E-Health Telecommunication Systems and Networks, 2012, 1, 19-25
http://dx.doi.org/10.4236/etsn.2012.12004 Published Online June 2012 (http://www.SciRP.org/journal/etsn)
A WBAN for Human Movement Kinematics and
ECG Measurements
Ahmed Baraka, Ahmed Shokry, Ihab Omar, Saged Kamel, Tarek Fouad,
Mohamad Abou El-Nasr, Heba Shaban
Arab Academy for Science, Technology & Ma ri time Transport (AASTMT) , Alexandria, Egypt
Email: hshaban@vt.edu
Received April 1, 2012; revised May 10, 2012; accepted May 26, 2012
Biomedical applications of body area networks (BANs) are evolving, where taking periodic medical readings of pa-
tients via means wireless technologies at home or in the office will aid physicians to periodically supervise the p atient’s
medical status without having to see the patient. Thus, one important objective of BANs is to provide the doctor with
the medical readings that can be collected electronically without being in close proximity to the patient. This is done
through the measurement of the patient’s physiological signals via means of wearable sensors. This paper investigates
wireless BAN cooperation via actual measurements of human movement kinematics and electrocardiogram (ECG),
which are believed to prov ide p atien ts with easy h ealthcare for continuous health-monitorin g. The collected information
will be processed using specially designed software, which in turn will enable the patient to send a full medical chart to
the physician’s electronic device. In this way, physicians will have the ability to monito r their patients more efficiently.
Keywords: Body Area Networks (BANs); Electrocardiogram (ECG); Human Gait; and Movement Kinematics
1. Introduction
Body area networks (BANs) are the systems of sensors/
devices that cooperate in close proximity to a person’s
body to provide a benefit to the user. There are multiple
applications of BANs including medical and non-medical
applications. Recently, wireless technology has invaded
the medical area of BANs with a wide range of capa-
bilities. These applications typically use biomedical sen-
sors to monitor the physiological signals of patients, such
as electrocardiogram (ECG), blood oxygen level, blood
pressures, blood glucose, body weight, heart rate, oxygen
saturation, etc [1-6].
Wireless technology enables clinician s to mon itor th eir
patients’ remotely and give them timely health informa-
tion and support. Especially, in emergency situations,
real-time health parameter is crucial. According to the
American Heart Association, treatment of a patient ex-
periencing ventricular fibrillation within the first 12 mi-
nutes of cardiac arrest brings a survival rate of 48% -
75%. On the other hand, long-term health-monitoring
requires intensive and repetitive assessment that could
last for months or even years to regain the lost fu nctions,
such as in the case of rehabilitation. Thus, one of the
main challenges in such a case is being able to monitor
patients for long-times in domestic environments. BANs
provide a promising solu tion for such situation s, however
currently, BAN technology is emerging, and there are a
lot of problems to address. One of the key challenges
associated with BANs is the integration and coordinatio n
of multiple sensors with different app lications [1-6].
This paper’s aim is to investigate wireless BAN co-
operation for human movement tracking and ECG mea-
surements, which are believed to provide patients with
easy healthcare for continuous health-monitoring. In ad-
dition, taking periodic medical readings at home or in the
office will aid physicians to periodically supervise the
patient’s medical status without having to see the patient
via means wireless technologies. The collected mea-
surement data will be processed using specially desig ned
software, which will help sending a full medical record
of the patient to an electronic device in the acquisition of
the physician using wireless technology. Figure 1 shows
a schematics diagram of the implemented WBAN. We
consider a WBAN that uses wireless wearable sensors
for gait kinematics and ECG measurements. The pro-
posed WBAN is assumed to use commercially available
noninvasive wireless sensors, as will be shown in detail
in later sections.
This paper is organized as follows. Section 2 explains
gait analysis, and gives a brief overview of its types and
measurement parameters. Then, Section 3 provides a
short overview of ECG. Section 4 describes the actual
measurements. Future work is provided in Section 5, and
opyright © 2012 SciRes. ETSN
Patien t Mo n ito r
Figure 1. Schematic diagram of the implemented WBAN.
conclusions are given in Section 6.
2. Gait Analysis
Gait analysis is the systematic study of human motion.
Gait analysis is divided into observational and quantita-
tive gait analyses. Clinical g ait analysis is to assist physi-
cians in treating walking disorders.
Clinical gait analysis, also referred to as quantitative
gait analysis, is the measurement of gait characteristics,
where abnormalities in gait ar e identified, and the causes
are postulated, such as pain, injuries, etc. Accordingly,
treatments are proposed. Recently, gait analysis has also
received increased attention and been widely used in
sports [1-6].
