Comprehensive assessment for motor
and visually impaired people using a
hierarchical model
Awais Ahmad Khan, Ghassan Ali Kbar &
Naveed Ahmad
Universal Access in the Information
Society
International Journal
ISSN 1615-5289
Volume 17
Number 1
Univ Access Inf Soc (2018) 17:147-160
DOI 10.1007/s10209-017-0523-2
1 23
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Univ Access Inf Soc (2018) 17:147–160
https://doi.org/10.1007/s10209-017-0523-2
LONG PAPER
Comprehensive assessment for motor and visually impaired
people using a hierarchical model
Awais Ahmad Khan1 • Ghassan Ali Kbar2 • Naveed Ahmad3,4
Published online: 6 February 2017
Springer-Verlag Berlin Heidelberg 2017
Abstract The evolution of various modern technologies
has inspired researchers to assess the effectiveness of these
technologies for people with diversified disabilities. In this
article, a comprehensive and effective assessment method
for motor and visually impaired people has been accomplished using analytical hierarchy process (AHP). In the
first phase, the proposed evaluation is based on the comparative judgment of the clinical experts using the AHP
rating scale. In the second phase, the evaluation is
authenticated by incorporating user judgment scores based
on multi-weighted scoring model (MWSM). The MWSM
output values are then converted to the AHP scale. The
AHP algorithm is applied on the basis of average scores
obtained from both evaluations. The technique successfully
explores the essential, supporting and optional technologies
for various people with disabilities (PwD) as well as
identifying the criteria weight used for assessing research
articles and existing solutions. Moreover, the assessment
criteria are used by MWSM to assess research papers
Electronic supplementary material The online version of this
article (doi:10.1007/s10209-017-0523-2) contains supplementary
material, which is available to authorized users.
& Awais Ahmad Khan
awais78@gmail.com
1
Mechanical Engineering Department, University of
Engineering and Technology, Lahore, Pakistan
2
Riyadh Techno Valley, King Saud University, Riyadh, Saudi
Arabia
3
Industrial and Manufacturing Engineering Department,
University of Engineering and Technology, Lahore, Pakistan
4
Princess Fatima Alnijiris’s Research Chair for Advanced
Manufacturing Technology (FARCAMT), King Saud
University, Riyadh, Saudi Arabia
related to motor and visual impairment conditions. The
assessment techniques have successfully identified the
relevant criteria that can be used to assess technological
research papers. It also reports the suitability of the various
technological solutions and explores the limitations of the
designed technological solutions for PwDs.
Keywords Motor and visual impairment AHP MWSM People with disabilities
1 Introduction
Analytical hierarchy process (AHP) is a decision-making
method used for selection and grading of various alternatives by comparison. Since it can use qualitative data
(expert opinions), it found its way to several sectors such as
industrial, social, educational and financial.
Phillison [1] has developed a program which uses multicriteria optimization theory for rapid prototyping machine
selection. The program uses three factors, i.e., time, cost
and quality to be reduced to single function. Pareto-optimal
solutions are used to solve the multi-objective conflicting
problem. Pham and Gault [2] compared the rapid prototyping technologies available at that time. They have presented the strengths and weaknesses of the technologies
used. In addition, the authors have presented a primary
guide for layered manufacturing processes selection
through a flowchart.
Vaidya and Kumar [3] showed a wide spectrum of AHP
applications such as maintenance, performance evaluation,
decision making, source allocation and other fields.
Hanumaiah et al. [4, 5] have developed an excellent quality
function deployment–analytical hierarchy process (QFD–
AHP) model for rapid hard tooling selection. The model
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consists of two steps: criteria prioritization using the AHP
method, and identifying the critical process parameters
using QFD. The output of AHP is used as input in the QFD.
This research has been extended by using fuzzy AHP for
manufacturability evaluation.
