USER EXPERIENCE AND DIGITALLY TRANSFORMED/CONVERTED EMOTIONS

International Journal of Managing Information Technology (IJMIT) Vol.10, No.2, May 2018
DOI : 10.5121/ijmit.2018.10201 1
USER EXPERIENCE AND DIGITALLY
TRANSFORMED/CONVERTED EMOTIONS
Mohammad Rafat Odeh1,
Dr. Badie Sartawi2
, Dr. Jad Najjar3
1
Information &Technology Center, Al-Quds Open University, Palestine
2
Faculty of Science &Technology, Department of Computer Science, Al-
Quds University, Palestine
3
Faculty of Science &Technology, Department of Computer Science, Al-Quds
University, Palestine
ABSTRACT
In human natural interaction (human-human interaction), humans use speech beside the non-verbal cues
like facial expressions movements and gesture movements to express themselves. However, in (human-
computer interactions), computer will use the non-verbal cues of human beings to determine the user
experience and usability of any software or application on the computer. We introduce a new model called
Measuring User Experience using Digitally Transformed/Converted Emotions (MUDE) which measures
two metrics of user experience(satisfaction and errors) , and compares them with SUS questionnaire results
by conducting an experiment for measuring the usability and user’s experience.
KEYWORDS
Usability, User Experience, Emotions, Facial Expressions, Gestures, MUDE Model.
1. INTRODUCTION
There are many evaluation methods and techniques used for testing user experience, in order to
produce an effective and efficient software. One of the modern techniques is using facial
expressions and gestures to expect the user interaction and perception of the developed software,
this technique depends on user face and gestures. Taking into consideration, those human facial
expressions and body gestures are very significant means of communication between people as
they provide important information and help deliver peoples communicative aim. Humans learn
to recognize facial expressions long before they learn to communicate verbally (Harty, 2011).
Faces and gestures not only provide us with the primary source of information about the people
that we are communicating with like there sex or gender, but also provide us extra communicative
functions like how they manage the conversation and how they express themselves towards what
is being said.
Faces and gestures are also excellent ways in a conversation for showing confirmation or surprise
without saying any word. Therefore, facial expressions and gestures are the most effective way
for communicating emotions of people (Harty, 2011).
2. METHODOLOGY
Our paper focuses on investigating a new approach for determining usability and user experience
indicators for any application used for any purpose. We will capture the expressed emotions
either positive or negative (digitally transformed/converted emotions) through face or hand over

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face gestures while using application software, these emotions will be used to measure two
parameters of usability and user experience satisfaction and errors.
The proposed methodology is a new approach, it maps the two usability and user experience
parameter into emotions. Each one of these emotions has its specific facial expressions and
specific hand over face gestures. In this way, we leave the traditional tools of usability
engineering and the user is the source of information.
An empirical experiment was carried out to show how the user expresses his or her emotions in
critical incidents. This study use in experiment an interactive camera manufactured by Intel that
reads emotions instantly(https://software.intel.com/en-us/articles/developing-applications-using-
intel-perceptual-computing-technology,29/7/2017).
3. WHY BODY LANGUAGE
The human behavioural cues consist of human emotional cues that contain visual cues including
nonverbal cues that the body language is one of its types. In addition, that body language
comprises of facial expressions, hand gestures and other cues.
Allan Pease in his book (the definitive book of body language) defined the body language as an
outward reflection of a person's emotional condition. (Pease and Pease, 2004) confirmed that each
gesture or movement can indicate a person emotional state or feeling at a specific time. Body
language is “a type of nonverbal communication that plays a central role in how humans
communicate and empathizes with each other. The ability to read nonverbal cues is essential for
understanding, analyzing, and predicting the actions and intentions of others” (Mahmoud and
Robinson, 2011).
Humans from different cultures, and in various situations can communicate, and interacted with a
certain level of accuracy when they observe both the face and the body (Mahmoud and Robinson,
2011).
4. EMOTIONS AND DIGITALLY TRANSFORMED/CONVERTED
EMOTIONS
Emotion is an essential ingredient for human that is complex and hard to find a consensus on its
definition. The emotion like joy, anger, disgust and plethora of other emotion add an active
motivation and richness for human experience. (Brave and Nass, 2003)
Paul and Anne Kleinginna research study (a categorized list of emotion definitions, with
suggestions for consensual definition) that contains a different collection of emotion definitions,
stated that emotion has two important aspects:
(a) emotion is a reaction to events related to the needs, goals, or concerns of human (b) emotion
includes physiological, affective, behavioral, and cognitive component (Brave and Nass, 2003).
These two general aspects are extracted from many different definitions that Kleinginnas have
mentioned in their research study.
Many emotions appear on human face and body; these emotions are translated through body
language, facial expressions and gestures. We differentiate these emotions and call them digitally
transformed/converted emotions. Emotions resulting from the interaction of human with PC,
pocket, smart phone, or any computer device is digitally transformed/converted emotions. These
digitally transformed/converted emotions carry the same aspects and characteristics of regular

