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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
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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
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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
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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.
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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).
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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.
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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.
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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)
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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
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~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.
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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
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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.
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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
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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
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