Lee and Kwak [6] presented an application of AHP for
health care. The authors presented a case study to plan the
information resource in a healthcare system. The main
objective was to design and evaluate a model for effective
planning of the healthcare system. Singpurwalla et al. [7]
proposed the use of AHP as a tool to facilitate decision
making of two specific healthcare populations. The authors
used AHP to improve physician–patient communication by
assisting shared health decision and helped the patients to
evaluate and understand their healthcare options rather than
relying completely on the doctors decision.
Forgionne and Kohli [8] used AHP to evaluate the
quality of journals, with a methodology for consolidating
multiple criteria into an integrated measure of journal
quality, with discussion on data collection process.
Tadisinaet al. [9] discussed the application of the analytical
hierarchy process (AHP) to the selection of a doctoral
program. The developed hierarchy discussed four criteria
which can be examined with four different perspectives to
evaluate different doctoral programs.
Byun [10] proposed a decision-making method for
selecting the best passenger car models through combining
AHP and a spreadsheet model. The consistency ratio is
used as the decision-makers weights. Kahraman et al. [11]
proposed a fuzzy AHP approach for the evaluation of
catering firms and the selection of the best location for a
facility. Chang and Lo [12] applied an integrated analytical
hierarchy process–genetic algorithm (AHP–GA) approach
to deal with the job shop scheduling problem. The
scheduling sequence is established by an AHP model using
qualitative data.
The AHP method can be applied for assessing assistive
technology (AT) solutions by identifying and ranking the
criteria used in the evaluation. The development of AT is
not about accommodating the person to the technology,
rather it aims to meet and satisfy the needs of the user.
Similar to the universal design and development process,
the assessment of the AT outcome is also very crucial to
eventually achieve customer satisfaction. Although the
assessment of the AT outcomes and their economic significance has been an important issue, still comparatively
little attention has been given to it [13]. Note that the
analysis of the AT system in terms of its effectiveness is
crucial to satisfy the end users (PwDs) as well as encourage
the investors for their participation in this noble work.
There has been a consensus among the stakeholders that
the AT field does need a reliable and valid measure to
determine the impact of AT systems [14]. The assessment
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of the AT outcome can be defined as an investigation
process to determine the effect of the AT in the lives of the
users and their environments [15].
Although a lot of qualitative methods are described in
the literature, there is shortage of translating qualitative
data into well-structured quantitative form. The EuroCol
[16] and MWSM [17] techniques use both qualitative and
quantitative assessment measurement which helps in
determining more accurately the performance or outcomes
using numerical results. However, EuroCol unlike MWSM
cannot be used for assessing solutions that support
modality. The MWSM technique involves users when
determining the weight of the performance criteria and still
requires usability test to validate the final solution in order
to get user feedback about the performance of the solutions
under assessment. The assessment criteria used in the
MWSM technique [17] have been defined through expert
opinion and cross-checked with users, however, without
considering the autocorrelation among the different criteria. For this reason, multiple models are considered in this
article for comprehensive assessment of various technologies among PwDs and by using well-defined criteria that
consider simultaneously the opinions of experts and users
as well as considering the correlation effect of these
criteria.
2 Assessment models
The assessment models considered three kinds of impairment groups, namely partial motor impairment, full motor
impairment and partial visual motor impairments. The
qualitative assessment approach is used to identify the
applicability of the different proposed technological solutions in addressing the needs of PwDs. Thirteen technologies have been shortlisted by reviewing the relevant
literature [17] and also presented in Table 1. It can be
observed in Table 1 that a person with disability has to
send signals to the computer system using technologies,
namely T1, T2, T3, T4, T5, and receive signal/messages
from the computer systems using technologies such as T6,
T7, T8, T9, T10 and T11. The other technologies (T12,
T13) are used to support the computer processing and
improving the signal quality.
Two types of assessment models are used in this study.
The AHP scale is used to gather qualitative data from
experts, and MWSM is used to assess users’ rating. The
reason of using AHP scale is that the experts are assumed
to be well aware of usability of these technologies and they
can rate them easily according to the requirements of PwD.
