Content uploaded by Danish Mahmood

Author content

All content in this area was uploaded by Danish Mahmood on Feb 09, 2021

Content may be subject to copyright.

Data Fusion for Orientation Sensing in Wireless Body Area

Sensor Networks using Smart Phones

Danish Mahmooda, Nadeem Javaida,∗

,

Muhammad Imranb, Umar Qasimc, Zahoor Ali Khand,e

aCOMSATS Institute of Information Technology, Islamabad, Pakistan

bCollege of CIS, King Saud University, Almuzahmiah, Saudi Arabia

cCameron Library, University of Alberta, Alberta, Canada

dFaculty of Engineering, Dalhousie University, Halifax, Canada

eCIS, Higher Colleges of Technology, Fujairah Campus, UAE

Summary

Information coming from diﬀerent sources may contradict with each other. To reach an

optimal and precise decision, that may be very crucial in nature gives birth to the concept of

information or data fusion. Amongst many problems, orientation sensing is one vital problem

to be tackled. Orientation sensing is in use since ages. The ﬁrst man on moon, Neil Arm-

strong landed there by applying a data fusion algorithm based upon orientation sensors on

navigational computer of his spaceship Apollo. Most importantly he succeeded in landing back

to earth. However, with emergence of new technologies such as Wireless Body Area Sensor

Networks (WBASNs), it gives new challenges. Amongst many, combining data from diﬀerent

sensors to use it in most eﬃcient and desirable fashion is one of the key challenge. Numer-

ous algorithms are developed and now there is a need to sort out which algorithm suits best

to some predeﬁned problem. Commencement of smart phones that have built in orientation

sensors are replacing expensive and complex Inertial Measurement Units (IMUs) which are

designed for a speciﬁc scenario. Orientation sensing in WBASN have numerous applications.

In e-health applications, investigation of rehabilitating backbone injuries can be measured by

continues readings of posture, or motion disorder of limb can be ﬁnd out and so on so forth.

For that, gyroscopes and accelerometers are key sensors that play vital role. Keeping machines

such as robots and air crafts in view, such data fusion is in practice. However, considering

human body movements yet there is a need to ﬁnd an accurate fusion algorithm that meets

all demands with low complexity.

In this chapter, initially Data Fusion and Data Fusion Algorithms (DFAs) are discussed con-

sidering WBASNs. Further, Keeping orientation sensing in focus two well known algorithms

i.e. Kalman and Complementary data fusion techniques are compared in context of Wireless

Body Area Sensor Fusion (WBASF). According to our ﬁndings, Kalman Filter may have given

∗Corresponding author: Dr. Nadeem Javaid, Associate Professor, Department of Computer Science COMSATS

Institute of Information Technology, Islamabad. Email: nadeemjavaidqau@gmail.com, Website: www.njavaid.com

1

2

very good results regarding machines however, Complementary ﬁlter proved itself better in

performance, complexity and required computational power in WBASNs.

1 Introduction

Advancements in Information and Communication Technologies (ICT) has given a new revolution

to this world. In every aspect and ﬁeld of life, things are changing, humans are seeking excellence

day after day. This ICT is involved in Smart Grids (the future of girds),in medical and bioinfor-

matics, in computer science and many more. Sensor networks have proven their importance in

almost every ﬁeld of life. Considering tiny nodes with little processing power, sensing and trans-

mitting units gives birth to numerous sub domains in WPANs. One of the most studied and needed

domain is Wireless Body Area Sensor Networks (WBASNs). These networks are mainly studied

and applied for e-health solutions. Considering healthcare, we can say that healthcare systems

are in a transitional phase. Manual healthcare is being replaced by automated healthcare. It is

transforming from centralized systems to distributed systems. If we fail to shift from centralization

to distributed environments, our existing hospitals will overwhelm with increasing population.

Besides there is more general awareness regarding healthcare. Now individuals are interested to

observe their physiology. Not only sportsmen are cautious to monitor their health and ﬁtness but

it helps in preventing or controlling diseases of a man from any domain and medical history. Hence

we can say that coming era hospital centric cure will be shifted to patient centric cure.

2 WBASN and E-Health Systems

Considering todays era, we have reached a point where wireless communication is at its boom

having numerous kinds of networks for numerous applications. However, there is a feeling that

nothing we propose is outside the scope of natural environment. One of emerging domains i.e.

Body Area Network (BAN) is existing as long as any living being is. We have eyes as cameras

seeing environment, nose that senses smell, tongue that gives sense of taste and list goes on. All

these sensors are connected to a hub known as brain to send request/information and take orders.

On sensing some smell, sensor (nose) transmits a signal via neuron to the brain and brain gives

orders regarding that speciﬁc environment. This vary concept is utilized in WBASNs as well.

As, sensors are deployed in/on a body, underlying some topology and they communicate with a

central hub to give information/requests and take orders accordingly as depicted in Fig. .1.

