SVM based context awareness using body area sensor network for pervasive healthcare monitoring

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SVM based context awareness using Body Area Sensor Network for pervasive healthcare monitoring

Sonali Agarwal Indian Institute of Information

Technology Allahabad, India

[email protected]

Divya Indian Institute of Information


[email protected]

G.N.Pandey Indian Institute of Information


[email protected]

ABSTRACT

In the present growing era advancement of computer processing power, data communication capabilities, low power micro electronics devices and micro sensors increases the popularity of wireless sensor network in real life. Body area sensor network is a group of sensor nodes inside and outside the human body for continuous monitoring of health conditions, behavior and

activities. Context awareness in pervasive health care is a proactive approach which is different from a conventional event-driven model (for example: visiting doctor when sick) and here we are continuously monitoring a patient health conditions through the use of Body area sensor network. This paper presents a layered architecture of Wide Area Wireless Sensor Body Area Network (WA-WSBAN) along with data fusion techniques, standards and sensor network hardware requirement for context

awareness. A BodyMedia sensor dataset collected from 9 different sensor nodes has been used to classify the user activities with reference to different sensor readings. The context information derived from the proposed Wide Area Wireless Sensor Body Area Network (WA-WSBAN) architecture may be used in pervasive healthcare monitoring to detect various events and accurate

episodes and unusual patterns and activities obtained from the study can be marked for later review. In this research work patient activity and gender classification has been done by using one to all and multi kernel based support vector data classification. The similar practices may be utilized for the study of various observations in real time health care applications and proactive measures may be initiated based on results obtained from data classification.

Keywords

Wireless Sensor Network, Body Area Sensor Network, Classification, Context Awareness, Pervasive healthcare, Support vector machine, Decision tree.

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IITM’10, December 28-30, 2010, Allahabad, UP, India. Copyright 2010 ACM 978-1-4503-0408-5/10/12…$10.00.

1. INTRODUCTION Healthcare is a prime concern for every society and pervasive healthcare with proper deployment of small handheld wireless devices is a way to observe the health of patients and get better

quality of life. A wireless body area network is a collection of multiple wearable external and implantable internal sensor nodes for monitoring the human physical and physiological behavior in a continuous manner in different environments like hospital, residence and work place [1]. The special network sensors are designed to measure human physical parameters having clinical significance and ranging from micro to nano units of applications. Table 1 shows the different types of sensors along with their input and output parameters through Body Area Sensors network [2].

The common body sensor products responsible for data collection and status monitoring are Armband, Glucose Meter, Pulse

Oximeter, Blood Pressure Monitor and Weight Scale [2]. The variety of desired application such as remote monitoring, computer-assisted ambulatory monitoring and continuous observation of day to day activities may be controlled and managed by using body sensors and ambient sensors [2]. The different layout of body area sensor network is depends upon the stand alone or global perspective of application. The working of body sensor nodes is well explained here with the help of figure 1.

Here four activities i.e. driving, aerobic, fever and inactive state is shown on the basis of low motion, high motion, low heat flux and high heat flux.

Table 1: Input and output parameters of Body Area Sensor Network

Sensor readings as

input

Behavior interpretation as

output

Accelerometer or Biokinetic sensors

determining activity state (driving, sleeping, exercising) and posture (lying, sitting, standing)

Temperature and heat flux by Ambient sensors

the subject is active or at rest and subject movement

Galvanic Skin Response (GSR)

Skin conductance (affected by sweat gland activity)

Heart Beats by Physiological sensors

Number of Steps, Sleep Efficiency

SPO2 Sensor Oxygen level at any cell

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The research paper is organized as follows; in section 2 we discussed about the related research work in context awareness health care monitoring using Body area sensor network. Section 3 explains about Body area network topologies and suitable data fusion techniques. Section 4 deals with the relevance of context awareness in health monitoring system. Section 5 presents a multi-tier Body area network based healthcare architecture enhanced by pervasive computing technology. Section 6 deals

with the use of Support Vector Machine (SVM) classification technique to detect different activities of a patient especially for remote health monitoring system. In Section 7, experimental details are elaborated and the efficacy of the approach on the basis of result obtained is very well interpreted. Section 8 includes concluding remarks.