It might be difficult for the users to rate on this scale
because of their lack of knowledge about all technologies.
Therefore, it would be more appropriate for the users to
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Table 1 Relevant technologies
Technologies
Explanation
Relevance to PwD
T1
Speech to text
Helps others to communicate with PwD and helps in controlling interfaces
T2
Keyboard or touch screen or remote
Helps to enter messages and control interfaces
T3
Gesture control using sensor or camera
Helps to observe the environment
T4
Tracking location using sensors or WIFI or RFID
Helps in determining location of PwD for decision making
T5
Sensors for behavior
Helps in tracking PwD movement
T6
Text or digital mapping to speech
Helps PwD to communicate with others people
T7
Magnification of font and window size
Helps in reading messages and seeing pictures
T8
Color control
Helps in seeing frames and interfaces
T9
Display system
Helps interacting with systems
T10
Auto-adjustable speaker and volume control
Helps with hearing and speaking
T11
T12
Haptic or vibration or flashing
Mobile support
Helps with sensory interfaces
Helps in carrying smart systems for interfacing with the environment
T13
Noise filtering
Improves hearing quality
Table 2 Performance criteria used for assessing technologies for PwD
Criterion
Importance rating
Additive
Weight
Ct1: Not needed (does not affect functionality)
1
1/10
10%
Ct2: Less important (slightly improve functionality)
2
2/10
20%
Ct3: Important (improve functionality)
3
3/10
30%
Ct4: Critical (depends on it, without it cannot function)
4
4/10
Total
answer in yes/no/may be format, and hence, a MWSM is
used to assess the user’s rating. Afterward, the MWSM
rating would be converted into AHP scale for compatibility. All the technology ratings were performed by the
authors and workgroup committee. In order to ensure that
the ratings are consistent with the needs of PwD, the
committee consulted with experts who have specialized in
accessibility for PwD for more than 20 years. For user
ratings, the committee conducted a survey with the target
stakeholders (PwD), and the results are described in
Appendix-A (Supplementary material: File name
‘‘SuppMI-PwDsurvey’’).
2.1 Applying MWSM for user rating
The MWSM model used to access technological criteria
can be performed using the following two basic steps.
Step 1 Identify the assessment criteria and their weight
The performance criteria (Ctx) are identified to assess the
relevant technologies which can satisfy the different
impairment conditions. As shown in Table 2, the Ctx is
rated according to the importance level of the technologies
and is a function of their application. Note that the total
40%
100%
additive weight for all the criteria (Ctx) should be
equal to 1.
Step 2 Assessment of technologies using MWSM
The mechanism of MWSM is applied to determine the
scores based on the technology performance criteria (Ct1–
Ct4) for technologies applicable to partial motor impairment
P.MI, full motor impairment F.MI and partial visual motor
impairment P.VMI. Note that the following assumptions are
considered for the evaluation of the technology score: critical: yes = 10, may be = 5, no = 0; important: yes = 10,
may be = 5, no = 0; less important: yes = 10, may be = 5,
no = 0; not needed: yes = 10, may be = 5, no = 0). To
calculate the average weight of a technology, each technology was given a rating of 0, 5 or 10 points for each of the
different criterions Ct1–Ct4. Since the severity of a disability is different from one person to another, PwDs rated
technologies differently. For example, T1 in Table 3 was
rated 10, 5, 5 and 0 for criterion Ct4, Ct3, Ct2 and Ct1,
respectively. This means that some PwD thought T1 is
critical for them, so they rated it with 10 on Ct4, while
others rated it with 5 on Ct3 because they thought it might
be important to them. However, no PwD thought that T1 is
not needed, so it was rated 0 on Ct1. As shown in Table 3,
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Table 3 Assessing input technologies for P.MI using performance criteria (Ctx) defined in step 1
Criterion
Weight (W)
T1 (speech to text)
T2 (keyboard or touch
screen or remote)
T3 (gesture control
using sensor or camera)
T4 (tracking location
using sensors, WiFi,
RFID)
T5 (sensors for
behavior)
Rating (R)
R
WXR
R
WXR
R
WXR
R
WXR
WXR
Partial MI (computer system input technology)
Ct1
10%
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Ct2
20%
5.0
1.0
5.0
1.0
5.0
1.0
5.0
1.0
5.0
1.0
Ct3
30%
5.0
1.50
5.0
1.5
5.0
1.5
10.0
3.0
5.0
1.5
Ct4
40%
10.0
4.0
5.0
2.0
10.0
4.0
5.0
2.0
5.0
2.0
Total
6.5
4.5
6.5
the rating of different technologies is calculated using the
4
P
Rji Wji . For example, Rji for T1 ¼
MWSM equation
i¼1
½0; 5; 5; 10
and Wji ¼ ½0:1; 0:2; 0:3; 0:4. Then
4
P
R1i i¼1
W1i ¼ 6:5: The rating of the 13 technologies that were
assessed by users is presented in Appendix-B (Supplementary material: File name ‘‘SuppMI-PwDWeight’’).