Natural BAN’s are so perfect that there is no chance of collision amongst various signals,

there is no energy issue, there is no malfunctioning in network behavior until some unexpected

event occurs. Considering man made BANs on which researchers are working for betterment have

countless issues. This cannot be as perfect as natural BAN though such artiﬁcial networks may

help humans whose natural BAN is disturbed. A Camera can be integrated that may inform brain

regarding the environment it sees, or an ECG sensor may keep the doctor of certain patient well

informed regarding his heart conditions. Besides medical issues, today is digital era, why not to

exchange our digital proﬁles with each other by shaking hands or have leisure time playing motion

games on a big screen and so on. We can use such networks for understanding behaviors of targeted

3

Figure .1: WBASN for E-Health Solutions

population regarding some predetermined subject. Application arena for BAN is as wide as one

can imagine. For any applications, sensor nodes are to be deployed on body/in body or within

vicinity of a body.

The sensors nodes generate sensed data and this data is used to investigate whatever purpose it

is intend to. At this point WBASNs give researchers a challenge to analyze and evaluate data

precisely. Data being sensed and transmitted continuously from diﬀerent sensors is enormous.

Moreover, analyzing raw data can also be troublesome. Answer of this challenge is data fusion

algorithms.

The concept of data fusion is not new. It is basically a procedure of gathering data acquired

from diﬀerent sources and merging it to reveal a complete picture of an environment or state or

any point of interest. If there is a bulk of information coming from diﬀerent sources, using data

fusion algorithms is a must for quality and integrity of data on which certain decisions are to be

made. There are many algorithms and techniques for eﬃcient data fusion ( (Majumder et al.

2001), Bar-Shalom and Li (1995)Bar-Shalom et al. (1990), Raol (2009), Zou and Sun (2013),

Cho et al. (2013)). However, considering WBASNs, there are certain limitations mainly power

and computational constraints. The data fusion technique that has high computational cost with

precision may not be applicable in certain scenarios where we have low computational power and

vice versa. Such kind of questions make researchers to ponder not only on existing data fusion

algorithms but also modifying or developing new algorithms for WBASNs.

2.1 Data Aggregation and Data Fusion

In any sensor network, data generated due to sensed attribute/s by any sensor is enormous. More-

over, it is prone to corruption as there might be diﬀerent interferences. For example, interference

due to pressure, temperature, EM waves etc. This curried data can not yield good results. Rather

in WBASNs, this can lead to disastrous decisions. Data Fusion is the answer to such failures or

inaccuracies of sensor readings. In literature fusion relates with many terms. We can ﬁnd multiple

4

terminologies regarding fusion of data i.e. information fusion, data fusion, sensor fusion, data

aggregation and sensor integration. These all terms have been deﬁned by their users explicitly

and there is yet no uniﬁed approach. Sensor Fusion normally relates with the fusion of data that

sensors generate whereas, Information and data fusion are accepted as general terms with same

meaning Abdelgawad and Bayoumi (2012).

Joint Directors of Laboratories (JDL) White (1991) deﬁnes data fusion as ”multilevel, multifaceted

process handling the automatic detection, association, correlation, estimation, and combination of

data and information from several sources.”Jayasimha (1994) states that data fusion is ”Combi-

nation of data from multiple sensors to accomplish improved accuracy and more speciﬁc inferences

that could be achieved by the use of single sensor alone”. In the same manner, multisensory fu-

sion deals with the fusion of sensed data by diﬀerent sensors and orchestrate the data into one

presentable format. Hence we can state that data fusion is the process of ﬁnding true values or

reaching a correct decision by resolving conﬂicting sensed values via multiple sources.

Besides data fusion, there is another term, data aggregation that is widely spread in literature

mainly in the domain of Wireless Sensor Networks (WSN). Data aggregation refers to reﬁning the

voluminous raw data sensed by a sensor. Simply stating, it is summarization of whole bulk of

data. Fig. .2 illustrates the concept of multisensory data fusion, Sensor data fusion, and sensor

data aggregation. Data aggregation reduces the data and can not be done for the applications

where precision is demanded. Data aggregation may reduces the amount of data but this may

occur in elimination of important set of data.

Figure .2: Relationship amongst Data Aggregation and Data Fusion

2.1.1 Data Fusion algorithms

Data fusion is an important aspect in Computational Intelligence (CI). This results in making

precise and accurate decisions. There is a huge application arena for data fusion algorithms.

Anywhere we have to deal with data, or bulk of data, anywhere we need precision and data

manipulation in desired manner, Data Fusion Algorithms provide the solution.

5

Data Fusion Algorithms

Inference Feature

Maps

1. Occupancy Grid

2. Network Scans

1. Baysian Interface

2. Damper Shafer

inference

3. Fuzzy Logic

4. Neural Networks

5. Adbuctive Reasoning

6. Semantic Information

Fusion.

Reliable

Abstract

Sensors

1. Fault-Tolerant

Averaging

2. The Fault-Tolerant

Interval Function

Estimation

1. Maximum

Likelihood (ML)

2. Maximum A

Posteriori (MAP).

3. Kalman Filter

4. Particle Filter

5. Complementary

Filter

Blundering Information Fusion

may result in waste of resources

and misleading assessments

Decision is taken based on the knowledge of the

perceived situation .

Inference refers to the transition from one likely true

proposition to another , whose truth is believed

to result from the previous one .

The concept of reliable abstract sensor was

introduced by Marzullo

[1990] to define one of three types of sensors :

concrete, abstract , and reliable abstract sensors .

