Multimedia in Healthcare.pptx

University of Information and Communication Technology
Faculty of Engineering
Department of Information and Communication Technology Engineering
Multimedia in Healthcare
Eng- Fatima Asaad Hikmat Al-zaidi
2022/10/12

‫الرحيم‬ ‫الرحمن‬ ‫هللا‬ ‫بسم‬
َ‫و‬ ‫ا‬ً‫نور‬ َ‫ر‬َ‫م‬َ‫ق‬‫ال‬َ‫و‬ ً‫ء‬‫يا‬ ِ
‫ض‬ َ
‫َّمس‬‫ش‬‫ال‬ َ‫ل‬َ‫ع‬َ‫ج‬ ‫ذي‬َّ‫ل‬‫ا‬ َ‫و‬ُ‫ه‬
‫موا‬َ‫ل‬‫ع‬َ‫ت‬ِ‫ل‬ َ‫ل‬ ِ
‫ناز‬َ‫م‬ ُ‫ه‬َ‫َّر‬‫د‬َ‫ق‬
ِّ
‫ل‬ِ‫إ‬ َ‫ك‬ِ‫ل‬‫ذ‬ ُ‫ه‬‫ـ‬َّ‫ل‬‫ال‬ َ‫ق‬َ‫ل‬َ‫خ‬ ‫ما‬ َ‫ساب‬ ِ‫الح‬َ‫و‬ َ‫نين‬ِِّ‫س‬‫ال‬ َ‫د‬َ‫د‬َ‫ع‬
ِ‫ت‬‫اآليا‬ ُ‫ل‬ِّ ِ
‫ص‬َ‫ف‬ُ‫ي‬ ِِّ‫ق‬َ‫ح‬‫ال‬ِ‫ب‬
َ‫مون‬َ‫ل‬‫ع‬َ‫ي‬ ٍ‫وم‬َ‫ق‬ِ‫ل‬
‫العظيم‬ ‫هللا‬ ‫صدق‬

Inscription.
Man was created on earth, and did not live in isolation from the
rest of humanity and at all stages of life there are people who
deserve thanks and the first people to be guided are the fathers
because of their credit for the heavens, their presence is a reason
for survival and peasantin our parents and the hereafter, so I
dedicate this humble research to them first and then to my husband
and companion of the struggle in the course of life and his support
for me in my studies. I dedicate my distinguished professor, Brof. Dr.
Abbas Fadhil al-Jubouri, and all the teachers of the Department of
Information Technology Engineering, and to all those who stood by
me and supported me, even with the encouragement of only my
brothers, friends and friends, to whom I testify that they are the
blessings of the comrades in all things.

Thanks and appreciation.
the first thanks is Allah Almighty, then my parents and my mother
for all their efforts since I was born to this moment, you are
everything I love you in God the most love. I am pleased to thank
all those who have advised me, guided me, guided me or
contributed to the preparation of this research by communicating
with me the required references and sources at any stage. In
particular, I thank my distinguished professor, Dr. Abbas Fadel Al-
Jubouri, for teaching me and guiding me and for choosing the topic.
I also thank all the professors of the Department of Information
Technology Engineering, .

Contents
1. Abstract.
2. Introduction.
3. A remote marker less human gait tracking for e-
healthcare based on content-aware wireless
multimedia communications.
4. Marker less human gait tracking.
5. Where the denotes a threshold.
6. Wireless streaming of remote human gait tracking the
proposed system model.
7. The joint optimization of content-aware wireless
streaming.

8. System experiments.
9. Conclusions.
10.A multimedia telemonitoring network for healthcare.
11.TELMES project aims to develop a securized
multimedia system devoted to medical.
12.Telemonitoring module.
13.Results and discussion.
14.Summary and conclusion.
15.Multimedia-based healthcare.
16.References.

Abstract
As the world population ages and the care ratio (ratio of
healthy young citizens to elderly citizens) is in decline,
monitoring and care of individuals with continuous
recording of medical information in electronic form
remotely or during in-site medical visits is becoming more
and more incorporated in daily life. Multimodal
technologies are constantly being developed to assist people
in their daily life, with a vast range of wearable sensors now
available for monitoring health parameters (e.g. blood
pressure, sweat, body temperature, heart rate etc.),
Multimedia in Healthcare

lifestyle (e.g. monitoring utility use, levels of activity,
sleep quantity and quality etc.), a person’s ability to carry
out activities of daily living.
At the same time, health professionals have integrated
new technologies into their workflow, for example by using
various types of medical imagery to facilitate and support
their clinical practice and diagnosis, and also by examining
data from sensors and home medical devices, which allow
them to remotely care for their patients. Health records and
databases are now enriched with digital multimodal data on
the patients, for which new methods need to be developed
for accurate and fast access and retrieval.

Introduction
It is reported that certain diseases can directly affect
human gait, stride, pace, and overall walking pattern.
Human gait tracking, the process of identifying an
individual by walking manner, can then be widely used in
surveillance, biomedical assistance, physical training and
therapy.
For instance, it can readily be used to provide prognostic
and diagnostic measures of pathological locomotion
biorhythm such as Parkinson's disease, diabetic peripheral
neuropathy, and Huntington's disease.

It can also be utilized for the clinical assessment of
stroke rehabilitation, prosthetic alignment, and the
success of orthopedic interventions such as anterior
cruciate ligament reconstruction.
Therefore, human motion tracking and gait analysis
create an opportunity for early intervention and possibly
the prevention of injury as a result of declining condition
from disease or the natural aging process [1, 2].

However, most existing tracking systems usually rely on
expensive equipment and lengthy processes to collect gait
data in a dedicated biomechanical environment, limiting
their accessibility to small clinics located in remote areas.
On the other hand, the increasing prevalence of
inexpensive hardware such as video phones.
And complementary metal oxide semiconductor (CMOS)
cameras has fostered the development of wireless
multimedia technologies, such as wireless multimedia
sensor networks (WMSNs).

WMSNs not only enhance existing sensor network
applications such as tracking, surveillance, home
automation, and environmental monitoring, but also
facilitate new medical applications such as telemedicine
and advanced health care delivery.
Indeed, telemedicine is a fast-growing application of
clinical medicine, where medical information is transferred
via telephone, the Internet, or wireless networks for the
purpose of consulting, and sometimes remote medical
procedures or examinations.