2. RELATED WORK Health monitoring with the help of small wearable body sensors is a growing research field. An application MIThril is developed by MIT Media Lab and works on human behavior recognition [3].

Another project CodeBlue is also focused on body area sensor network applications for health care [4]. The research work clearly explains the method of transmitting vital signs to the health care monitoring agenesis [4, 5]. A research work by G. Virone et al. proposed a low-cost, ad hoc use of multimodal sensors for an enhanced quality of medical care [6]. Different operating environment using an OPNET based

simulation model is proposed by Ali Peiravi. [7]. A paper authored by Brown, L developed Human++ UniNode health care application through sensors. It this application ECG, respiration, skin conductance and skin temperature was recored through body sensors and these signals were monitored specifically for health care purposes [8]. Wac K. et al., developed MobiHealth system based on body area

network (BAN) and mobile applications. A tele-monitoring system has been used to record highly customizable vital signs for investigation and a tele-treatment system is also suggested for patient care [9]. A paper authored by Soo Me Woo et al., proposed ECG signal monitoring system using body sensors. In this research work One-class support vector machine classifier was used to detect

abnormal heart signal values [10]. Shyamal Patel et al., presented an approach to estimate the severity of symptoms based on accelerometer sensor data. The results of SVM based classification were compared and verified with the help of visual inspection [11].

A paper authored by M. EIHelw represents a multi sensing framework with data fusion techniques and behavior profiling of the users by HMM model [12]. Korel B.T. et al., proposed context awareness Body area sensor network based health monitoring system to detect abnormal episodes in the signal [13]. A research

work proposed by Anthony et al., was used to recognize different activities of a user by using a smart home. The collected data are classified using Multi class SVM and result is compared for different kernel functions [14].

3. BODY AREA SENSOR NETWORK The figure 2 indicates different application domains of body area sensor network. A stand-alone Body Sensor Network is a collection of small wireless nodes inside out outside the human body collected in Star or Mesh topology [15]. A coordinator node

collects all the observations and response for input output or processing related queries. In figure 3 a mobile phone or PDA is used as a gateway to provide connectivity between a body area sensor network and an application server through the Internet [15]. In figure 4 a mobile body sensor network is connected to a stationary body sensor network with the help of coordinator nodes [15].

Work

Figure 1: Working of body Sensor Network

Figure 2: Star and Mesh topology body Sensor Network

Figure 3: A Body Sensor network connected to a remote centre using internet

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3.1 Data fusion As stated in the section 3, Body Area Network is a collection of multiple sensors in the human body and each sensor is collecting different set of information. Integration of heterogeneous, incomplete, and error-prone body sensor data is a difficult task. Multi-sensor fusion is an effective use of multiple sources of information. There are three different type of Data fusion techniques i.e. competitive, complementary, and cooperative

fusion [16]. In competitive fusion, each sensor independently provides information about the monitored process. In this approach there is a very high probability of redundancy and inconsistent measurements. In complementary fusion, each sensor captures different aspects and submits the information which is further merged as a single picture. In cooperative fusion, sensors work together to gather complex information that is difficult to get alone by any of the sensors. For Body area sensor network, the

key purpose of sensor data fusion is to collect information from multiple sensors and combine the data with full precision and reliability. Figure 5, 6 and 7 shows the different levels of data fusion in Body area sensor network [17]. The choice of data fusion techniques depends upon the nature of events, episodes and diagnosis which is required at different stages of applications.