2.1. Observational Gait Analysis
Observational gait analysis is primary clinical tool used
by clinicians for observing walking patterns, and abnor-
malities in gait. It is sometimes preferable to physicians
than quantitative gait analysis, but it can be very unreli-
able due to the lack of measurement instruments [1-3 ].
2.2. Quantitative Gait Analysis
Quantitative gait analysis is generally considered to be
the best way used to measure walking performance,
where accurate measurement devices are included, and
simple, and is limited to measuring step length with a
ruler, or determining speed with a stopwatch. It can also
be a very sophisticated process that includes full-body
motion capture (MoCap) with very accurate instrumenta-
tion, and well-equipped laboratories with experienced
personnel. Regardless of the complexity of the method
used, the collected measurement data is used to assess
the quality of gait, and characterize the locomotion [2-6].
Quantitative gait analysis used to fully describe a per-
gait parameters are calculated. The procedure can be very
3. Electrocardiogram (ECG)
as the test that re-
symptoms of heart disease. Fourth, check if the walls of
n’s gait generally includes the measurement of a set of
parameters namely, temporal-spatial parameters, kine-
matics and kinetics. Temporal-spatial parameters gene-
rally include the measurement of the parameters related
to walking distance, speed, etc. Whereas, kinematics is
concerned with measuring the parameters related to the
geometry of motion, such as joint angles. Finally, kine-
tics measures motion parameters that include forces, such
as joint moments [2-9].
Electrocardiogram (ECG) is defined
cords the electrical activity of heart using electrodes at-
tached to the skin, and recorded by an external device.
The typical purposes of using an ECG device are as fol-
lows. First, check the heart’s electrical activity. Second,
determine the cause of unexplained chest pain, which
could be caused by a heart attack. Third, find the cause of
Copyright © 2012 SciRes. ETSN
the heart chambers are too thick. Fifth, check the effec-
tiveness of certain medicines. Finally, check the effi-
ciency of mechanical devices that are implanted in the
heart, such as pacemakers are working to control a nor-
mal heart beat [10].
4. Actual Measurements
al measurements of the
applications. The im-
reless sensors specially designed
gait measurements are taken
matic sensor [11], which is
This section describes the actu
implemented WBAN for medical
plemented WBAN includes body kinematic and ECG
measurements via wireless sensors. It is believed th at the
measurement of body kinematics and ECG will help to
better understand movement artifacts. Moreover, using
wireless sensors connected via means of WBANs will
help monitoring patients for long periods, which are es-
sential in the cases that require long-term monitoring,
such as rehabilitation.
The used sensors are manufactured by SHIMMER,
which are low-power wi
r noninvasive biomedical research. In our motion cap-
ture measurement, we use SHIMMER’s wireless nine-
degrees-of-freedom (9DoF) sensors, shown in Figure
2(a), and for ECG measurements, we use SHIMMER’s
wireless ECG sensor, shown in Figure 2(b). The mea-
surement setup and captured data will be explained in
detail in the follo wing subsections.
4.1. Gait Measurements
In the implemented WBAN,
via SHIMMER’s 9DoF kine
equipped with an accelerometer, magnetometer, and gy-
Figure 2. (a) Wireless 9 Doensor by SHIMMER; (b)
Wireless wearable ECG sensor by SHIMMER.
(a) and (b)
F s
roscope. In our measurement setup, we consider different
measurements for the arms and legs. Figures 3
show the measurement setup for the wrist and leg-shank,
respectively. Figures 4(a) and (b) display the captured
signals from the arm and leg, respectively. The second
measurement scenario included a measurement set of the
left and right upper-arm segments, and the measured
signals have also been recorded. The measurement setup
for the left and right upper-arms are shown in Figures
5(a) and (b), respectively. The corresponding captured
signals are depicted in Figure 6.
4.2. SHIMMER Connect and M
We use two software packages for captured signal dis-
play and processing. For real-time display and data
saving purposes, we use SHIMMER connect software
package developed by SHIMMER [12]. A snapshot of
the program is depicted in Figure 7. Also, we use
MATLAB software [13] for real-time data display, and
post-processing simulations.
Figure 3. (a) Wrist kinemateasurement setup; (b) Leg-
shank kinematic measuremetup.
ic m
nt se
Copyright © 2012 SciRes. ETSN
Copyright © 2012 SciRes. ETSN
Figure 4. (a) Display of real-time captured data from the wrist measurements via SHIMMER connect; (b) Display of real-
time captured data from the leg-shank measurements via SHIMMER connect.
(a) (b)
Figure 5. (a) Left upper armmatic measurement setup.
kinematic measurement setup; (b) Right upper arm kine
Figure 6. Display of real-time captured data from the left and right upper arm measurements using SHIMMER connect.
Figure 7. SHIMMER connect software.