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emotions, and we can measure them using the same tools in regular emotions that result from
human–human interaction.
5. INFERRING EMOTIONS
Human can express his feelings and emotions using verbal and nonverbal cues. Nonverbal cues
have their own channels to transfer feelings and emotions such as text, audio, physiology and
visual. One of the visual communications or transfer channels is body language that includes hand
gestures, body pastures, and facial expressions etc.
Facial expressions are created as result of contract of face muscles, the operation of face muscular
relaxation and contraction deform the facial expression system. Each facial deformation have its
feeling or emotion meaning. Recognizing facial expression depends on many factors like the
classification of facial motion and facial feature deformation to abstract classes based on visual
information (Fasel and Luettin, 2002). Facial expressions are generated from different factors
such as verbal and nonverbal communications, mental state and physiology activity.
Figure1. Sources of facial expression (Adopted from (Fasel and Luettin, 2002))
Besides that, face transmits messages about emotion, mood, age etc. Therefore, the face is also a
multi message system (Ekman and Friesen, 1975).
6. PSYCHOLOGICAL APPROACHES OF FACIAL EXPRESSIONS
ANALYSIS
6.1 Judgment based approaches or “message judgment approaches”
The objective of this approach is to indicate the message behind the facial expression. The facial
expression or mental status have its predefined class of emotions. Coders’ group consensus on
emotions inferred from facial expression or mental status by computing the average of the
responses of the users, this consensus possessed the absolute truth (Fasel and Luettin, 2002). A
problem in judgment-based approach is that it may be affected by context of observation behavior
of human face (Bettadapura, 2012). This approach is correspond to Facial effect (emotions)
approach which used by researchers in automatic analysis of facial expressions. The Ekman’s six

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emotions approach, Pultchik’s approach ,Russell’s approach and Monitoring hand over face
gestures are examples of Judgment based approaches (facial effect ).
2-Sign based approaches or “vehicle based approaches“
The basic idea behind vehicle-based approach is to characterize the facial muscles movements
and temporary wrinkles that face create into visual classes, each movement is determined by its
location and intensity. A complete system contains all possible forms of face (Fasel and Luettin,
2002). This approach is correspond to facial muscles action (action unit) approach which used by
researchers in automatic analysis of facial expressions .the Facial Action Coding System (FACS),
Facial Animation Parameters (FAPs) MPEG-4 standard
7. USER EXPERIENCE AND USABILITY
Some experts defined the two terms as metaphor to clear the contrast between the two terms.
They compared them to science (usability) vs. arts (user experience) and freeway (usability) vs. a
twisting mountain road (user experience). This metaphorical representation is trying to describe
usability as “something that is usable as functional, simple and requires less mental effort to use.
Thus, a freeway is usable since it has no oncoming traffic, enables you to get from point A to
point B in a fast manner and has consistent signage, hence requiring little learnability”. In
contrast, user experience is “a freeway is highly usable but it is boring when assessed. It is
something that focuses on user experience depicted as highly emotional. Thus, a twisting
mountain road is less usable but, because of its scenery, the smell of nature and the excitement of
the climb, it conveys a pleasant user experience”.(https://usabilitygeek.com/the-difference-
between-usability-and-user-experience/)
Figure 2. The relationship between usability and user experience (Adopted from (Moville, 2004))