It is an interval of values

that represents the observation provided by a

concrete sensor.

For some applications , such as guidance

and resource management , it might not be

feasible to directly use raw sensory data .

In such cases, features representing aspects

of the environment can be extracted and

used by the application .

inherited from control theory and use the

laws of probability to compute a process

state vector from a measurement vector

or a sequence of measurement vectors

Figure .3: Data Fusion Algorithms

As shown in Fig. .3, Data Fusion Algorithms (DFA) can be classiﬁed in 4 classes i.e.

Inference, Estimation, Feature Maps and Reliable Abstract Sensing Nakamura et al. (2007).

Inference algorithms are used when decision rely upon the knowledge of perceived circumstances

or situations or events. Inference means transition from one true state to another while the result

is dependent on previous result. Classical Inference algorithms are Baysian Inference Box and

Tiao (2011), Dempster-Shafer Belief Accumulation Theory Gordon and Shortliﬀe (1984). Besides

fuzzy logic McNeill and Thro (2014), Artiﬁcial Neural Networks M¨akisara et al. (2014) and

abductive reasoning Walton (2014) are major inference algorithms.

Feature Maps Algorithms intend to solve such problems where raw sensory data is not appropriate

to use. Instead, certain features are selected amongst whole set of sensed data. Normally Inference

methods are used to extract a feature map. Occupancy Grid Thrun (2003) and Network Scans

Zhao et al. (2002) are two methods that lie in this class of algorithms.

Reliable abstract sensor methods are used in context of time synchronization by maintaining lower

and upper time boundaries Marzullo (1990). Fault Tolerant Averaging Jayasimha (1994) and

Fault tolerant interval Function belong to Reliable Abstract Sensing group.

Estimation Algorithms are based upon control theory and is widely studied in diﬀerent domains.

Most prominent methods of this class are Maximum Likelihood Kubo (1992), Least Squares

Marquardt (1963), Moving Average ﬁlters Sato (2001), Kalman Filters Srinivasan (2015),

Complementary ﬁlters Cockcroft et al. (2014) and particle ﬁlters Gordon et al. (2004).

6

2.2 Smart phones for E-health Monitoring

Technology shift from mobile phone to smart phone is perhaps the fastest technology shift globally.

Smart phones are penetrated deep into every ones lives. They have much more to oﬀer then

mobile phones. Today they have powerful processors, large memory, many built in sensors along

with multiple network interfaces. Discussing cellular technology, GSM is replaced with 3G and 4G

networks oﬀering high bandwidth feasible for numerous applications.

Conﬁning ourselves only to sensory part, we can ﬁnd accelerometers, gyroschopes, magnetometers,

cameras, temperature sensors, GPS, microphones, ECG sensors etc in smart phones. This gives us

an opportunity to use these sensors inspite of expensive and complex sensory units . Using smart

phones, diﬀerent systems are developed for diﬀerent applications. Considering E-health Solutions,

Table. 1 illustrates a few established systems Want (2014).

Table .1: Smart Phones for E-health Solutions

Solution Sensor Types Application

SPA Biomedical sensor, GPS Heathcare suggestions

UbiFit Garden 3D Accelerometer UbiFit Garden’s Interactive Application

Balance Accelerometer, GPS Balancing

CONSORTS-S Wireless Sensor, MESI RF-ECG Healthcare Services

Keeping motion capture or physical activity monitoring in view, sensors such as accelerometers

and gyroscopes are in use for diﬀerent healthcare and assisted living applications. With advent of

smart phones having built in accelerometer and gyroscope sensors have taken spot light in research

arena to use them for the said purpose.

For activity monitoring two sensor data fusion techniques i.e. Kalman and Complementary data

fusion in context with WBASF for WBASNs using smart phones are analyzed in this chapter.

Simulations are conducted in comparison of said algorithms with comparative analysis between

these two estimation based Data Fusion Algorithms (DFAs). According to our results, considering

human body movements Complimentary Data Fusion Algorithm (CDFA) is more appealing with

respect to Kalman Data Fusion Algrorithm (KDFA) due to its simplicity, and accuracy.

3 Orientation Sensing

In WBASNs, activity monitoring and fall detection for elderly is becoming a hot topic. Initially,

we required a stationary camera based complex setup with very limited freedom of movement.

This was replaced with Inertial Measurement Units (IMUs) considering mobility, wearability and

ease of use Bachmann et al. (2001), Zheng et al. (2005). These IMUs are now being replaced

with such smart phones that have built in orientation sensors (Gyroscopes and Accelerometers).

Moreover, they have high computational power and eﬃcient transmitting modules making it more

interesting for WBASF point Lane et al. (2010). The orientation sensors can track or monitor

activities of a human body precisely in accordance with its application and transmits directly on

programmed location.

7

As stated earlier, smart phones contain accelerometers and gyroscopes that can be related with

motion capture systems in form of IMUs. Pascu et al. (2012) Proposed a medical application using

smart phones for ambient health monitoring. The question that can smart phones take place of

IMUs is solved by Pascu et al. (2013) that derives a motion capture using kinematic models and

displayed interpretable data on smart phones screen. They used Kalman ﬁltering for data fusion

of gyroscope, accelerometer and magnetometer sensory data. Besides Kalman ﬁltering, Bayesian

ﬁltering, Central Limit Theorem and Dempster-Shafer other than Kalman and Complementary

ﬁltering are prominent ones.