Telemedicine can be integrated with third-generation
(3G) broadband multimedia networks to provide
ubiquitous e- healthcare services. Furthermore, remote
monitoring is a new technology emerging to improve
disease treatment and lower medical costs.
The essence of remote monitoring is to enable
assessment of an individual's medical status in real time
regardless of his or her location, and to allow a doctor or a
computer to view the information anywhere to aid
diagnosis, observe how a treatment is working, or
determine if a condition has become acute.

Therefore, an e-healthcare platform based on wireless
multimedia technologies for remote marker less human
motion tracking and gait analysis will significantly
improve current clinical medicine practices, especially
for small clinics located in remote areas. Patients in
remote areas can also get online screening or a
preliminary diagnosis before they are physically
transported to a fully equipped medical center to save
time and resources.
By reducing the number of visits to clinics or care
facilities, medical costs can be significantly reduced, and
convenience and care quality are highly improved.

In this article we present a new marker less human
gait tracking based on content-aware wireless streaming
for remote tracking, as shown in Fig. 1.
The proposed system first extracts the human motion
region accurately by jointly utilizing properties of
interframe relations, as well as spectral and spatial inter-
pixel-dependent contexts of the video frames.
Thus, it does not need the expensive equipment in the
traditional pre- designed intrusive marker-based
environment (Fig. 2).

Then, based on content aware analysis results, collected
gait data are transmitted to the medical center for speedy
or real-time medical prognosis and diagnosis through
wireless networks.
A proposed quality-driven distortion-delay framework is
adopted to guarantee user-perceived video quality at the
receiver end. Specifically, video coding parameters at the
application layer, and a modulation and coding scheme at
the physical layer are jointly optimized through a minimum
distortion problem to satisfy a given playback delay
deadline.

In other words, the optimal combination of encoder
parameters and adaptive modulation and coding (AMC)
scheme are chosen to achieve the best fidelity of the
collected gait data.
Also, the extracted human gait region can be coded with
finer parameters, and be transmitted with better
protection than the insignificant background area under
the given quality of service (QoS) requirement.

Experimental results using H.264/AVC have shown the
validity and effectiveness of the proposed system.
The remainder of this article is organized as follows.
We present the procedures for marker less human
motion tracking.
We also discuss the proposed optimized delivery of
wireless streaming based on the content-aware analysis
results of the previous section.
We then describe the system environment as well as
the experimental results. Finally, we conclude the
article.

A remote marker less human gait tracking for e-healthcare based
on content-aware wireless multimedia communications
Remote human motion tracking and gait analysis over
wireless networks can be used for various healthcare
systems for fast medical prognosis and diagnosis.
However, most existing gait tracking systems rely on
expensive equipment and take lengthy processes to
collect gait data in a dedicated biomechanical
environment, limiting their accessibility to small clinics
located in remote areas.

In this work we propose a new accurate and cost-
effective e- healthcare system for fast human gait tracking
over wireless networks, where gait data can be collected
by using advanced video content analysis techniques with
low-cost cameras in a general clinic environment.

In this way the encoder behavior and the modulation and
coding scheme are jointly optimized in a holistic way to achieve
the best user-perceived video quality over wireless
networks.
Experimental results using H.264/AVC demonstrate the validity
and efficacy of the proposed system.
Furthermore, based on video content analysis, the extracted
human motion region is coded, transmitted, and protected in
video encoding with a higher priority against the insignificant
background area to cope with limited communication bandwidth.

Marker less Human Gait Tracking
Given a gait video sequence, the human gait region
contains valuable information for gait analysis and
medical examinations. In contrast, the background area
usually provides much less useful knowledge.
However, according to our study, for a video frame of
the collected human gait data, the background area
usually comprises more than 50 percent of the whole
video frame area.

Transmitting the background area and the human gait
region without differentiation is not only unnecessary but
also wasteful of precious wireless network resources.
Therefore, the identification of human gait region
through the proposed content-aware analysis techniques
is a meaningful task.
The result provides a foundation for wireless
transmission of remote human gait tracking by prioritizing
the human gait region against the unimportant
background area under the bandwidth-limited
environment.

Further- more, the proposed marker-less human
motion tracking system makes gait tracking much less
dependent on dedicated gait tracking facilities.

Figure 3 shows the proposed procedures for marker
less human gait tracking and the corresponding results,
where background subtraction and contextual
classification play major roles.
Through these two steps, the temporal inter frame
correlation of the video clip, the spectral and spatial
inter-pixel dependent contexts of the video frames are
cooperatively utilized, making it possible to accurately
track the human gait region in a marker less environment.

Background Subtraction- The rationale of back ground
subtraction is to detect the moving objects from the
difference between the current frame and a reference
frame, often called the "background image.
Therefore, the background image is ideally a
representation of the scene with no moving objects and is
kept regularly updated so as to adapt to the varying
luminance conditions and geometry settings [3].

Given a video sequence, the objective of background
subtraction is to detect all foreground objects (i.e., the
human gait region in this article).
The naive description of the approach is to depict the
human gait region as the difference between the current
frame Fri and the background image Bgi:

where The denotes a threshold
However, the background image is not totally static.
Therefore, before this approach can actually work,
certain factors need to be adapted to, which includes
illumination changes, motion changes, as well as
sometimes changes in background geometry.
Over time, different background objects are likely to
appear at the same pixel location. Sometimes the
changes in the background object are not permanent
and appear at a rate faster than that of the background
update.

To model this scenario, a multivalued background model
can be adopted to cope with multiple background objects.
Therefore, algorithms such as the proposed Gaussian
Mixture Model (GMM) can define an image model more
properly as it provides a description of both foreground
and background values [4]. The result of this step using
GMM is illustrated in Fig. 3b.
Contextual Classification- The objective of classification is
to classify a video frame by the object categories it
contains.

Supervised classification is a type of automatic
multispectral image interpretation in which the user
supervises feature classification by setting up prototypes
(collections of sample points) for each feature class to be
mapped.
A supervised contextual classification that utilizes both
spectral and spatial contextual information can better
discriminate between pixels with similar spectral
attributes but located in different regions. First, in many
images, especially those remotely sensed images, object
sizes are much greater than the pixel element size.