4. CONTEXT-AWARE SENSING Context can be defined as information that is sufficient enough to illustrate the condition of an entity. The main entities are persons, place, or related objects [18]. A health monitoring system is termed as context-aware if its action plan is based on the context, which is gathered from the user and surroundings. It simply records user identity, types of activities, interaction, location and time related details for any environment. For Body area sensor

network applications, user movement, emotional states, common and rare activities may be captured as context [19]. The Data used in present application is collected by a commercial monitoring tool SenseWearTM Armband from BodyMedia [20][33], which is wearable on the upper arm of the patient. With the help of this device vital information about human body such as body temperature, heat flow, ambient temperature, different movements and Galvanic Skin Response (GSR) may be recorded [21]. Main

Considerations of context aware sensing are as follows: • User identity. • User location, orientation, speed and acceleration. • Temporal details: time, date and season. • Environmental details: temperature, air quality, light or

noise levels. • Social interaction: connected person. • Resources : Battery, display, network, and bandwidth

• Physiological details: Blood pressure, heart rate, respiration rate, tone of voice, and emotions.

• Activity details: talking, reading, walking and running.

5. PROPOSED ARCHITECTURE The Proposed Wide Area Wireless Sensor BAN (WA-WSBAN) architecture is based on sensor fusion multi tire approach. The proposed application utilizes multiple sensors on a human body to get better information about the basic function of human body. Sensor fusion is a complex approach because different sensors provide different types of data, with different accuracy and

complexity of information. It is important to collect inputs from different sensors put them collectively in an intelligent and useful way in a real time environment. The framework is divided into three stages as shown in figure 8. The lowest layer is body area network of a patient, which is collection of multiple sensors and monitored a personal server on mobile or PDA.

Figure 4: A mobile body sensor network is connected

to a stationary body sensor network.

Figure 5: Data level fusion

Figure 6: Feature level fusion

Figure 7: Decision Level Fusion


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Figure 8: Layers of proposed framework

The BAN information gathered by the personal server is further submitted to a hospital area network which may have wired or wireless connectivity. This is referred as Tier 2 or middle layer. The communications links used between the body sensors and the

personal server may be implemented by using ZigBee, Bluetooth, or WiFi [22]. The data collected from Hospital area network may be further connected to Wide Area Network of all connected Hospitals with a central data repository server for chronic patients. Figure 9 indicates application environment of proposed framework. The different levels of this Wide Area wireless sensor BAN may be helpful for researchers, health care professionals, and emergency service providers and so forth. For seamless

connectivity of multiple tire, a range of communications protocols are available, and the choice is based on the necessities of the particular problem domain [23][24].

5.1 Components of System Architecture

The system architectures of WA-WSBAN mainly consist of six major components [23][24]:

i. Processor –Processing speed of 10 MIPS (Millions of Instruction Per Second)

ii. Wireless Communication – Wireless link between sensor nodes based on 802.15.4 and Zigbee communication protocol.

iii. Memory – External storage for sensor readings, working memory (RAM) – 1 to 10 KB

iv. On chip flash memory – 10 to 100 KB v. Integrated peripherals (Timers, Counters, Pulse

Width Modulator, A/D, D/A, and communication controllers).

vi. External flash memory –Approx. 1 MB vii. Sensor Interface – Interface with sensors and other

devices i.e. Analag interface and digital interface viii. Power Supply – 1-10 mW for processing, approx.

100 μW in idle state and approx. 20-50 mW during communication

ix. Operating System – System Software as resource manager.

6. SUPPORT VECTOR MACHINE (SVM) The fundamental theory of SVM is originated from statistical learning theory given by Vapnik and others [26]. Support Vector Machines (SVMs) are based on supervised learning methods

which analyze the data and identify the patterns. Support Vector Machine is based on the construction of single or multiple hyperplane in a very high dimensional space for the utilization of classification and regression techniques [27]. In the classification

problem training dataset is given and it comprises observation

( i, i) for . Here iD and i refers the

class labels. The main purpose is to learn a classification rule, d

, that closely matches attributes, i, to the

class label, i.

Equation of hyper plane is given as:

=0

where and are equation parameters of the hyper plane to

compute.

From this we build up a hyper plane given by:

0 , =1

0 , =-1

SVM is initially intended to perform binary classification later it was designed to solve multi- classification problem by three methods such as “One-Against-Many”, “Multicategory-SVM” and “One-Against-One”. In conventional way, Multiclass problem is decomposed into a series of two-class problems and several

binary classifiers are constructed [28]. One-Against-Many is a method in which Multiple SVM classifiers are constructed. In this classification every time it checks that ith class is separate from rest of the classes. The problem with this method, however, arises when final decision is made by combining M classifiers; the classifier which generates the maximum value from the decision function is selected as winner and assigned the corresponding class label without considering the competence of the classifiers.