3.1 Sensor Data Fusion: A Layered Approach

For Sensor data fusion, we have to iterate whole procedure into three major steps as in Khaleghi

et al. (2013) and illustrated in Fig. .4.

•Sensing phase: in which raw data is sensed by the sensor.

•Analysis phase: where decisions are to be made from sensed data.

•Dissemination Phase: accurate information is handed over to user application.

Sensing Phase

Data Sensing, Sample Management

and Feature Extraction

Analysis Phase

Feature Selection, Data Fusion and

Decision Making

Dissemination Phase

Event Propagation/ Notification

Figure .4: Data Fusion Layers

At initial phase i.e. sensing phase, sensed data is processed to acquire diﬀerent features i.e.

mean, variance, min , max etc. These features are submitted to analysis phase. In analysis phase,

required features amongst all are selected, fused together to make a decision. That decision is fed

to dissemination phase from where this event is displayed on application modules.

To understand orientation sensing, we have to understand basic functionality of diﬀerent sensors.

Accelerometer and Gyroscope are the most prominent ones besides Magnetometer and Inclinome-

ter.

Accelerometer are meant to calculate G-force amongst X, Y and Z axis of any body. This sensor

do not inevitably work on the deﬁnition of acceleration as, rate of change of velocity always. For

simple motion based sensing, these sensors are best to use. G-force embraces acceleration owing

to gravity. If the sensor is placed as facing up wards, Z axis reading of accelerometer will be -1.

Fig. .5 brieﬂy describes calculations of an accelerometer.

8

Gyroscopes are meant to calculate angular velocities amongst X, Y and Z axis. This sensor has

no concern of orientation but take care of rotation at diﬀerent velocities. To accurately measure

the orientation of a body, gyroscope and accelerometers have to consult each other to determine

whether body is moving and in which angles. Fig. .5 explains the angular rotations that a gyro-

scope measures.

Orientation of a body with attached accelerometers and gyroscope articulated via quaternion and a

rotation matrix to oﬀer a precise calculation of body placement with respect to global coordinates.

Figure .5: Gyroscope Accelerometer Functioning

4 Orientation Approximation

Major objective of orientation approximation is to guess the rotation amongst coordinate frame

of sensor and rest of the world as precisely as possible. 3D IMUs typically use gyroscopes and

accelerometers to measure acceleration vector and rotational vector in coordinate frame relative to

global coordinates. Orientation approximation is conducted by fusing above mentioned vectors.

4.1 Gyroscope Accelerometer Integration

Gyroscope integration provides an approximation regarding relative rotation given that initial

rotation is known. This angular velocity, calculated by gyroscopes is also directly integrated to

deliver accurate approximation even if the body is moving at high speed. A generalized orientation

approximation algorithm is illustrated in Fig. .6. Mathematically expressing, gyroscope integration

can be modeled as in Eq.(1).[32]

ft=ft−1+1

2dt(0, ~ω)⊗ˆ

ft−1;,(.1)

9

Figure .6: Generalized Orientation Approximation Algorithm

where

ft=approximated orientation

dt= sample period

~ω =angular rate vector calculated in rad/sec

⊗= quaternion multiplication operator.

Whenever, any change in orientation occurs, approximated quaternion must also be normalized to

omit or reduce angular errors that may persists. This integration gives two signiﬁcant problems

i.e.

•Any error in angular rate vector will increase cumulatively.

•Initial orientation of the body is a must to know for relating it with upcoming changes.

Vectors illustrate an approximation of orientation that is relative to global coordinate frame. Com-

bining these vectors and then comparing the resultant with known initial position vectors can

provide us with the rotation occurred. Mathematically, this rotation ”R” can be calculated as in

Eq.(2).

R=vi

~ωi

∀iε(1.....n); ,(.2)

Where

R= Rotation

Vi= Number of sensed vectors

10

~ωi= Reference vectors in global coordinates.

As depicted by Khaleghi et al. (2013) for noisy vector observations, there is no optimal solution

to calculate rotation but by neglecting some of the information in vectors. General algorithm used

for orientation is deﬁned as in Eq.(3) and Eq.(4):

~e =~a

||~a||;,(.3)

R= [~e]T≡ˆ

f;,(.4)

where ~a = acceleration vector.

Using vectors for approximation of orientation gives absolute values. On other hand, if we discuss

accelerometer, it is polluted with noise due to acceleration and gravity phenomenon that lies on a

moving body. Here we will discuss two most widely used data fusion algorithms i.e. Complementary

ﬁltering and Kalman ﬁltering simultaneously.