There- fore, the neighboring pixels are more likely to belong to
the same class, forming a homogeneous region. Furthermore,
some classes have a higher possibility of being placed adjacently
than others, so the information available from the relative
assignments of the classes of neighboring pixels is also very
important. By using both spectral and spatial contextual
information, the speckle error can effectively be reduced, and the
classification performance can be improved significantly.
Nonetheless, this type of classification also suffers from the
problem of small training sample size, where the class conditional
probability has to be estimated in the analysis of hyper spectral
data.
Therefore algorithms such as adaptive Bayesian contextual
classification that utilizes both spectral and spatial inter-pixel-
dependent contexts to estimate the statistics and classification can
be adopted for accurate classification [5].

This model is essentially the combination of a Bayesian
contextual classification and an adaptive classification procedure.
In this classification model only inter-pixel class dependency
context is considered, while the joint prior probabilities of the
classes of each pixel and its spatial neighbors are modeled using
Markov random fields (MRF) [6]. The result of this step is shown in
Fig. 3c.
Region Growing- At this stage, shown in Fig. 3d, density check is
adopted to combine the results of the previous two steps to form
the continuous human gait region. As long as a homogeneous
region achieved from stage 2 contains more than a threshold
percentage of human motion pixels obtained from stage 1, this
region is regarded as the human gait region. Otherwise, it falls
into the background area.

Morphological Operations and Geometric Corrections - Results
from the previous stage contain undesired noises and holes. As
shown in Fig. 3e, morphological operations use dilation and
erosion to populate the holes in the human motion region and
remove the small objects in the background areas.
Then, geometric correction can be performed horizontally and
vertically to further remove noises for the accurate achievement of
human gait [7]. Finally, we achieve the desired results of the
extracted human gait region as displayed in Fig. 3f.

Wireless Streaming of Remote Human Gait Tracking The Proposed
System Model
Based on the proposed marker less human motion tracking
results, we also develop a unified quality- driven optimization
system of wireless streaming for delay-bounded human gait
transmission. Figure 4 illustrates the proposed system model,
which consists of an optimization controller, the marker- less
human gait tracking module, a video encoding module, as well as
the modulation and coding module. To increase the overall video
quality, we first adopt the proposed methods mentioned earlier to
identify the human gait region in a marker- less environment. Due
to the different contribution to gait analysis from the human
motion region and the background area, the human gait region can
be coded at finer quality at the video encoder of the application

layer, and the packets of the human gait region can use smaller
constellation sizes and lower channel coding rates to guarantee
the required packet error rate at the physical layer.
By redistributing the limited resources needed for encoding and
transmission according to the video content, the overall quality of
real-time human gait video delivery over wireless networks will
be significantly improved. Furthermore, the controller is the core
of the proposed system, which can jointly optimize the key system
parameters of the video codec at the application layer, and the
modulation and coding schemes at the physical layer. Therefore,
through these parameters, the controller can control the behaviors
of the video encoder and the modulation and coding module.

More important, adjusting with coordination the system
parameters of the video encoder residing in the application layer
and the modulation and coding scheme residing in the physical
layer can greatly enhance overall network performance.
As shown in Fig. 5, the system performance in terms of video
distortion is jointly decided by the encoder behavior (i.e.,
quantization step size, or QP) and the packet loss rate.
Additionally, packet loss rate is determined by bit error rate (BER),
which is then collectively affected by the channel quality and AMC
scheme. Therefore, all related system parameters can be
holistically optimized toward achieving the best possible video
quality under a given delay constraint.

For example, when a wireless channel is experiencing bad
quality, the time-varying channel information can be used to
dynamically adapt the AMC scheme to minimize the packet loss
rate, enhancing the received video quality over wireless networks.
Thus, the proposed cross-layer joint optimization is able to
choose the optimal set of parameter values to achieve the best
received video performance, providing a natural solution to
improve overall system performance for wireless streaming of
remote human gait tracking.

The Joint Optimization of Content-Aware Wireless
Streaming
At the video encoder, for hybrid motion-compensated video
coding and transmission over loss channels, each video frame is
generally represented in block-shaped units of the associated
luminance and chrominance samples (16 x 16 pixel region) called
macro blocks (MBs). In the H.264 codec MBs can be either intra-
coded or inter-coded from samples of previous frames [8]. Intra-
coding is performed in the spatial domain, by referring to
neighboring samples of previously coded blocks to the left and/or
above the block to be predicted.

Inter-coding is performed with temporal prediction from samples
of previous frames. Many coding options exist for a single MB, and
each of them provides different rate- distortion characteristics. In
this work only predefined MB encoding modes are considered,
since we want to apply error-resilient source coding by selecting
the encoding mode of each particular MB.
This is crucial to allow the encoder to trade off bit rate with error
resiliency at the MB level. For real-time source coding, the
estimated distortion caused by quantization, packet loss, and error
concealment at the encoder can be calculated by using the
Recursive Optimal Per-Pixel Estimate (ROPE) method, which
provides an accurate video-quality-based optimization metric to
the cross-layer optimization controller [9].

At the physical layer, the BER pem is decided by the dynamically
chosen mode of AMC, which has been advocated to enhance the
throughput of future wireless communication systems at the
physical layer [10]. With AMC, the combination of different
constellations of modulation and different rates of error control
codes are chosen based on the time-varying channel quality.
For example, in good channel conditions, an AMC scheme with
larger constellation sizes and high channel coding rate can
guarantee the required packet error rate, which means that AMC
can effectively decrease the transmission delay while satisfying the
packet loss rate construing. Each AMC mode consists of a pair of
modulation scheme a and FEC code c as in Third Generation
Partnership Project (3GPP), HIPERLAN/2, IEEE 802.lla, and IEEE
802.16 standards. Furthermore, we adopt the following
approximated BER expression:

where m is the AMC mode index and is the received SNR.
Coefficients am and bum are obtained by fitting Eq. 2 to the exact
BER as shown in Fig. 5. Therefore, the expected mean squared
error (MSE) between the received pixels and original pixels of the
video frames can be adopted as the

distortion metric [9]. Thus, the expected distortion Ed(ρ) accurately
calculated by ROPE under instantaneous network conditions, which
is represented by packet loss rate ρ, becomes the objective
function in the proposed optimization framework.
Packet loss rate ρ can be further calculated from BER pme as long
as the packet size is known. Meanwhile, the transmission delay
ttrans which is constrained by the given frame delay bound Tf, can
be represented by bandwidth Be and data bit rate Rm. Finally, the
problem can be formulated as a minimum distortion problem
constrained by a given frame delay bound.