The outputs of the decision function are employed as only index to indicate how strong a sample belongs to the class.

6.1 One-Against-Many Approach One-Against-Many (OAM) SVM is an approach of multi-classification and introduced by Vapnik in 1995 [26] [29]. The initial formulation of the One-Against-Many method requires agreement among all SVMs. It states that a data point would

Figure 9: Application environment of proposed framework

Wide Area Network of all connected Hospitals with a

central data repository server for chronic patients

Wireless LAN of the hospital with a

central server

Body Area network of patient

with a personal Server on

Mobile or PDA


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belong to a class if and only if the SVM of that class accepts this data point and all other classes' SVMs reject this [29]. Consider an M-class problem, where N training samples are given:

{ 1, 1},…, { N, N}. Here im is representing a feature vector

in multi dimension and i represents the respective

class label [29]. In One-Against-Many approach M binary SVM classifiers are constructed, and each is used to separate one class from all the remaining classes [29]. The ith SVM is trained here with all the remaining training samples of the ith class having positive class labels, and all the others with negative class labels. Here the ith SVM follow the following expression that generates

the ith decision function i( ) iT

ii with minimum value:

Subject to:

Where if yj = i and otherwise. A sample x is classified as in the class i* in the classification phase, whose decision function fi* produces the largest value [29].

6.2 M-SVM approach This approach is useful when problem have more than two categories.The approach is helpful solve the case of multicategory

classification. It works concurrently on multiple categories without breaking it into unrelated pieces as in one against multi classifiers [30]. In classification a set of training data points are

given which consist data points ( i, i) where . i belongs to training data points and i are the class

label of that training data points.( i, i). A random sample is

taken from a target population independently with probability

distribution [30]. Let denote a random sample from

and j( )= refer to the conditional

probability of a given class j where for .When misclassification cost is equal or symmetric

the loss function is

Where is an indicator which is assumed 1 when argument is

true and 0 when argument is false [30].The best classification rule would be the one that minimize the expected misclassification rate given by:

But when misclassification cost is unequal, it is not useful in

solving real the world problem. First, we define Cj for , is represented as misclassifying cost of an example

from class to class l [30]. The correct decision should not be

penalized here so Cjj for are all zero [30].The loss function is

Then best classification rule is given by:

If the sample is bias then it give confusing results and distort the

proportion of class which affect the classification rule [30]. If we already know the prior class proportion then we can solve the problem of sampling biasness by incorporating the discrepancy between the sample proportion and population proportion into the

cost component [31].Now it is assumed that j is the prior

proportion of class j in population and j s is the pre-specified

proportion of class j in training dataset [31]. j s may be different

from j if biasness has occurred. If ( s, s) be a random sample

obtained by sampling mechanism. Then the difference between

( s, s) and is clear from conditional probabilities [31].

7. EXPERIMENT

In this research work, for the classification purpose we have taken

a real time body area sensor network dataset taken from Body Media SenseWearTM Armband System [32]. This data set is available in excel format, which contains following details as shown in table 2:-

Table 2: Data set details.

S

No

Attribute

names

Attribute

Description

Measuring Quantity

1 Char 1 Age Measures age in years

2 Char 2 handedness Measure the movement of left and right hand as 0 and 1

3 Annotation Activity of a person

Measure activity with the help of various

motion sensors as annotation number

4 Sensor 1 gsr_low_ average

Measures sweat (conductivity across the skin)

5 Sensor 2 heat_flux_high_ average

Measures heat lost to the environment unit:

Watts/meter^2

6 Sensor 3 near_body_ temp_average

Measures temperature near armband Unit: degrees Centigrade

7 Sensor 4 pedometer Measures number of steps Unit: steps

8 Sensor 5 skin_temp_ average

Measures skin temperature using


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armband Unit: degrees Centigrade