4.2 Complementary Filtering

Complementary Data Fusion Algorithm (CDFA) is meant to derive one single output by combining

two diﬀerent measurements with diﬀerent noise properties. In focused case, Accelerometer signal

has high frequency noise while gyroscope results contain low frequency noise. This data fusion

technique apply both low and high pass ﬁlters as expressed in Eq.(5) :

Hs=HLP (s)+HHP (s)= 1 (.5)

Using this approach of data fusion, we overcome the delay problem. Mathematically we can express

CDFA equations as in Eq.(6) and Eq.(7):

ft=ft+1

kf0(.6)

ˆ

ft=(f0

t+1

k(f00

t−f0

t) for |kak − 1|< aT

f0

tfor|kak − 1| ≥ aT

(.7)

Where

f0

t= gyroscope integration.

f00

t= vector observation.

k= ﬁlter co-eﬃcient.

aT= threshold for attaining vector observation in linear accelerations.

First part of the Eq.(7) maintains high frequency response while low frequency noise is handled

by latter part of Eq.(7). Filter coeﬃcient plays a vital role of drift cancelation rate control. As the

values of drift cancelation coeﬃcient increases, drift correction gets slower however more accuracy

is guaranteed.

Complementary ﬁlter integrates static truthfulness of accelerometer and gyroscope within vibrant

movements. In comparison with Kalman ﬁlter, it oﬀers a constant gain.

11

4.3 Kalman Filter

For fusing multisensory data, Kalman ﬁltering is one of the most widely accepted algorithm. Neil

Armstrong, reached ”moon” on his spaceship ”Appollo” whose navigation computer follows kalman

ﬁltering. Though recursive in nature, it shows its worth in navigational systems on air crafts and

in ﬁeld of robotics. Mainly this ﬁlter is well suited for whole of the instrument trade that can be

applied in any ﬁeld requiring data fusion. Kalman Data Fusion Algorithm (KDFA) gather past

knowledge of dynamics for prediction of future states. Mathematically KDFA can be expressed as

in Eq.(8) Young (2009).

~xk+1 =A~xk+B~uk+~ωk(.8)

Where

~xk= state vector

A= Transition matrix of prior states

B= state matrix of control inputs

ω= noise vector

If ~ykis a set of any measured state than, it can be expressed as Eq.(9)

~yk=C~xk+~υk(.9)

In Eq.(9), ”C” is the matrix relating with observed state while ~υkis the noise vector.

Given the above mentioned equations, Kalman ﬁlter can be deﬁned by Eq.(10)-(12)as in Young

(2009).

Kk=APkCT(CPkCT+Rk)−1(.10)

ˆ

~xk+1 = (Aˆ

~xk+B~uk) + k(~y +k+ 1 −Cˆ

~xk) (.11)

Pk+1 =APkAT+Qk+APkCTR−1

k+CPkAT(.12)

where;

K= Kalman gain.

P = error covariance matrix.

Q= uncertainty factor of the system.

Rk= covariance matrix of noise vector ~υk

5 Experimental setup

The concept and usability of data fusion in multiple ﬁelds of engineering and computer sciences

is not a new thing. However, with emerging new technologies and applications, the said concept

needs to be modeled in an eﬃcient and progressive manner. Considering Wireless body area sensor

networks, which is rapidly emerging and widely accepted technology, there are diﬀerent sensors

12

0 10 20 30 40 50

−5

0

5

time(s)

x(t)

accellerometer

gyro

actual

0 10 20 30 40 50

−2

0

2

time(s)

x(t)

actual

kalman

0 10 20 30 40 50

−2

0

2

time(s)

x(t)

actual

complementry

Figure .7: CDFA and KDFA on test basis

implanted or attached on a body. There are plenty of applications that need orientation sensors

i.e. Gyroscopes and Accelerometers. Continues sensing results in enormous amount of data which

is to be analyzed precisely in order to get desired and accurate results.

The smart phones today are equipped with numerous sensors and most commonly orientation

sensors, gyroscopes and accelerometers. These sensors can play a vital role in fall detection and

motion capture considering e-health solutions. Sportsmen, mainly bowlers of cricket often gets

backbone injury due to which they have to visit physiotherapist on daily basis. To investigate

improvements, sportsmen have to perform certain biological and motion tests that are expensive

and time consuming as well. If a smart phone is attached over his back that continuously monitor

the bend while walking, sitting or performing any activity can be a better choice for the patient

to monitor rehabilitation process in terms of his injury.

Considering the same phenomenon, an application is developed on the basis of gyroscope and

accelerometer (built in sensors of smart phone) that continuously monitors, display data on smart

phone screen as well as store it in smart phone database.

In this work, we develop such an application using two diﬀerent data fusion techniques i.e. KDFA

and CDFA to compare their results. KDFA rests with high computational cost and brief history

while CDFA is simple and easy to implement. The real time fused data is collected and results are

compared using MATLAB to verify which algorithm performs eﬃciently in said scenario.

6 Kalman and Complementary Filtering

6.1 On Test Basis

Before getting real time data, we, using Matlab compared Kalman and Complementary ﬁlters to

observe computational time, cost and complexity diﬀerences. Above all, the performance accuracy

was also noticed as shown in Fig. .7.

13

As one can easily depict from the Fig. .7, Kalman ﬁltering results are not that accurate with

respect to Complementary ﬁltering. Though, Kalman ﬁltering has brief history but in navigational

systems, where drifts and angular velocities are easier to predict. In wireless body area sensor fusion

(WBASF) it is relatively hard to predict both, angular velocity and degree of movement. On other

hand, Complementary ﬁlter has a constant gain that proves its worth in WBASF.