2.2 Analytical hierarchy process
2.
3.
4.
5.
6.
AHP is an efficient and most widely used multi-criteria
decision-making tool that can use qualitative data as expert
or user opinion and synthesize it in the form of numeric
weights. This is an eigenvalue approach to the pairwise
comparisons. The process is elaborated in the steps
described in the following.
Step 1 Identify the scale for assessing the technologies
AHP provides a methodology [3] to calibrate the numeric
scale for the measurement of quantitative as well as qualitative performances. The scale ranges from 1 to 9, for
‘least valued than,’ to 1 for ‘equal,’ and to 9 for ‘absolutely
more important than’ covering the entire spectrum of the
comparison. The AHP scale is presented in Table 4.
The analytical hierarchy model consists of three main
activities, namely hierarchy construction, priority analysis
and consistency verification. The overall procedure of the
AHP is elaborated in the following steps:
Table 4 AHP scale
Scale
1
Equal importance
3
Less importance
5
7
Moderate importance
Strong importance
9
Extreme importance
2,4,6,8
Intermediate values
123
1.
6.0
4.5
Breakdown the complex multiple criteria decision
problems into their component parts;
Arrange every possible attributes into multiple hierarchical levels;
Make pairwise comparisons in the same level with
respect to the corresponding criteria in the previous
level;
Establish priorities among the elements in the hierarchy;
Synthesize judgments (to obtain the set of overall or
weights for achieving your goal);
Evaluate and check the consistency of judgments.
Since the comparisons are carried out through personal
or subjective judgments, some degree of inconsistency may
be occurred. To guarantee the judgments are consistent, the
final operation, i.e. consistency verification, which is
regarded as one of the biggest advantages of AHP, is
incorporated in order to measure the degree of consistency
among the pairwise comparisons by computing the consistency ratio. If the consistency ratio exceeds the limit, the
decision makers should review and revise the pairwise
comparisons. Once all pairwise comparisons are carried out
at every level and are proved to be consistent, the judgments can then be synthesized to find out the priority
ranking of each criterion and its attributes [18]. The complete AHP procedure is shown in Fig. 1.
Step 2 Assessment of technologies using AHP
In the AHP model, each disability is considered to be
equally important. Therefore, in the first hierarchy, the
relative importance (weight) for each impairment group is
{0.33, 0.33, 0.33}. The complete assessment model for the
current problem is shown in Fig. 2.