9 Sensor 6 longitudinal_

accelerometer_ SAD

Measures vertical

acceleration (Sum of absolute differences) Unit: g

10 Sensor 7 longitudinal_ accelerometer_ average

Measures average of vertical acceleration Unit: g

11 Sensor 8 transverse_

accelerometer_ SAD

Measures average of

vertical acceleration Unit: g

12 Sensor 9 transverse_ accelerometer_ average

Measures average of vertical acceleration Unit: g

Two types of attributes are significant to analyze the status of the

user: i. Static attributes: Attributes which has information that

does not change at the time of training. (e.g. age, gender, pathway configuration, sensing nodes position, etc.)

ii. Real time attributes: Attributes which are continuous updating (e.g. temperature, heart rate, body movement etc.).

Two approaches may be possible for the implementation: i. A user-centric implementation, where all the sensed

data is taken together for processing. ii. A distributed approach, where different sensors process

its data in distributed manner. On the basis of user- centric approach it is assumed that all sensor data is collected as one place and the experiment has been

performed by using MATLAB 7.9.0 and a SVM One-to-many and Multicategory model is derived for context identification. The steps are as follows:

1. Collect sensor data from 9 sensors. 2. Perform Preprocessing operations. 3. Data is normalized between -4 to +4. 4. 10 folds cross validation is applied to separate training

set and test set. 5. Assign class labels as Context based on different

activities i.e. sleeping, sitting, walking and exercise. 6. Model building is performed by using SVM One-to-

many and Multicategory SVM classification using polynomial and Gaussian kernel.

7. Performance evaluation is based on classification accuracy.

The snapshots of SVM classification for different cases are shown

as figure 10, figure 11, figure 12 and figure 13. The visual observation of the dataset indicates that there are multiple outliers present in sensor data. The outliers are eliminated here and out of only 400 instances are selected for the present analysis. The classification is based on two parameters:

i. Classification based on Gender: Age and handedness attributes with all sensor readings has been taken for Gender classification. The study indicates that the contribution of age and handedness is very high for getting a clear gender classification. Gender

interpretation based on every session id has been also done here.

Figure 10: SVM Multi classification using

“one –against – many” for polynomial kernel


“one –against –many” for Gaussian kernel


“Multi category”

for Gaussian kernel


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ii. Classification based on Annotation: In this case only

sensor readings has been taken for classification and a multiclass SVM based classification is performed by taking one annotation versus rest sensor signals. Here it is explored that for any context there is a correlation between different values of sensors so value of any sensor may be predicted with the help of other sensor readings. As an end result we can also different activities of a person as a result of classification based on different sensor readings.

The table 3 indicates classification accuracy for each of the

classifier. It clearly indicates that context classification with multi-category SVM using Gaussian kernel is gives best classification accuracy. Figure 14 indicates a comparative analysis classification accuracy of different classifiers used in present study.

Table 3: Comparison between polynomial and Gaussian kernel function using different classification method

Classifier Polynomial kernel based

Gaussian

Kernel based

One against many 85.14% 95.98%

M-SVM(when misclassification cost are equal or symmetric)

83.25% 97.19%

M-SVM (when misclassification cost are asymmetric)

51.78% 62.52%

8. Conclusion Healthcare is always a prime concern for everyone and Body area sensor network based health monitoring systems are contributing

significantly to improve the quality of life. In this research work, we have firstly presented a survey of the Body area sensor network topologies and data fusion. The paper emphasizes on the application of Context-aware sensing in Body area sensor networks because it generates much more information than any conventional heath care application. To utilize the significance of context awareness in health care a new Wide Area Wireless Sensor Body Area network (WA-WSBAN) architecture has been presented. A body sensor data has been taken to monitor the

activities of a patient with the help of Support Vector Machine based classification techniques. The classification results indicate that Multi-Class SVM (with equal or symmetric misclassification cost) has maximum classification accuracy when tested with Gaussian kernel function. The results of classification may be clinically correlated by the health care experts. Here similar approaches can accommodate the classification of other significant parameters i.e. energy expenditure, common physical

activities and rare physical activities based on the observed sensor data. The suggested architecture may be additionally improved to avoid power backup, security and interference related problems. Other challenges in Body area sensor network based healthcare application includes: regulatory requirements, material constraints, robustness and continuous operation [33]. It is also

difficult to manage multiple sensors with efficient routing, required radio frequency bandwidth and error free functionality.