(a)Accelerometer Reading, X-axis

0 1 2 3 4 5 6 7 8 9

−0.04

−0.03

−0.02

−0.01

0

0.01

0.02

0.03

0.04

0.05

time(s)

angle

x−axis

(b)Accelerometer Reading, Y-axis

0 1 2 3 4 5 6 7 8 9

−0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

time(s)

angle

y−axis

(c)Accelerometer Reading, Z-axis

0 1 2 3 4 5 6 7 8 9

0.7

0.75

0.8

0.85

0.9

0.95

1

1.05

1.1

time(s)

angle

z−axis

Figure .8: Accelerometer Readings

6.2 On Real Time Data

Considering the results obtained in Fig. .7, an application is developed fusing gyroscope and

accelerometer data. The results obtained from accelerometers are depicted in Fig. .8 considering x,

y and z axises.Whereas roll rate, pitch rate and yaw rate of gyroscope are measured as in Fig. .9.

6.3 Comparison

In accordance with Fig. .10 experimental results express that Complementary ﬁlter outperforms

Kalman ﬁlter signiﬁcantly by using less computational and processing power providing more ac-

curacy. Complementary ﬁlter for WBASF can be applied by having only vector and quaternion

mathematical operator. On other hand, traditional Kalman ﬁlter needs enormous matrix opera-

tions including multiplications and taking inverses of these matrices resulting in high computational

and processing cost besides complexity. Moreover, considering WBASF where prediction of next

state is not optimal, Kalman ﬁlter performs badly.

According to the plots illustrated in Fig. .10, alongwith Eq.s (8)-(12), Complementary ﬁlter super-

sedes Kalman ﬁltering in the aspect of computational costs. In simple arithmetic manipulations,

14

0 1 2 3 4 5 6 7 8 9

−1

0

1

time(s)

x(t)

RollRate

0 1 2 3 4 5 6 7 8 9

−0.5

0

0.5

time(s)

x(t)

Pitch Rate

0 1 2 3 4 5 6 7 8 9

−0.5

0

0.5

time(s)

x(t)

Yaw Rate

Figure .9: Gyroscope Reading

and trigonometric notations, Complementary ﬁlter bears less than ten percent computational costs

in accordance with Kalman ﬁltering Simon (2010)Br¨uckner et al. (2014). Table. 2 depicts a com-

parative analysis of Kalman and Complementary ﬁlter techniques for WBASF.

Table .2: Comparison: CDFA and KDFA

Parameters KDFA CDFA

Fusing abilities Theocratically ideal but not for hu-

man body orientation sensing

clear, noise eﬃcient

Approximation re-

quirements

physical properties as mass and in-

ertia required

Rapid estimation of angles, low la-

tency

Coding Complexity Diﬃcult and complex to code Easy to code

Processor Much processor intensive Not very processor intensive

Mathematical

Complexity

Much Complex, require linear alge-

bra and matrices calculations

A bit more theory to understand,

however simpler

Addition and sub-

traction

579 times 36 times

Multiplication and

Division

524 + 46 times 39 + 1 times

7 Discussion

What kind of help such a WBASN can provide if data collected is so complex to analyze and

diagnose accurately. For this purpose eﬃcient data fusion algorithms play vital and very critical

role. In our point of view, without eﬃcient and accurate data fusion techniques WBASNs cannot

work eﬃciently.

15

(a)Orignal Vs KDFA Vs CDFA

0 1 2 3 4 5 6 7 8 9

−5

0

5

time(s)

x(t)

accellerometer

gyro

actual

0 1 2 3 4 5 6 7 8 9

−5

0

5

time(s)

x(t)

actual

kalman

0 1 2 3 4 5 6 7 8 9

−5

0

5

time(s)

x(t)

actual

complementry

(b)KDFA Vs CDFA

0123456789

−2.5

−2

−1.5

−1

−0.5

0

0.5

1

1.5

2

2.5

time(s)

x(t)

Kalman

complementry

Figure .10: CDFA Vs CDFA as WBASF Algorithms

Our work is based on the said statement. Hence Kalman and Complementary structures

considering WBASF are discussed and experimented. Generally, Kalman ﬁltering is more in use

due to its long history however, it fails to provide eﬃcient solutions in sensor fusion for Body Area

Networks considering posture tracking for e-health solution. Besides human posture tracking, no

doubt, KDFA has proven its worth in navigational and robotics trade where prediction for the

next state is not that tricky. Furthermore, calculating and fusing orientation of diﬀerent human

body organs (limbs, legs, back, head and so on so forth) may require diﬀerent process models

each with its own parametric values. This is the major reason that in WBASF, Kalman ﬁlter fails

to predict accurate approximation of next state. Moreover creating predeﬁned process model for

diﬀerent body organs is also a complex task.

Considering Complementary ﬁltering for WBASF, it does not rely on any assumptions for process

dynamics, hence, it does not suﬀer from the problems, Kalman ﬁlter has to face. Having low

complexity and less processing time with zero prediction algorithms, CDFA proved its worth as

can be seen in experimental results over KDFA (Fig. .10). Keeping energy consumption in view,

which is one of the major constraints in WBASNs, again, CDFA surpasses as processor may enjoy

much longer low power sleep timings with respect to Kalman ﬁltering.