By eliminating from the potential solution set the parameters that
make the transmission delay exceed the delay constraint, the
constrained problem can be relaxed to an unconstrained
optimization problem. Furthermore, most decoder concealment
strategies introduce dependencies among slices. For example, if
the concealment algorithm uses the motion vector of the previous
MB to conceal the lost MB, it would cause the calculation of the
expected distortion of the current slice to depend on its previous
slices.
Therefore, we can assume that the current slice depends on its
previous z slices (z ≥ 0). Then, given the current decision vectors,
the selection of the next decision vector is independent of the
selection of the previous decision vectors, which makes the future
step of the optimization process independent of its past steps,

Forming the foundation of dynamic programming. Thus, the
problem can be converted into and solved as a well-known
problem of finding the shortest path in a weighted directed acyclic
graph (DAG) [11]. In this way the optimization problem is efficiently
solved [12].
System Experiments
In the experiments video coding is performed by using the
H.264/AVC JM 12.2 codec, where the gait video is recorded through
an ordinary video camera in an indoor environment, as shown in
Fig. 3. The frames of the recorded QCIF sequence are coded at
the frame rate (Reframe) of 30 frames/s, where each!-frame is
followed by 9 P-frames. We set one packet to be one slice (one row
of MBs). When a packet is lost during transmission, we use the
temporal-replacement error concealment strategy.

The motion vector of a missing MB is estimated as the median of
motion vectors of the nearest three MBs in the preceding row. If
that row is also lost, the estimated motion vector is set to zero.
The pixels in the previous frame, pointed to by the estimated
motion vector, are used to replace the missing pixels in the
current frame.
Furthermore, we adopt the Rayleigh channel model to describe
signal-to-noise ratio (SNR) ƴ statistically. For channel adaption, we
assume the channel is frequency flat, remaining time invariant
during a packet, but varying from packet to packet. Therefore,
AMC is adjusted on a packet-bypacket basis. Besides, we also
adopt perfect channel state information at the receiver, which is
fed back to the transmitter without error or latency. For the joint
optimization of QP and AMC, we allow QP to range from 1 to 50
and AMC to be chosen from the six available schemes in Fig. 5.

We use NS-2 and MATLAB for system experiments. A serial multi-
hop network topology is adopted using NS-2. The bandwidth and
the propagation delay on .each link are fixedly set to 106
symbols/s and 10 µs, respectively.
The expected video quality at the receiver is measured by the
average of the peak SNR (PSNR) of the whole video clip. We
compare the PSNRs, under the same network conditions, of the
reconstructed video sequences at receiver side achieved by using
the proposed system to those achieved by using the existing
system of non-content-aware analysis, where the video clip is
transmitted with fixed QP and AMC scheme. In the experiments,
we set QP to 20 and AMC to 3 for the existing system, respectively.
The relation between playback deadline Tt and frame rate Rframe
meets Eq. 4:

On the basis of this, we consider three different playback
deadline values, 20 ms, 30 ms, and 40 ms, respectively. The
received video quality achieved through the proposed system in
the experiments compared with that through the existing system is
demonstrated in Fig. 6. We can observe that in the proposed
system, the human motion region based on content-aware
analysis has 3-5 dB PSNR improvement over the existing system.
Meanwhile, the performance gain of the human motion region
over the existing system is even larger in the case of 20 ms than
that in the other two cases, indicating that the more stringent the

single-packet delay deadline, the more PSNR improvement of the
human motion region the proposed scheme can achieve. In other
words, the proposed framework is extremely suitable for delay-
stringent wireless networks.
To explain this, in a more stringent environment, the packet is
more likely to miss the playback deadline. Therefore, more error
concealment will be used to play back the video, resulting in
degradation of the user-received video quality. By adopting the
proposed scheme, the optimized parameters can be chosen
dynamically to try to meet this playback deadline. Accordingly,
higher PSNR gain can be achieved. However, in a less stringent
environment, more packets can meet the delay bound even without
using the proposed optimization scheme, so there is less
opportunity for the proposed scheme to take effect.

Therefore, the proposed joint optimization system can choose
the best coding parameters and the optimal AMC scheme to
decrease packet loss rate by dynamically adapting to the varying
channel quality, while trying to avoid missing the packet playback
deadline.
Additionally, in the experiments the content aware analysis helps
reduce the amount of video traffic by around 50 percent
compared to the existing non-content-aware analysis system.

Conclusions
In this article we propose an e-healthcare system based on video
content analysis and quality-driven content-aware wireless
streaming for remote human gait tracking. The proposed scheme
can significantly reduce the reliance on traditional marker-based
gait data collection facilities, providing a low-cost high-accuracy
gait tracking system. A distortion-delay framework has been
proposed to optimize the wireless streaming for delay-bounded
retrieval of the collected video data, where key system parameters
residing in different network layers are jointly optimized in a
holistic way to achieve the best user-perceived video quality over
wireless environments.

Experimental results have demonstrated that the proposed
system can provide great convenience and cost effectiveness for
fast prognosis and diagnosis of pathological locomotion biorhythm
over resource-constrained wireless networks.

A Multimedia Telemonitoring Network for Healthcare
TELMES project aims to develop a securized multimedia system
devoted to medical consultation teleservices. It will be finalized
with a pilot system for a regional telecasters network that connects
local telecasters, having as support multimedia platforms. This
network will enable the implementation of complex medical
teleservices (teleconsulations, telemonitoring, homecare, urgency
medicine, etc.) for a broader range of patients and medical
professionals, mainly for family doctors and those people living in
rural or isolated regions. Thus, a multimedia, scalable network,
based on modern IT&C paradigms, will result. It will gather two
inter-connected regional telecasters, in Iasi and Pitesti, Romania,
each of them also permitting local connections of hospitals,

As communications infrastructure, we aim to develop a combined
fix mobile-internet (broadband) links. Other possible
communication environments will be GSM/GPRS/3G and radio
waves.
The electrocardiogram (ECG) acquisition, internet transmission
and local analysis, using embedded technologies, was already
successfully done for patients’ telemonitoring.
Diagnostic and treatment centers, as well as local networks of
family doctors, patients, even educational entities.