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8. MEASURING USER EXPERIENCE USING DIGITALLY TRANSFORMED/
CONVERTED EMOTIONS (MUDE MODEL)
8.1 MUDE Model Components
We can summarized the components of MUDE as follow
1- User Experience Parameters “Usability Parameters “
Usability have its parameters Learnability, satisfaction, errors, efficiency and memorability. On
the other hand user experience have its specific parameters utility, usability, desirability and
adoptability .So , usability is a metrics of user experience and if we measure usability we can
measure user experience .In other words , measuring Learnability ,satisfaction, errors, efficiency
and memorability give us user experience.
2- The Emotions that Related to User Experience Parameters
Depending on Pultchik’s wheel we can map each user experience parameter with its expected or
related emotions based on its meaning as follow:
Table 1.MUDE Model Mapping of User experience parameters
User Experience
Parameters
Meaning of It Parameter Related Primary Emotions
Satisfaction How pleasant to use software Joy
Learnability How easy is it for users to accomplish
basic tasks the first time they encounter
the design?
Joy
Efficiency Once users have learned the design, how
quickly can they perform tasks
Joy
Memorability When users return to the design after a
period of not using it, how easily can
they reestablish proficiency? Joy
Errors Errors occurrence Anger, ,Surprise, contempt
,disgust, fear ,sadness
3 -Measuring Systems that Inferring Emotions from Facial Expressions and Hand over Face
Gestures. Integrating the three previous components together, we can have a MUDE model. The
following table explains the mapping between the metrics, expected emotions, action unit, FAPs
and Pease & Pease. We take the satisfaction as example for mapping between components.

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Table 2. MUDE Model Mapping of User experience parameters relative
emotionFACSFAPsPease & Pease
User
Experience
(usability )
Parameters
Emotions
(That
related to
UX)
Inferring Methods Logs
FACS FAPs Pease &
Pease
Satisfaction
(How pleasant
is it to use the
software)
Joy 6+12 open jaw (F3), lower t
midlip (F4), raise b midlip
(F5), stretch l corner lip
(F6), stretch r corner lip
(F7), raise l corner lip
(F12), raise r corner lip
(F13), close t l eyelid
(F19), close t r eyelid
(F20), close b l eyelid
(F21), close b r eyelid
(F22), raise l m eyebrow
(F33),raise r m eyebrow
(F34), lift l cheek (F41),
lift r cheek (F42), stretch l
corner lip o (F53),stretch r
corner lip o (F54)
PP3 Review the
recording of log
file and see if
user have been
learn the
methods of
filling forms in
addition we will
consider the
post –
questionnaire
9. SUS QUESTIONNAIRE MEETS INTEL INTERACTIVE GESTURES
CAMERA DESCRIPTION OF THE EXPERIMENT
The hypothesis of experiment is that the attitudes of users emotions captured by Intel interactive
gesture camera that use FAPs model about usability, are consistent with the attitudes of users in a
paper based on SUS questionnaire about usability. In addition, we aim to complete the analysis
process by using SPSS 19 (Orfanou, Tselios, and Katsanos,2015 ).
9.1 Participants
The number of participants is 70 participants (44 males and 26 females) with different ages
ranging from 19 to 42. The academic background of participants:56 B.A (16 Media and
Television, 7 Computer Science, 2 IT, 1 MIS, 2 Mathematics,1 Physics and 1 political science),
and 14 M.A(6 Computer Science , 1 Public Health and 7 MBA/ Entrepreneurial ).
9.2 Experiment Environment
The experiment is done in unconstraint environment.