16

8 Conclusion and Future Works

WBASF is the hot topic of research arena as, it gives numerous challenges. Advent of smart

phones though have got spot light of researchers to be used as middle ware between tiny sensors

and server database where data has to be stored due to its high computational and transmitting

powers. However, yet, there is a lot of diﬀerences in data fusion algorithms. Kalman data fusing

technique no doubt have proven its metal in previous decades for calculating machine orientations.

This technique predicts future state that is more easily done in machines by having past knowledge

(aircrafts and robots). According to our study, work and experiments, when we discuss humans,

future prediction of Kalman ﬁlter did not prove its worth. More over, its high complexity and

computational costs forbid us to use it as WBASF algorithm. Complementary ﬁlter, in comparison

with Kalman ﬁlter shows its better performance with features of simplicity and low processing as

discussed in Table. 2.

In future, we are going to implement Complementary ﬁlter for orientation based data sensor fusion

on diﬀerent patients that suﬀer from back bone injury and compare their results with actual

rehabilitation tests conducted by physiotherapists.

Acronyms

3G 3rd Generation

4G4rth Generation

BANBody Area Network

CDFAComplementary Data Fusion Algorithm

CIComputational Intelligence

DFAData Fusion Algorithm

ECGElectroardiogram

GSMGlobal System for Modile

ICTInformation and Communication Technology

IMUInertial Measurement Unit

JDLJoint Directors of Laboratories

KDFAKalman Data Fusion Algorithm

WBASFWireless Body Area Sensor Fusion

WBASNWireless Body Area Network

WPANsWireless Personal Area Network

WSNWireless Sensor Network

Please include a list of acronyms with explanations, at the end of the chapter.

17

Glossary

Acceleration Rate of change of velocity by basic physics deﬁnation

Accelerometers a sensor/ instrument that calculates velocity of any moving/ vibrating body

activity monitoring monitoring any activity using any system. In this chapter, it refers to

monitor movement of body using diﬀerent sensors.

Angular velocities by classical physics, it is rate of change of angular position.

Assisted living relates with handling a patient oﬀering independence, dignity and care.

Body Area Netwroks a network of wearable/ implanted inside body devices that usually are

sensors sensing diﬀerent attributes.

Computational constraints restrictions and limitations on computing that may be capability

of device, or power or any other.

Computational intelligence refers to the study of designing intelligent decision making devices

regarding any speciﬁc problem.

Data aggregation summarizing huge bulk of data.

Data fusion process that integerates multiple data to provide one concrete, near to accurate

output.

Data fusion algorithms Algorithms that perform data fusion.

E-health healthcare practice supported electronically using ICT.

Gyroscopes a sensor that measure angular rotational velocity.

IInformation and Communication Technology is a parent terminology that include any

communication device integrated with any domain to attain speciﬁc results.

Inertial Measurement Unit an instrument composed of multiple sensors used to measure

orientation of a body normally used in aircrafts, spacecrafts, ﬁnding any body movement disorders

etc.

Multisensor data fusion refers to fusing data collected from multiple types of sensors.

Orientation sensing states sensing or knowing velocity, acceleration and gravitational forces

with respect of outer world or any speciﬁc point.

Velocity by classical physics, it is rate of change of position with respect to some point of

reference.

Wireless Body Area Sensor Fusion refers to the fusion of data collected by sensors in a

WBAN

Wireless Body Area Sensor Network refers to the network of tiny sensors deployed on/in a

body to sense diﬀerent attributes as per need.

Index

Abductive Reasoning

Acceleration

Accelerometers

Activity Monitoring

Ambient Health Monitoring

18

Angular Velocities

Artiﬁcial Neural network

Assisted Living

BAN

Baysian

CDFA

Centeral Limit Theorm

Classical Inference Methods

Complementary Filter

Computational Constraints

Computational Intelligence

Correlation

Dampster-Shafer Belief Accumulation Theory

Data Aggregation

Data Fusion

Data Fusion Algorithms

e-health

Elderly Assistance

Estimation

Fault Tolerant Averaging

Fault Tolerant Interval Function

Feature Maps

Fuzzy Logic

G-Force

Gravity

Gyroscopes

Healthcare

Hospital Centric Cure

ICT

IMUs

Inclinometer

Inference

Kalman Filter

KDFA

Kinematic Model

Least Squares

Magnetometer

Maximum Likelihood

Motion Capture

Moving Average Filters

Multi facet

Multi Level

Multisensory data

19

Network Scans

Occupancy Grid

Orientation Sensing

Particle Filter

Patient Centric Cure

Physical Activity Monitoring

Recursive

Reliable Abstract Sensing

Sensor Integration

Smart Phones

Velocity

WBASF

WBASN

References

Abdelgawad, A. and M. Bayoumi, 2012: Data fusion in wsn. Resource-Aware Data Fusion Algo-

rithms for Wireless Sensor Networks, Springer, 17–35.

Bachmann, E. R., R. B. McGhee, X. Yun, and M. J. Zyda, 2001: Inertial and magnetic posture

tracking for inserting humans into networked virtual environments. Proceedings of the ACM

symposium on Virtual reality software and technology, ACM, 9–16.