.3. ECG Measurements
asurement, particularly for
5. Future Work
is project will include the assess-
ment of measured data via the implemented WBAN, and
hat includes the measurement of
matics and ECG signals was imple-
For physiological signal me
ECG measurement, we use SHIMMER wireless ECG
sensor. We further display the measured data using
MATLAB for future post-processing simulations. Figure
8 shows a sample captured ECG signal. The captured
signal is associated with normal walking speed.
The future work of th
integrating the measured gait kinematic and ECG signal
into finding more accurate results related to both gait
analysis and ECG measurements.
6. Conclusions
A WBAN network t
human motion kine
mented via means of wireless noninvasive sensors. The
WBAN is suitable for real-time data capturing and pro-
cessing. The implemented WBAN is ultimately suitable
Copyright © 2012 SciRes. ETSN
Figure 8. Captured ECG signal using MATLAB.
r long-time health monitoring and for taking daily
[1] L. D’Astous alenges in
d U. Della Cro
measurements at home or in the office.
nd B. MacWilliams, “Current Chal
Clinical Gait Analysis,” ASB 29th Annual Meeting, Cle-
veland, 31 July-5 August 2002, p. 946.
[2] A. Leardini, L. Chiari, A. Cappozzo ance,
“Human Movement Analysis Using Stereophotogramme-
tryc Part 1: Theoretical Background,” Gait and Posture,
Vol. 21, No. 2, 2005, pp. 186-196.
[3] H. Shaban, “A Novel Highly Accurate Wireless Wearab
. Abou El-Nasr and R. M. Buehrer, “Per-
d R. Buehrer, “Toward a
Human Locomotion Tracking and Gait Analysis System
via UWB Radios,” Ph.D. Thesis, Virginia Tech, Blacks-
burg, 2010.
[4] H. Shaban, M
formance of Ultralow-Power IR-UWB Correlator Re-
ceivers for Highly Accurate Wearable Human Locomo-
tion Tracking and Gait Analysis Systems,” IEEE Global
Telecommunications Conference, Honolulu, 30 Novem-
ber-4 December 2009, pp. 1-6.
[5] H. Shaban, M. Abou El-Nasr an
Highly Accurate Ambulatory System for Clinical Gait
Analysis via UWB Radios,” IEEE Transactions on In-
formation Technology in Biomedicine, Vol. 14, No. 2,
2010, pp. 284-291. doi:10.1109/TITB.2009.2037619
“Reference Range Correlation (RRCR) Ranging and Per-
formance Bounds for on-Body UWB-Based Body Sensor
[6] H. A. Shaban, M. Abou El-Nasr and R. M. Buehrer,
Networks,” Progress in Electromagnetics Research B,
Vol. 35, 2011, pp. 69-85. doi:10.2528/PIERB11082212
[7] M. Abou El-Nasr, H. A. Shaban and R. M. Buehrer, “Key
Design Parameters and Sensor-Fusion for Low-Power
Wearable UWB-Based Motion Tracking and Gait Analy-
sis Systems,” Progress in Electromagnetics Research Let-
ters, Vol. 29, 2012, pp. 115-126.
[8] M. Abou El-Nasr, H. Shaban and R. Buehrer, “Low-
Power IR-UWB Coherent TOA Estimators with Subop-
timal Sinusoidal Templates for UWB-Based Body Area
Networks,” Wireless Networks, Vol. 17, No. 7, 2011, pp.
1641-1648. doi:10.1007/s11276-011-0369-0
[9] H. Shaban, M. Abou El-Nasr and R. Buehrer, “Toward a
Highly Accurate Ambulatory System for Clinical Gait
Analysis via UWB Radios,” IEEE Transactions on In-
formation Technology in Biomedicine, Vol. 14, No. 2,
2010, pp. 284-291. doi:10.1109/TITB.2009.2037619
[10] T. Pawar, N. S. Anantakrishnan, S. Chaudhuri and S. P.
Duttagupta, “Impact Analysis of Body Movement in Am-
bulatory ECG,” 29th IEEE EMBS Annual International
Conference, 23-26 August 2007, PP. 5453-5456.
[11] A. Burns, B. R. Greene, M. J. McGrath, T. J. O’Shea, B.
Kuris, S. M. Ayer, F. Stroiescu and V. Cionca, “SHIM-
MERTM—A Wireless Sensor Platform for Noninvasive
Biomedical Research,” IEEE Sensors Journal, Vol. 10,
Copyright © 2012 SciRes. ETSN
No. 9, 2010, pp. 1527-1534.
[12] Shimmer Discovery in Motion Software, 2012.
[13] Mathworks MATLAB and SIMULINK for Technical
Computing, 2012. http://www.mathworks.com/
Copyright © 2012 SciRes. ETSN