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9.3 Procedure (Scenario)
Figure 2: Sketch Explain the Procedure of Experiment
9.4 Apparatus
Figure 3. Experiment Layout

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Figure 4. Summary of Questionnaire Data
Figure 5. Questionnaire Results (SUS Score)
Intel creative interactive gesture camera is a perceptual computing camera designed to interact
with computers in a more natural way through facial expression, gestures and voice. We used this
camera in our experiment to capture the emotions of participants. The kit of this camera has
different examples to capture emotions .(https://software.intel.com/en-us/articles/developing-
applications-using-intel-perceptual-computing-technology,28/7/2017)
Intel camera change the way user interact with computer, making it more natural, intuitive, and
immersive. Computers will be able to perceive our actions through hand gestures, finger
articulation, speech recognition, face tracking, augmented reality, and more. To support

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perceptual computing, Intel introduced the Intel® Perceptual Computing SDK, a library for
pattern detection and recognition algorithms.https://software.intel.com/en-us/articles/developing-
applications-using-intel-perceptual-computing-technology,28/7/2017)
Figure 6. Intel Interactive Gestures Camera. (Reprinted from (Doss and Raj,2013))
We implement facial expressions example to capture emotions, the captured emotions (Anger,
Contempt, Disgust, Fear, Joy, Sadness and Surprise), are saved in a text file. Then we take the
emotions of each participant and place them in Microsoft Excel file. After that, we count the
positive and negative emotions for each participant. I automatically analyzed the usability
attitudes of participants depending on the extracted. The results are summarized in the following
Figure:
Figure7. Summary of Camera Emotions Data
The above figure contains seven emotions for each participant, and total numbers of these
emotions.
After we collect the raw camera data, we should consolidate the data of camera and the data of
questionnaire to be compared . The data must be have the same type (like be Likert scale(0-4)).
This step is important to compare the data that we extract from camera (count of emotions) with
questionnaire data (Likert scale). We will use the SPSS19 to change camera data to frequencies
using 20th
percentiles (Gay, Airasian ,2003). Based on these frequencies we will create ranges that
will represent the Likert scale responses as (1 =Strongly disagree,2=Disagree,3= Natural

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,4=Agree and 5Strongly agree) these for negative 6 emotions that we extract (Anger, Contempt,
Disgust, Fear, Sadness and Surprise).But for positive emotion that we have (joy) we will do the
opposite .
We will change the responses to become (5 =Strongly disagree,4=Disagree,3= Natural ,2=Agree
and 1= Strongly agree). The final step is to calculate the usability score for camera data like
questionnaire data. In previous section we calculate the SUS score or usability score multiply 4(0-
4 Likert) by 10 (10 questions) to get score of questionnaire from 40 and to change it from 100 we
should multiply by 2.5 . On other hand , For camera to we should multiply 7(emotions)by 4(0-4
Likert) to get score from 28 and to change from 100 we should multiply by 3.57.
The next figure shows camera data after converted to Likert scale:
Figure 8. Camera Data after converted to Likert Scale
The next figure shows the usability score for camera data from 100:
Figure 9. The usability score of camera data from 100

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10. DISCUSSION
The main aim of experiment is to find a new valid tool to measure usability or user experience in
automatic way that can be more sensitive and easier than SUS paper based questionnaire. To
achieve our goal we use two valid tools; Intel interactive gesture camera and SUS (System
Usability Score) questionnaire to measure the usability and user experience of Al-Quds
University website.
We infer that, there is a total agreement between the usability results of the questionnaire and the
usability results of the camera. To show the total agreement between the results, we will explore
the quantitative and qualitative data for both camera data and questionnaire data. For quantitative
data the next table is contain the basic statistical information about the SUS questionnaire
usability score (from 100) and camera usability score (from 100) ,Participants(N)=70.
Table3. Basic statistical information for usability about SUS and camera
Statistics Mean Median Standard
Deviation
Variance Minimum Maximum Average
SUS
Questionnaire
Usability
Score
55.68 57.50 8.732 76.254 35 73 55.9
Camera
Usability
Score
53.4990 53.5500 12.07197 145.733 24.99 82.11 53.5
The above table shows that basic statistical information for both the SUS questionnaire usability
score and camera usability score are very close and the camera can be a good alternative for SUS
questionnaire.
The Cronbach alpha, which refers to reliability of measurements, is estimated as 0.498 for
questionnaire data and 0.373 for camera data. These values indicate the strong of questionnaire
and camera as instruments used in the evaluation (Harrati, Bouchrika, Tari, Ladjailia, 2016). The
SUS questionnaire is valid measure tool for more than 25 years (Brooke, 2013). On the other
hand, Intel interactive gestures camera and its application that we used is built on FAPs model
.So, the two tools that we use are concurrent valid tools.
There are many ways to express SUS usability scores like acceptability ranges ,adjective rating
and grade scale .In 2009 Bangour, Kortum and Miller have study 3.500 SUS scores over one
decade for different systems and technologies . They found a relation and correlation between
SUS scores and people evaluate systems and products. People who evaluate systems and products
use adjectives like good, excellent and poor. On other hand, they use scoring system ranging from
0 to 100 to interrupting SUS scores as percentage ,but it is not interrupt (Brooke,2013) .The using
of percentiles scores which give a more meaningful interrupting for SUS score
(Sauro,2011)(Bangour, Kortumand Miller,2008). The next figure showing the original imagine of
grade rankings of SUS scores from “Determining What Individual SUS Scores Mean: Adding an
Adjective Rating Scale,” by A. Bangor, P.T. Kortum, and J.T. Miller, 2009, Journal of Usability
Studies.