TELMES project aims to develop a securized multimedia system
devoted to medical.
Keywords-Healthcare, telemedicine, telemonitoring, ECG analysis.
In spite of decreased mortality, coronary artery disease still
remains the leading cause of death almost all over the world. The
existence of silent myocardial ischemia emphasizes the need for
monitoring of the asymptotic patient. Extended patient monitoring
during normal activity has become increasingly important as a
standard preventive cardiologic procedure for detection of cardiac
arrhythmias, transient ischemic episodes and silent myocardial
ischemia.

Existing “halter” devices mostly record "24-hour activity" and
then perform off-line record analysis, so they are not real-time.
The task may also be achieved by telemedicine (enabling medical
information-exchange as the support to distant.
This work was supported by a grant from the Romanian Ministry
of Education and Research, within CEEX program (www.mct-
excelenta.ro), contract No. 604/645/21.10.2005.
H. N. Costin, C. Rotariu, and B. Dionisie are with the Faculty of
Medical Bioengineering, Univ. of Medicine and Pharmacy, Iasi,
Romania, 700098, str. Universitati 16, (phone: +40-232-213573; fax:
+40-232-211820; e-mail: hcostin@gmail.com).

S. Puscoci is with the National Institute of Telecommunications,
062203, B-dul Precise Nr.6, sector 6, Bucharest, Romania. (e-mail:
sorp@quickweb.ro).
M. C. Compose is with the “Stefan cell Mare” National College,
Suceava, Romania, (e-mail: monicacimpoesu@yahoo.com).
(decision-making) and telemonitoring (enabling simultaneous
distant-monitoring of a patient and his vital functions), both having
many advantages over traditional practice.
A telemonitoring network (Fig. 1) devoted to medical
teleservices, will enable the implementation of complex medical
teleservices for a broader range of patients and medical
professionals, mainly for family doctors and those people living in
rural or isolated regions.

Doctors can receive information that has a longer time span than
a patient's normal stay in a hospital and this information has great
long-term effects on home health care, including reduced expenses
for health care. Physicians also have more accessibility to experts,
allowing the physician to obtain information on diseases and
provide the best health care available. Moreover, patients can thus
save time, money and comfort.
As for patient monitoring, we propose the development of a
flexible environment based on an acquisition module and an
embedded system for real-time bio signals processing and
transmission through Internet, GPRS/3G (mobile telephony) or
radio networks already existing in each Romanian county.

I. TELEMONITORING MODULE.
A. General Structure.
Our patient telemonitoring module is based on an ECG / bio
signal acquisition module and an embedded system, for real-time
signal processing and transmission through Internet (Fig. 2). It is
built by using custom developed hardware, open source and
application software.
For instance, the monitoring device could be used either for
acquisition of anomalous ECG sequences (e.g. with arrhythmic
events, ST segment deviation etc.),

and storing to a compact flash memory, as a warning device during
normal activity, or an exercise stress test.
The heart of the module is an 8–bit microcontroller (uPSD3234A
from ST Microelectronics). It has an 8032 compatible UCP capable
of being clocked up to 40 MHz. The μPSD has a memory structure
that includes two independent Flash memory arrays, main (256 Kb)
and secondary (32 Kb), capable of read-while-write operation. It
also contains a large SRAM memory (8 Kb) on chip, with battery
back-up option for RTOS and communication buffers. The other
features of the μPSD include: communication interfaces such as
USB v1.1 Low Speed (1.5Mbit/s), I2C Master/Slave controller
running up to 833 kHz, SPI Master controller, two UARTs with
independent baud rate, IrDA Protocol up to 115 k baud, 4channel
8-bit A/D Converter and 5 PWM channels.

The Patient module is connected to a medical device such as
Wrist Clinic™ or Monoclinic™ via the USB connector and receives
the data. An external USB camera can be connected to a secondary
USB connector in order to receive images from the patient. Data
can be stored temporarily on the μPSD internal memory or in a
Data Memory which is an external memory chip capable to store
up to 32Kb.
The patient module offers the possibility to make an audio
connection with the patient by using the microphone and the
speaker connected to μPSD through an external A/D and D/A
converters.
The ETHERNET controller is RTL8019AS, one of the modern
implementations of the NE2000 standard.

It integrates 16 Kbytes of SRAM, modulator and demodulator for
the physical interface, Ethernet protocol controller, memory
interface, and many other functions.
The data collected from the Medical devices, waveforms and
parameters, can be displayed by using a popular graphical module
(128 x 64 pixels) before sending them to a Telemedical Centre by
using the Internet connection or by using the GSM Modem.
A. Software.
The software working on the Patient Module collects the data
from medical devices and video/audio sources, computes the
parameters and displays them on the LCD Display activates the
alarms and sends the results to the Telemedical Centre.

The device has as main features: real-time ECG / bio signal
acquisition and processing, executes the operator’s commands,
monitors the system’s overall performance, acts in emergency
situations, and aids the diagnostic.
To make a simple software implementation, we choose to use the
standard TCP/IP network protocol as the link provider, a scalable
and economically feasible tool.
For DAQ applications in real-time, such as ours, one must use
real time (RT), multitasking operating systems. A modern and
economic solution is to choose an open source (free) RTOS, such as
RT-Linux. It is comprised of a small RT kernel which runs: (i) a C/C++
RT process at top priority, and (ii) the standard Linux kernel as a
fully preempt able low priority task. High speed (low interrupt
latency)

and predictable timing are achieved by limiting the RT process to
functions that are essential to real time.
B.ECG Acquisition and Processing.
The most important ECG phases for morphological analysis are[1]:
• P-wave (representing contraction of the atria);
• QRS complex (representing contraction of the ventricles);
• T-wave (representing the recovery of the ventricles).
Typical ECG processing algorithms consist of the following steps:

a) Initialization - used to determine initial signal and timing
thresholds, positive/negative peak determination, automatic gain
control, etc.
b) Filtering - this is performed first as analog filter on ECG
amplifier board, and then as digital filter on acquisition board. In
addition, a 50 Hz notch filter is used to reduce power line
interference.
c) QRS complex detection - reliable detection of R-peak is crucial
for morphological analysis [3].
d) Baseline correction - compensates for low-frequency ECG
baseline drift.
e) ST segment detection [6].

C. Detection of QRS Complexes
An adaptive thresholding technique with search back serves as
the primary method for QRS detection. The thresholds are based
on the most recently detected signal and noise levels to react to
changes in the patient’s heart rate, as well as to signal and noise
levels.
The QRS complex is the most significant feature in the ECG signal.
Being characterized by sharp slopes, its duration is about 70 – 130
msec and its energy spectrum is mostly between 1 and 40 Hz. The
input of the QRS detector is the digital ECG signal, sampled at 250
Hz and quantized with 12 bits/sample by A/D converter. The
outputs are the limits of the QRS complex (QRSon and QRSoff), the
location of the R wave, and location of the QRS peaks and notches
(if they exist) of every beat (complex) [4], [5].