Bar-Shalom, Y., T. E. Fortmann, and P. G. Cable, 1990: Tracking and data association. The

Journal of the Acoustical Society of America,87, 918–919.

Bar-Shalom, Y. and X.-R. Li, 1995: Multitarget-multisensor tracking: principles and techniques,

volume 19. YBS Storrs, Conn.

Box, G. E. and G. C. Tiao, 2011: Bayesian inference in statistical analysis, volume 40. John Wiley

& Sons.

Br¨uckner, H.-P., B. Kr¨uger, and H. Blume, 2014: Reliable orientation estimation for mobile motion

capturing in medical rehabilitation sessions based on inertial measurement units. Microelectronics

Journal,45, 1603–1611.

Cho, T., C. Lee, and S. Choi, 2013: Multi-sensor fusion with interacting multiple model ﬁlter for

improved aircraft position accuracy. Sensors,13, 4122–4137.

Cockcroft, J., J. Muller, and C. Scheﬀer, 2014: A complementary ﬁlter for tracking bicycle crank

angles using inertial sensors, kinematic constraints and vertical acceleration updates.

Gordon, J. and E. H. Shortliﬀe, 1984: The dempster-shafer theory of evidence. Rule-Based Expert

Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project,3, 832–838.

Gordon, N., B. Ristic, and S. Arulampalam, 2004: Beyond the kalman ﬁlter: Particle ﬁlters for

tracking applications. Artech House, London.

20

Jayasimha, D., 1994: Fault tolerance in a multisensor environment. Reliable Distributed Systems,

1994. Proceedings., 13th Symposium on, IEEE, 2–11.

Khaleghi, B., A. Khamis, F. O. Karray, and S. N. Razavi, 2013: Multisensor data fusion: A review

of the state-of-the-art. Information Fusion ,14, 28–44.

Kubo, H., 1992: Maximum likelihood sequence estimation apparatus. US Patent 5,081,651.

Lane, N. D., E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. T. Campbell, 2010: A survey

of mobile phone sensing. Communications Magazine, IEEE,48, 140–150.

Majumder, S., S. Scheding, and H. F. Durrant-Whyte, 2001: Multisensor data fusion for under-

water navigation. Robotics and Autonomous Systems,35, 97–108.

M¨akisara, K., O. Simula, J. Kangas, and T. Kohonen, 2014: Artiﬁcial neural networks, volume 2.

Elsevier.

Marquardt, D. W., 1963: An algorithm for least-squares estimation of nonlinear parameters. Jour-

nal of the Society for Industrial & Applied Mathematics,11, 431–441.

Marzullo, K., 1990: Tolerating failures of continuous-valued sensors. ACM Transactions on Com-

puter Systems (TOCS),8, 284–304.

McNeill, F. M. and E. Thro, 2014: Fuzzy logic: a practical approach. Academic Press.

Nakamura, E. F., A. A. Loureiro, and A. C. Frery, 2007: Information fusion for wireless sensor

networks: Methods, models, and classiﬁcations. ACM Computing Surveys (CSUR),39, 9.

Pascu, T., M. White, N. Beloﬀ, Z. Patoli, and L. Barker, 2012: Ambient health monitoring: The

smartphone as a body sensor network component. InImpact: The Journal of Innovation Impact,

6, 62.

Pascu, T., M. White, and Z. Patoli, 2013: Motion capture and activity tracking using smartphone-

driven body sensor networks. Innovative Computing Technology (INTECH), 2013 Third Inter-

national Conference on, IEEE, 456–462.

Raol, J. R., 2009: Multi-Sensor Data Fusion with MATLAB R

. CRC Press.

Sato, H., 2001: Moving average ﬁlter. US Patent 6,304,133.

Simon, D., 2010: Kalman ﬁltering with state constraints: a survey of linear and nonlinear algo-

rithms. IET Control Theory & Applications,4, 1303–1318.

Srinivasan, V., 2015: Sensor fusion techniques using extended kalman ﬁlter. Int. J. Adv. Eng,1,

18–22.

Thrun, S., 2003: Learning occupancy grid maps with forward sensor models. Autonomous robots,

15, 111–127.

Walton, D., 2014: Abductive reasoning. University of Alabama Press.

Want, R., 2014: The power of smartphones. IEEE Pervasive Computing, 76–79.

21

White, F. E., 1991: Data fusion lexicon. Technical report, DTIC Document.

Young, A., 2009: Comparison of orientation ﬁlter algorithms for realtime wireless inertial posture

tracking. Wearable and Implantable Body Sensor Networks, 2009. BSN 2009. Sixth International

Workshop on, IEEE, 59–64.

Zhao, Y., R. Govindan, and D. Estrin, 2002: Residual energy scans for monitoring wireless sensor

networks. Center for Embedded Network Sensing.

Zheng, H., N. D. Black, and N. Harris, 2005: Position-sensing technologies for movement analysis

in stroke rehabilitation. Medical and biological engineering and computing,43, 413–420.

Zou, W. and W. Sun, 2013: A multi-dimensional data association algorithm for multi-sensor fusion.

Intelligent Science and Intelligent Data Engineering, Springer, 280–288.