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Figure 10. Grade Rankings of SUS Scores ( Bangor & et al, 2009)
Implementing (Bangor, Kortum, and Miller) on our results of experiment (SUS and camera ) we
can get the next figures :
Figure 11. SUS Questionnaire Score results of our experiment
Figure (5-44): Camera Score results of our experiment

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Implementing the Sauro and Lewis SUS Score grading (2015) (see table (5-14)) on usability
score results of questionnaire and camera we will get the next figure:
Figure 12. The questionnaire and camera usability grading score
In figure11, we see how the scores grades are distributed for camera and questionnaire from 100.
Beside, we notice that camera is more sensitive than questionnaire in capturing usability.
The qualitative data that we collected through the sessions with users are indicate that the users
opinion is support the usability results of camera and questionnaire .As we mentioned the
usability average score of SUS questionnaire is 55.9 and for camera is 53.5. These averages
indicate that usability is below average (68) depending on (Sauro, 2011) (Bangour, Kortumand
and Miller, 2008). In interviews, the participants have present key comments about the website of
Al-Quds University. We asked the participants about the usability of website by mentioned the
negative and positive points in site .One participant stated :( There are many negative points like
moving between pages and update critical information on e-class) another participant stated :( I
think the worst thing in website finding the desired link that I want to use and unclear icons that
used in site to instruct the user). When we asked participant about the degree of ease and
difficulty to access information from the website. He said (the degree of ease to access
information is below median) .In general, the participants have impression that website have
some complicated in usability issues and should work to improve the site.
11. CONCLUSIONS
Human-computer interaction does not depend on traditional input devices, but it has exceeded the
normal limits and has become very close to human–human interaction. In human–human
interaction, the facial expressions and human gestures are the basic part of interaction beside the
speech. Facial expressions and gestures have also become the basic part of human-computer
interaction. Keyboard, mouse and other input devices have become old interaction tools, the new
interactions tools are facial expressions and gestures like raising right eyebrow, raising left
eyebrow, smiling etc. These new interaction tools are also indicators to evaluate usability or user
experience of any computer application or program. This hidden relation between emotions and
body language that include (facial expression and gestures ) gives the researchers the opportunity
to investigate the affect state of human who uses computer device without explicitly
communicating with it. Personally think that reading of nonverbal cues of user makes the
evaluation operation of any application more efficient and more accurate while it decreases