The QRS detection algorithm consists of three steps:
(1) coarse QRS limits determination; (2) peaks and notches
determination, and (3) exact limits determination.
D. ST Segment Analysis.
The ST-segment begins 40 msec after the R-peak in the event the
heart rate is more than 100 bpm, or 60 milliseconds after the R-
peak otherwise. ST-segment has normally a predefined length of
160 milliseconds. The normal Segment template is constructed for
each patient as the average of the first ten normal ST-segments.
Baseline drift is compensated according to the slope between the
isoelectric levels of the two beats. Standard annotated databases,
such as the European ST-T Database and the MIT/BIH Arrhythmia
Database, provide means for algorithm evaluation. In order to
compute ST-segment length, a T wave detector must be
implemented [2 ].

E. Data Compression and Error Rate.
Experiments revealed the necessity for data compression, in
order to make a real-time ECG transmission. We used Linux zip
programmed, that yields about 2:1 average compression ratio by
means of Lempel Zip algorithm.
Table I presents results obtained for a resolution of 12
bits/sample and 250 Hz sampling rate, with 3 leads ECG. In this
way, only 6 KB/s bit rate is enough for a real-time 3 leads ECG
transmission!

The quality of data transmission was evaluated by computing
PRD (percentage rate of distortion, a kind of root mean square),
according to formula (1) below. Table II shows a PRD level under
10%, a value accepted by clinicians for expressing a correct
diagnosis.

II. RESULTS AND DISCUSSION.
A. The whole telemonitoring system acts as a client-server
application. The server module includes: a database server (using
MySQL and open sources for server procedures, tables,
restrictions coming from “client” application); an
administration/control module that supervises general dataflow;
an access/security module; a parameters configuration module
a.s.o. Also, it uses HTML and HTTP to send most up to date
information on heart care to clients.

B. The client module comprises the software working on the
expert's computer. It is implemented by using Java applets and has
the following facilities: GUI (Graphic User Interface) for ECG
monitoring (Fig. 3); displays the patient’s ECG in real-time and the
extracted ECG segments data; communicates the experts'
commands (e.g. remote selection of the ECG lead) and medical
decisions to the physician/patient. Also, some off – line processing
algorithms are implemented, such as: advanced filtering;
morphologic ECG analysis (intervals, amplitudes, electrical axes),
average complexes with measurement reference markings; heart
rate variability analysis, etc.

We designed and prototyped the monitoring unit for acquisition
and real-time ECG processing, the software implementation for the
Internet connectivity (the embedded TCP/IP subsystem), and the
software for displaying ECG information on the medical doctor’s
computer. The average reconstruction error of the ECG signal is
about 4.6%. We also tested various algorithms for morphologic ECG
analysis with good results on MIT/BIH Arrhythmia Database (Table
III). The utility of our system is as follows.
1.The monitoring.
Medical monitoring – to watch the clinical / preclinical
parameters of a patient in order:
(a) to decide upon a oncological, etiological or prognostic
diagnosis and to make a treatment decision, or (b) to assess the
efficiency / to correct a treatment plan.

2.Pre-diagnostic monitoring.
It is used in order to establish a oncological, etiological or
prognostic diagnosis, in the case of discrete, infra-clinical, atypical
manifestations of diseases or for obvious signs in case of stress
tests / daily conditions. A single parameter in conditions of
interrupting the medication is motorized. E.g.: Halter monitoring of
ECG in arrhythmias, arterial pressure in border hypertension
syndrome.
3. After-diagnostic monitoring (I).
(a) Vital functions monitoring: cardio-vascular, respiratory or vital
nervous centers functions in case of :
- shock syndrome (cardiac, hypovolemic, politraumatic, septic
emic, anaphylactic, post-combustion) ;

- comas (traumatic, metabolic – hepatic, uremia, diabetic);
(b) only in emergency hospital services, in situations that needs a
rapid therapeutic reaction;
(c) multi-parameters monitoring devices with alarm systems
released by pathological patterns or critical values of: heart or
respiratory rate, oxygen saturation, central arterial or venous
pressure.
4. After-diagnostic monitoring (II).
Treatment monitoring – posology: - establishes the dose or the
efficiency a drug; - establishes the circadian rhythm of maximum
effect of a drug; - establishes the secondary effects or adverse
effects, or effects of association the drugs; - it is monitor zed the
improving of functional parameter under treatment and it make
the decision to change the drug or to modify the dose

; - e.g., respiratory flows MEF50, FEV1 under β-agonist treatment
in Bronchial Asthma; arterial tension after ant hypertension drugs.
5. After-diagnostic monitoring (III).
(a) Monitoring the recovery and the maintenance of remaining
functions:
- cardiac, respiratory, (Insufficiency Syndromes) or
- locomotion (lack of force / articular mobility – dynamometry,
goniometry.
(b) Monitoring the recovery with specific functional stress
testing: cycloergometer treadmill.

6. Special monitoring.
- Telemetric monitoring for– Psychiatric patients (dementia with
dromomania);
- Persons in extreme environment with physical stresses
(deserts, under water, extra-terrestrial, calamities);
- Sportsmen training;
- Periodical monitoring the health status.
(c) Alarm feedback mechanism to watch the level of effort and
risk of decompensating.

III. SUMMARY AND CONCLUSION.
Real time personal ECG monitoring, as an important application
of telemonitoring system, requires devices with high peak
performance and low power consumption. High performance of
RT-Linux development environment allows high speed multitasking
procedures and real time signal processing. The proposed system
could be used as a warning system (Halter-type) for monitoring of
arrhythmia or ischemia during normal activity or physical exercise.
In addition to monitoring of physiological signals, we plan to use
the proposed environment for development of a high performance
user interface. New user inputs, including correlates of the user's
physiological and emotional states could significantly improve
human-computer interface and interaction.