9. REFERENCES [1]. Liolios et. al., 2010. An Overview of Body Sensor

Networks in Enabling Pervasive Healthcare and Assistive Environments. PETRA „10, June 23 - 25, 2010, Samos, Greece. Copyright © 2010 ACM ISBN 978-1-4503-0071-1/10/06 .

[2]. Andrew D. Jurik, Alfred C. Weaver, 2009. Body

Sensors: Wireless Access to Physiological Data. IEEE Software, vol. 26, no. 1, pp. 71-73, Jan./Feb. 2009, doi:10.1109/MS.2009.5.

[3]. DeVaul, R.W., Sung, M., Gips, J., and Pentland, S., MIThril 2003. Applications and Architecture. In Proceedings of ISWC 2003, (White Plains, U.S.A., 2003).

[4]. Georgios Kambourakis et. al. 2007. Securing Medical

Sensor Environments: The CodeBlue Framework Case. Second International Conference on Availability, Reliability and Security (ARES'07) 0-7695-2775-2/07.

[5]. Harvard University CodeBlue project: Wireless Sensor Networks for Medical Care. Available: http://www.eecs.harvard.edu/~mdw/proj/codeblue/

[6]. G. Virone, A. Wood, L. Selavo, Q. Cao, L. Fang, T. Doan, Z. He, R. Stoleru, S. Lin, and J. A. Stankovic,

Figure 13: SVM Multi classification using “Multi

category” for asymmetric or unequal penalization coefficient:

for Gaussian kernel

Figure 14: Classification accuracy of different classifier

used for Gaussian kernel


278

2006. An Advanced Wireless Sensor Network for Health Monitoring at Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare (D2H2), Arlington, VA, April 2-4, 2006.

[7]. Ali Peiravi. 2010. Reliability of Wireless Body Area

Networks used for Ambulatory Monitoring and Health Care, Life Science Journal , Volume ( 7 ) , 2010-6.

[8]. Brown L, et. al. 2009. Body area network for monitoring autonomic nervous system responses. This paper appears in: Print ISBN: 978-963-9799-42-4 , Digital Object Identifier: 10.4108/ICST.PERVASIVEHEALTH2009.5973

[9]. Wac, K., Bults, R., et al. 2004. Mobile Health Care over

3G Networks: The MobiHealth Pilot System and Service. Global Mobile Congress, Shanghai, China.

[10]. Soo-Min Woo , Hye-Jin Lee , Bub-Joo Kang , Sang-Woo Ban. 2010. ECG signal monitoring using one-class support vector machine, Proceedings of the 9th WSEAS international conference on Applications of electrical engineering, p.132-137, March 23-25, 2010, Penang, Malaysia

[11]. Shyamal Patel, Konrad Lorincz, Richard Hughes et al. 2009. Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors., 864-73. In IEEE Transactions on Information Technology in Biomedicine 13 (6).

[12]. M. EIHelw, et. al. 2009. An Integrated Multi-Sensing Framework for Pervasive Healthcare Monitoring Digital Object Identifier: 10.4108/ICST.PERVASIVEHEAL

TH2009.6038 http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05191197

[13]. Korel, B.T.; Koo, S.G.M.; 2007. Addressing Context Awareness Techniques in Body Sensor Networks This paper appears in: Advanced Information Networking and Applications Workshops, 2007, ISBN: 978-0-7695-2847-2 Digital Object Identifier: 10.1109/AINAW.2007.69

[14]. Anthony et. al., 2010. SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results IEEE[2010] http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5352277

[15]. Guang-Zhong Yang et al., 2006. Body Sensor networks” ISBN-10: 1-84628-272-1, ISBN-13: 978-1-

84628-272-0, © Springer-Verlag London Limited 2006. [16]. Hairong Dong, David Evans, 2007. Data-Fusion

Techniques and Its Application. Fuzzy Systems and Knowledge Discovery, Fourth International Conference on, vol. 2, pp. 442-445, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) Vol.2, 2007.

