Systems, A Self-test to Detect a Heart Attack Using a Mobile Phone and Wearable Sensors by Peter Leijdekkers and Valerie Gay (2006). [9] Dokovsky, N. and Halteren, A. and Widya, I., BANip: enabling remote healthcare monitoring with Body Area Networks, University of Twente, Enschede, 11p. (2004). [10] A Hybrid Mobile-based Patient Location Tracking System for Personal Healthcare Applications by 1S. H. Chew, 1P. A. Chong, 1E. Gunawan, 1K. W. Goh, 2Y. Kim and 1C. B. Soh (2007). [11] R. Plonsey and R. Barr, Bioelectricitv: A Quantitative Approach , Plenum Press, New York, (1988). [12] K. Anant, F. Dowla, and G. Rodrigue, ―Detection of the electrocardiogram P-Wave using wavelet analysis,‖ Proc. SPIE, Vol. 2242, pp. 744-749 (1994).

PN Narendra Reddy received Bachelor of Engineering degree in Electronics and Communication Engineering from the Visvesvaraya Technological University, Belgaum and pursuing Master of Technology degree from Visvesvaraya Technological University, in the field of VLSI Design and Embedded Systems. His research interests are VLSI based Agent applications, Bio-medicine, Wireless Communication and Computer Networking. He has published two papers at national conferences and one international journal. He is a member IEEE (MIEEE, India). PI Basarkod received B.E degree in Electronics and Communication from National Institute of Engineering, Mysore, M.E degree in Electronics from B M S engineering College, Bangalore, M.S in Software Systems from Birla Institute of Technology and Science, Pillani and currently doing research under the guidance of Dr. Sunilkumar S Manvi from Kuvempu University, Shankaragatta, Shimoga. He is currently working as a Professor in Electronics and Communication Department of Reva Institute of Tecnology and Management, Bangalore. He is having a teaching experience of twenty four years and his areas of interest are Wireless Communication and Computer Networking. He is a member of ISTE (MISTE, India), member of IEEE (MIEEE, USA).

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Sunil S Manvi received M.E degree in Electronics from the University of Visweshwariah College of Engineering, Bangalore and PhD degree in Electrical Communication Engineering, Indian Institute of Science, Bangalore, India. He is currently working as a professor and Head of Department of Electronics and Communication Engineering, Reva Institute of Technology and Management, Bangalore, India. He is involved in research of Agent based applications in Multimedia Communications, Grid Computing, Vehicular Adhoc Networks, E-Commerce and Mobile Computing. He has published many papers in national and international conferences and journals. He has published three books. He is a fellow IETE (FIETE, India), Fellow IE (FIE, India) and member ISTE (MISTE, India), Member IEEE (MIEEE, USA).

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