In the first step, a pairwise matrix that is constructed on the
basis of relative importance of each technology depends on
average scores of expert and user ratings. The experts and user
score data for each technology are obtained from the survey
results. Each technology priority/weight is evaluated in a
pairwise comparison fashion. The advantage of using the AHP
model is that not only those technologies are being assessed
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Step 1 For a pairwise matrix
2
3
C11 C12 C13
4 C21 C22 C23 5
C31 C32 C33
Develop a multi-attribute hierarchical model
Step 2 Sum of the column values
n
X
Cij
Cij ¼
Construct the pairwise comparison matrix for the
attributes
i¼1
Step 3 Divide each element in the matrix by its column
total
2
3
X11 X12 X13
Cij
Xij ¼ Pn
¼ 4 X21 X22 X23 5
i¼1 Cij
X31 X32 X33
Establish priorities among
attributes
Step 4 Divide the sum of normalized column of matrix by
the number of criteria used (n) to generate weighted matrix
2
3
Pn
W11
j¼1 Xij
¼ 4 W12 5
Wij ¼
n
W13
Synthesize Judgment
Check consistency
If consistency within the
limit
No
Yes
Rank Judgment
Fig. 1 Analytical hierarchy process
separately for each impairment group but also their relative
importance is established. The degree of consistency among the
pairwise comparison is evaluated by computing the consistency
ratio. The AHP algorithm is described in the following:
Pairwise matrices are presented here along with their
calculated priority vectors/weights for each technology
separately for PMI, FMI and PVMI. The ranking of each
technology was first obtained on the basis of their importance by experts using the AHP scale (Table 4). Then,
these technologies were ranked again by user survey scores
using MWSM (Table 3). Afterward, the average values of
these two scores were taken and listed on the AHP scale.
This scale is used for obtaining the relative importance of
these technologies. For example, technology T5 in PMI
group is very important according to expert opinion with a
score 7 (Table 4) though the user score for this technology
is smaller. Therefore, by taking the average of these two
scores, T5 will come out as moderate important technology
with a score of 5 on AHP scale (Table 4). Now, the pairwise comparison matrix is constructed by checking the
relative importance of each technology with respect to each
other. For example, technology T1 has a score of 8 and
technology T5 has 6 in PMI; therefore, technology T5 has
ranked as 3 when comparing with T1 and technology T1
has a score of 1/3 (0.33) compared to T5 (Table 5). When
comparing the same technologies such as T1 with T1 or
different technologies with the same scores like T1 with
T6, they are ranked as 1 (equal importance on AHP scale).
The pairwise comparison matrix is obtained in this way for
all the technologies with each impairment group as listed in
Tables 5, 6 and 7.
Moreover, weight with respect to each impairment
group will be calculated by applying the above algorithm
from step 1 to step 4. It can be observed from Tables 5, 6
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Assessment Criteria
PMI
T1
T2
FMI
T3
T4
T5
T6
T7
PVMI
T9
T8
T10
T11
T12
T13
Fig. 2 Developed AHP model
and 7 that few technologies, such asT1, T3 and T6 for PMI,
T1, T6 for FMI and PVMI, have equal weight because of
their equal importance at the AHP scale. The output data
are presented in Tables 5, 6 and 7.
The appropriate consistency index is called random
consistency index (R1). Satty [19] proposed the index by
randomly generated reciprocal matrix using scale 1/9, 1/8,
…, 1, …, 8, 9 (similar to the idea of Bootstrap) and get the
random consistency index to see whether it is about 10% or
less. If the consistency levels are greater than or equal to
10%, then you have to do your survey again. The following
steps are applied to ascertain the judgment consistency for
each impairment group.
Step 5 Consistency vector is generated by multiplying
pairwise matrix by the weight vector:
2
32
3 2
3
C11 C12 C13
W11
Cv11
4 C21 C22 C23 54 W12 5 ¼ 4 Cv12 5
C31 C32 C33
W13
Cv13
Step 6 k is calculated by averaging the value of consistency vector:
k¼
n
X
Cvij :
i¼1
123
Step 7 Consistency index (measure of diversion) is calculated by
c1 ¼
kn
:
n1
Step 8 Cr \ 0.1 is set as threshold value to check the
inconsistencies in the judgment by Satty [19]
Cr ¼
C1
:
R1
The consistency of the evaluation is validated for each
impairment group separately by applying step 5–8 of the
algorithm. The data are presented in Table 8.