[17]. Jitendra R. Raol.2006. Multi Sensor Data Fusion with

Matlab. CRC Press International Standard Book Number: 978-1-4398-0003-4 (Hardback).

[18]. Wu, W.H. Batalin, M.A. Au, L.K.; Bui, A.A.T. Kaiser, W.J.. 2007. Context-aware Sensing of Physiological Signals. 29th Annual International Conference of the IEEE, vol., no., pp.5271-5275, 22-26 Aug. 2007 doi: 10.1109/IEMBS.2007.4353531

[19]. Benny P.L. Lo; Guang-Zhong Yang; 2006. Body Sensor Networks: Infrastructure for Life Science Sensing Research. Life Science Systems and Applications Workshop, IEEE/NLM , vol., no., pp.1-2, July 2006.

[20]. Sunseri, M. The SenseWear armband as a Sleep

Detection Device. http://www.bodymedia.com/Learn-More/Whitepapers/The-SenseWear-armband-as-a-Sleep-Detection-Device.

[21]. Craig B. Liden et. al. Characterization and Implications of the Sensors Incorporated into the SenseWearÔ Armband for Energy Expenditure and Activity Detection. www.bodymedia.com/site/docs/papers/Sensors.pdf

[22]. Jin-Shyan Lee; Yu-Wei Su; Chung-Chou Shen. 2007. A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi. 33rd Annual Conference of the IEEE , vol., no., pp.46-51, 5-8 Nov. 2007 doi: 10.1109/IECON.2007.4460126

[23]. Mukherji, R.; Egyhazy, C.; Johnson, M.. 2002 Architecture for a large healthcare information system. IT Professional , vol.4, no.6, pp. 19- 27, Nov/Dec 2002

doi: 10.1109/MITP.2002.1114843 [24]. Joonyoung Jung et al. 2006.Wireless Body Area

Network in a Ubiquitous Healthcare System for Physiological Signal Monitoring and Health Consulting International Journal of Advanced Science and Technology http://www.sersc.org/journals/IJSIP/vol1_no1/papers/06.pdf

[25]. John Shawe-Taylor & Nello Cristianini 2000. Support Vector Machines and other kernel-based learning methods. Cambridge University Press.

[26]. V. Vapnik. 1998 Statistical Learning Theory ,Wiley. [27]. C.W. Hsu and C.-J. Lin 2002. A comparison of methods

for multi-class support vector machines, IEEE Transactions on Neural Networks, 13:415-425.

[28]. J. Weston and C. Watkins, MultiClass Support Vector Machines.

[29]. In M. Verleysen, 1999 Proceedings of ESANN99, Brussels.D.Facto Press.

[30]. Yi Liu; Zheng, Y.F. 2005. One-against-all multi-class SVM classification using reliability measures. IEEE International Joint Conference on , vol.2, no., pp. 849- 854 vol. 2, 31 July-4 Aug. 2005

[31]. Lingyun Zou; Zhengzhi Wang. 2007 Microarray Gene Expression Cancer Diagnosis Using Multiclass Support

Vector Machines. The 1st International Conference on , vol., no., pp.260-263, 6-8 July 2007 doi: 10.1109/ICBBE.2007.70

[32]. Yoonkyung Lee, Yi Lin, & Grace Wahba. 2004. Multicategory Support Vector Machines: Theory and Application to the Classi. cation of Microarray Data and Satellite Radiance Data © 2004 American Statistical Association Journal of the American Statistical

Association March 2004, Vol. 99, No. 465. [33]. Physiological Data Modeling Contest

http://www.cs.utexas.edu/~sherstov/pdmc/ [34]. Varshney, Upkar 2005. Pervasive Healthcare:

Applications, Challenges And Wireless Solutions. Communications of the Association for Information Systems: Vol. 16, Article 3. Available at: http://aisel.aisnet.org/cais/vol16/iss1/3

SVM based context awareness using body area sensor network for pervasive healthcare monitoring

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