Many algorithms for ECG analysis have already been tested with
very good results. Moreover, our monitoring system is general
enough to enable a wide range of bio signals monitoring and
analysis, e.g. ECG, EEG, EMG a.s.o.
ECG tele-monitoring of a patient in real time, according to our
project, has as main feature the analysis and transmission of the
patients’ bio-signals through the Internet, so that experts in
cardiology could make the right diagnostic. So, by using the existing
web-based and embedded technologies, the quality of medical
decision in tele-healthcare and emergency medical services systems
can be significantly improved.

Multimedia-Based Healthcare.
THE rapid advances in electronic devices, digital imaging,
information technology, computer systems, and networks in recent
years have stimulated the explosive growth of multimedia
computing with diverse applications to different areas including
medical service and healthcare. Equipped with various multimedia
tools, techniques, and services, computerized healthcare is
emerging as an ever-increasing important multidisciplinary area
which offers tremendous opportunities and excellent facilities to
doctors, healthcare professionals, and other eligible users to
enhance performance by fully utilizing the rich health-related
multimedia data for effective decision making.

The evolution of this new combination of computer based and
health-oriented trends will be beneﬁcial not only for computer-
aided medical diagnosis and therapy, but also for modern
telemedicine and promising e-health applications. Although the
current achievements are exciting and the results can be powerful,
it remains a challenging task to manage the diversity of health-
related multimedia data on an open heterogeneous landscape
(multi-modality, big volume, mobility, time series) efﬁciently,
accurately, reliably, and cost-effectively.
On the technology front, the use of computing power to handle
computation intensive analysis of multi-scale, multi-modal,
heterogeneous, and time-variant health related data has led to a
new path which encompasses innovations in disease diagnosis,
prognosis, customized drug development, and medical monitoring.

However, a significant barrier to high standard healthcare services
is caused by the lack of powerful methods to handle three
fundamental issues involved:
1) data management of time series of health-related record swhich
were collected at different time, stored in different formats, and
represented by different models;
2) feature selection and fusion of vast and vital medical multimedia
information for comprehensive data analysis; and 3) classification
and decision support scheme for convenient, reliable, efficient, and
cost effective health assessment. It is highly desired to develop
new and effective multimedia computing methods for health data
infrastructure by integrating multiple, distributed, heterogeneous
data sources for high performance with consistency, objectiveness
scalability, and cost-effectiveness.

This Special Issue on the topic of multimedia-based healthcare
presents the latest findings and the state-of-art methodologies for
the cross-area research on computerized healthcare,
whichisbeyondthecoverageoftheconventionalmedicalimaging.Wesel
ectedsevenpaperswhichareconcernedwithnotonly the fundamental
issues on data analytics in the rich context of health related
multimedia data, but also the crucial components such as health
data representation, management, analysis, classification, and
machine learning technologies.
We hope that the introduction of these seven papers will provide
the insight into the current advances, challenges, and trends. These
papers also offer the state-of-art research methodologies,
technologies and services with exciting findings and applications.

The first three articles are on disease detection and classification
by machine learning for different applications. The first article
entitled “Classification-based record linkage with pseudonym zed
data for epidemiological cancer registries,” by Y. Siegert, X. Jiang, V.
Krieg, and S. Bartholomew's, presents a new approach to monitor,
evaluate, and predict the development of cancer.
The objective of this research is to explore machine learning
methods for cancer registries. The main contribution of the work is
the proposal of a new method to encode the “pseudonym zed” data
for feature extraction and incorporate three classifiers (neural
network, support vector machines, and decision tree) for
classification-based record linkage to achieve robustness in cancer
registries.

The comprehensive data analysis of the experimental results
demonstrates the promising potential of the proposed approach in
real practice. The second article, “ConfidentCare: A clinical
decision support system for personalized breast cancer screening,”
by A. M. Alaa, K. H. Moon, W. Hsu, and M. van der Schar, is
concerned with the development of a computer-aided clinical
decision support system with learning capacity from the electronic
health record (HER) for personalized breast cancer screening.
Most of the existing screening systems are based on a “one-size-
fits-all” approach adopted by current clinical practice guidelines
(CPGs). Such systems can offer only a general analysis according to
the fixed pre-defined rules for all of patients without consideration
of the differences in each individual.

By contrast, ConfidentCare is developed based on a new
theoretical framework for supervised learning, where the space of
patients’ features are decomposed into clusters and the learning
process is guided by cost-effective and accurate personalized
screening policies to ensure a high confidence level of performance
bounds for every cluster of patients. More specifically,
ConfidentCare operates by computing clusters of patients with
Similar features, then learning the “best "screening procedure for
each lust erusingasu prevised learning algorithm.
The experimental results on practical cases show that
ConfidentCare outperforms the current CPGs in terms of cost and
efficiency. The third article, authored by Q. Fang and Y. Zhou, is
entitled “Kernel combined sparse representation for disease
recognition.” The work reported is on the recognition of disease by
classification of medical images.

One of the key issues is to develop effective classifiers that can be
used to detect and categorize diseases.
Although sparse representation-based classification (SRC) has
been widely used for image classification with good performance,
the method can be improved further by considering the correlation
structure of the prototype to enhance the performance.
The third article presents the combined sparse representation
(CSR) classifier to find the relationship between the test sample and
the training samples. In addition, the kernel combined sparse
representation (KCSR) classifier is adopted to map the original
feature to a nonlinear high dimensional feature such that it can
obtain the nonlinear information for classification.

In order to increase the information of the training samples and
the corresponding class mean sample,
The proposed classifiers have been evaluated by extensive
experiments on several well-known databases including the
EXACT09 database, Emphysema-CT database, mini-MIAS database,
WBC database, and HD-PECTF database. The experimental results
demonstrate that the proposed classifiers achieve better
recognition rates than many of the popular methods including
sparse representation-based classification and collaborative
representation-based classification.
The fourth selected article, “Audiovisual spatial-audio analysis by
means of sound localization and imaging: A multimedia healthcare
framework in abdominal sound mapping” by C. A.

Dimoulas, illustrates the trend and significance of multimodality
data fusion for health monitoring. The novelty of the method
presented in this paper is to apply sound-field localization and
visualization techniques for audiovisual spatial audio analysis,
content description and management.
The focus of the proposed method is topographic analysis and
mapping of gastro-intestinal motility (GIM) through multichannel
recording of abdominal sounds (AS). Unlike the existing methods
which rely on GIM physiology patterns to diagnose specific
abnormalities or diseases, a generic framework based on spatial
audio analysis is introduced, where multimodal monitoring of GIM
and other psycho-physiological parameters can be com binned.