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annoyance to the user. The emotions or digitally transformed/converted emotions that result from
the human–computer interactions have the same characteristics and aspects of emotions that
results from human–human interactions, and we can use the same category approaches of
emotions to implement them on digitally transformed/converted emotions. Therefore, methods for
monitoring these digitally transformed/converted emotions can be developed and using these
digitally transformed/converted emotions in measuring the usability of applications. Digitally
transformed/converted emotions can be a good and a clear indicator to usability of any
application.
We can infer digitally transformed/converted emotion by traditional ways from psychological
behavior through blood pressure, eyelid, etc. but these traditional methods have problems make it
not identical for use. The alternative methods that depend on reading the emotions by body
language (facial expressions and gestures )like Facial action coding system FACS that can be
used in psychology and in computer science and the closet one to this model is MPEG or Facial
Animation Parameter used only in computer science. Moreover, we can use hand over face
gestures using the analysis method used by Mahmoud and Robinson.
This research evolve new model (MUDE) depending on the methods (FACS, FAP and Pease &
Pease) that inferring digitally transformed /converted emotions that results fronm human –
computer interaction . MUDE Model can be used to measure the User Experience of user using
the expected emotions that user expressed before, during and after interaction with application or
software. This new model makes the way of measuring so natural and not bothering users during
the evaluation and measuring process. In addition, it provides us with the needed information
without asking users. Furthermore, it is more accurate and truthful way, by which the researcher
can determine the usability of application and user experience of user.
The method can be used to measure the impression of any human not only in HCI domain, but
also in diverse domains like coffee shops, to take impression of customers about new drinks. We
can also use the model in airports to take feedback from passengers about services that have been
served to them. I think that this model will have a wide range of uses in different domains.
Beside the model this thesis introduce a new experiment that implement the MUDE model and
find a new valid tool to measure usability or user experience in automatic way that can be more
sensitive and easier than SUS paper based questionnaire. To achieve our goal we use two valid
tools; Intel interactive gesture camera and SUS (System Usability Score) questionnaire to
measure the usability and user experience of Al-Quds University website. The qualitative data in
experiment that we collected through the sessions with users are indicate that the users opinion is
support the usability results of camera and questionnaire .As we mentioned the usability average
score of SUS questionnaire is 55.9 and for camera is 53.5. These averages indicate that usability
is below average (68) depending on (Sauro, 2011) (Bangour, Kortumand and Miller, 2008).So,
the results give us indicator that camera can be valid tool to user experience and usability and
there is a new way to develop new accurate and reliable methods.
This field of new interactions have its principles and rules that should be studied to successfully
extract the accurate indications and meaning of the facial expressions and human gestures, in
order to develop applications and programs that are capable of understanding these cues.
To fully understand this subject, we must have a sufficient knowledge of models like Ekman and
FAPs. We need to develop new models to enhance the overall operation cycle; also, we have
experimental proofs that these new interactions tools are real sources of usability and user
experience data.

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12. FUTURE WORK AND LIMITATIONS
We concluded that the human–computer interactions tools have advanced to become very close to
human–human interactions. We should work on new models to contribute in the development of
human–computer interactions. On the other hand, we should work hard to develop new
applications and programmers that are compatible with these interactions. We need to concentrate
on applications utilizing from emotions extracted from facial expressions and gestures to evaluate
usability and user experience without bothering the users. However, there are many limitations
and constraints like hardware slow improvement in the domain of monitoring facial expressions
and gestures. A limited number of companies manufacture the monitoring hardware tools.
Another limitation is that sometimes the facial expressions of users do not indicate the real
psychological state of human, in some situations the facial expressions shows sadness expression,
while in reality the human may not be in this situation. The third limitation is that facial
movements is only one of other nonverbal cues, but we need to consider the all-nonverbal cues
that occur to understand the real psychological state. The next limitation is that people faces are
different, some have expressing faces, others do not have clear expressing faces that are difficult
to read, and hence hardware tools like camera will not be completely accurate. The finial limit is
that reading facial expressions and gestures is not acceptable for people in some societies, and
may be considered a controversial topic in some cultures. On the other hand, there are many open
roads to future work there main way to work on hardware of Intel interactive gestures camera to
develop it to be more accurate, specific and sensitive.
There is main way to develop its applications like developing new application that give us the
usability and user experience of any application directly comparing with SUS questionnaire .The
third main way is working on MUDE model, which needs developing, and more become widely
to cover more areas like evaluate the satisfaction of customers about any service in cafe or
restaurant.
13. PROBLEMS
1- The research topic is very difficult and have limited number in references.
2- The camera that I used (Intel interactive gesture camera) take long period to be imported
(about 6 months).
3- Many people refused to participate in the experiment (especially girls).
4- No budget for scientific research in Al-Quds University.
5- This research needs full time researcher to accomplish the research.
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