For the study of human digestive patterns and their relation to
other factors (i.e. subjects’ medical history, nutrition, medication,
psychological state, and others). The proposed analysis and
management automation can be generally adopted in human
bioacoustics, as well as in other spatial-audio applications such as
audiovisual surveillance, acoustic scene analysis, soundscape
semantics, voice detection, and localization.
Mobile health, also referred to as Health, has emerged as an
appealing practice of medicine and public health by using mobile
devices supported by mobile telecommunication and multimedia
technologies. Equipped with technology integration within the
health sector, nowadays Health is posed with great potential on
different aspects such as offering a better health communication
channel to the public for the education and awareness of healthy

life style, improving decision making by health professionals to
enhance healthcare service by providing diverse medical and health
information to facilitate support for diagnosis, treatment, disease
monitoring, remote data collection, and epidemic outbreak
tracking. The pair of the fifth and sixth articles represent two case
studies of Health.
The fifth article, “Tensor manifold discriminant projections for
acceleration-based human activity recognition” by Y. Guo, D. Tao, J.
Cheng, A. Dougherty, Y. Li, K. Yue, and B. Zhang, presents the
studies on human activity recognition using wearable
accelerometers for health monitoring.
The third major contribution of the reported work is the proposal
of a new tensor-based feature selection method named “tensor
manifold discriminant projections (TMDP)” for classification.

The novelty of the propose algorithm is rotated around three
major steps during the following operation: 1) applying an
optimization criterion that can directly process the tensor spectral
analysis problem so as to reduce the computational cost, 2)
extracting local rank information by ﬁnding a tensor subspace that
preserves the rank order information of the within-class input
samples, and 3) extracting discriminant information by maximizing
the sum of distances between every sample and their interclass
sample mean.
Experiments on the naturalistic mobile devices-based human
activity (NMHA) 2.0 dataset are performed with benchmarking with
other methods to demonstrate the effectiveness and robustness of
the proposed TMDP approach.

The sixth article, authored by S. Cical’o, M.Mazzotti, S.Moretti, V.
Tralli, and M.Chiani, addresses another important application of
Health: “Multiple video delivery in m-Health emergency
applications.” The challenging issue is how to deliver multiple video
streams smoothly from an ambulance to a remote hospital over a
single bandwidth-limited wireless access channel rather than a large
band width. The author Softhis article proposed con text-aware
approach with three major objectives: 1) to maximize the QoE for
the ﬁnal user, 2) to efﬁciently manage distributed hardware
resources,and3)to optimize the usage of radio resources for video
transmissions.
To achieve the objectives, a new optimization framework is
introduced, which can transmit data over a single bandwidth-limited
wireless access channel by automatically select and adapt the best
video streams for data transmission.

The major contribution of the proposed approach is as follows:1)a
camera ranking technique for the VSN deployed in the emergency
area, and 2) a novel solution for the transmission of the videos from
the emergency area, based on the joint selection, adaptation and
aggregation of the streams directly performed at the application
layer of the processing equipment inside the ambulance.
Numerical simulations considering a realistic emergency scenario
with LTE-Advanced connectivity show the feasibility of the proposed
approach. Given the sensitive nature of an individual’s medical
information, integrity, security, privacy, and conﬁdentiality are
critical issues that must be clearly and effectively addressed by
Health applications. X. Yuan, X. Wang, C. Wang, J. Weng, and K.

Ran presented a secure cloud-based framework for healthcare
monitoring systems with privacy guarantees in the seventh
selected article “Enabling secure and fast indexing for privacy-
assured healthcare monitoring via compressive sensing.” The
proposed framework is a result from an integration of cross-area
technical advancement in different domains such as compressive
sensing with uniﬁed data acquisition and compression, practical
encrypted search techniques, high-performance content based
indexing, and novel on current program Ming algorithms.
The design of system architecture and indexing structure enhance
secure and very fast indexing of continuously generated
compressed data samples for health monitoring, and enable high-
performance encrypted search over compressed samples.

Experimental evaluation on Amazon Cloud demonstrates the
promising potential of the proposed approach.
Today, computerized healthcare is furnished with various
applications and encouraging achievements. The rapid advance in
multimedia computing offers an appealing synergy to manage
medical big data with automated, objective, and scalable
measurement reliably, efﬁciently, and cost-effectively.
In this special issue, we aim to strive and foster the state-of-art
research on a general platform of multimedia computing for the
cross-area healthcare applications.
We hope that the selected seven articles form the basis for further
development of multimedia-based healthcare applications.

The guest editor team would like to take this opportunity to thank
all of the people who helped and supported us indifferent ways
during the preparation of this Special Issue:
The recommendation and support from the TCMC selection
committee chaired by Prof. S.-C. Chen for our initial proposal on this
feature topic of “multimedia-based healthcare,” the hard work and
contribution from all of the authors of submitted manuscripts,
thetimeandeffortmadebyallofthereviewers,andtheconsideratearran
gementandcoordinationbytheTMMeditorialteam.

The proposed network architecture is designed as four layers:
perception layer, network layer, cloud computing layer, and
application layer. In the network, the integration of TCP/IP and
Zigbee in the coordinator devices is utilized. Consequently, WBAN
coordinators can compatibility inter-operate with various local
networks such as WiFi and LTE network to support high mobility of
users. Besides, we integrate Content Centric Networking (CCN)
with our proposed architecture to improve the ability of the WBAN
coordinator. Thus, it can support uninterrupted media healthcare
content delivery. In addition, adaptive streaming technique was also
utilized to reduce packet loss. Various simulations were conducted
using OPNET simulator to show the feasibility of the proposed
architecture in terms of transmitting a huge amount of media
healthcare data in real-time under traditional IP-based network.

The latest medical information, when expressed in understandable
terms, can empower patients to takecharge of their health, take
appropriate preventive measures and make more informed choices
regarding their treatment. In this paper, we report on the ongoing
development of PERSIVAL (PErsonalized Retrieval and
Summarization of Image, Video And Language), a system designed
to provide personalized access to a distributed digital library of
medical literature and consumer health information. Our goal for
PERSIVAL is to tailor search, presentation, and summarization of
online multimedia information to the end user, whether patient or
healthcare provider. PERSIVAL utilizes the online patient records at
New York Presbyterian Hospital [7] as a sophisticated, pre-existing
user model that can aid in predicting user interests.

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