The importance of each technology on the basis of their
weight is also presented in Fig. 3. On the basis of their
weight, the technologies are classified as essential
(Wt C 0.08), supporting (0.04 B Wt \ 0.08) and optional
(Wt \ 0.04), as shown in Table 9 (percentage of essential
technologies). Table 9 presents the total score of essential
technologies needed for each disability in order to satisfy
the requirements of users with disabilities and determine
how well particular technologies justify the essential conditions of that group. The percentage of supporting technologies presents a total score of the supporting
technologies needed for these impairment groups. These
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Table 5 Weight/priority vector for PMI
P.MI
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T13
Weights
T1
1.00
2.00
1.00
1.00
3.00
1.00
4.00
5.00
1.00
2.00
2.00
1.00
4.00
0.12
T2
0.50
1.00
0.50
0.50
2.00
0.50
3.00
4.00
0.50
1.00
1.00
0.50
3.00
0.07
T3
1.00
2.00
1.00
1.00
3.00
1.00
4.00
5.00
1.00
2.00
2.00
1.00
4.00
0.12
T4
1.00
2.00
1.00
1.00
3.00
1.00
4.00
5.00
1.00
2.00
2.00
1.00
4.00
0.12
T5
0.33
0.50
0.33
0.33
1.00
0.33
2.00
3.00
0.33
0.50
0.50
0.33
2.00
0.04
T6
1.00
2.00
1.00
1.00
3.00
1.00
4.00
5.00
1.00
2.00
2.00
1.00
4.00
0.12
T7
0.25
0.33
0.25
0.25
0.50
0.25
1.00
2.00
0.25
0.33
0.33
0.25
1.00
0.03
T8
0.20
0.25
0.20
0.20
0.33
0.20
0.50
1.00
0.20
0.25
0.25
0.20
0.50
0.02
T9
1.00
2.00
1.00
1.00
3.00
1.00
4.00
5.00
1.00
2.00
2.00
1.00
4.00
0.12
T10
0.50
1.00
0.50
0.50
2.00
0.50
3.00
4.00
0.50
1.00
1.00
0.50
3.00
0.07
T11
T12
0.50
1.00
1.00
2.00
0.50
1.00
0.50
1.00
2.00
3.00
0.50
1.00
3.00
4.00
4.00
5.00
0.50
1.00
1.00
2.00
1.00
2.00
0.50
1.00
3.00
4.00
0.07
0.12
T13
0.25
0.33
0.25
0.25
0.50
0.25
1.00
2.00
0.25
0.33
0.33
0.25
1.00
0.03
Table 6 Weight/priority vector for FMI
F.MI
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T13
Weights
T1
1.00
7.00
2.00
2.00
4.00
1.00
6.00
6.00
2.00
2.00
3.00
5.00
5.00
0.17
T2
0.14
1.00
0.17
0.17
0.25
0.14
0.50
0.50
0.17
0.17
0.20
0.33
0.33
0.02
T3
0.50
6.00
1.00
1.00
3.00
0.50
5.00
5.00
1.00
1.00
2.00
4.00
4.00
0.11
T4
0.50
6.00
1.00
1.00
3.00
0.50
5.00
5.00
1.00
1.00
2.00
4.00
4.00
0.11
T5
0.25
4.00
0.33
0.33
1.00
0.25
3.00
3.00
0.33
0.33
0.50
2.00
2.00
0.05
T6
T7
1.00
0.17
7.00
2.00
2.00
0.20
2.00
0.20
4.00
0.33
1.00
0.17
6.00
1.00
6.00
1.00
2.00
0.20
2.00
0.20
3.00
0.25
5.00
0.50
5.00
0.50
0.17
0.02
T8
0.17
2.00
0.20
0.20
0.33
0.17
1.00
1.00
0.20
0.20
0.25
0.50
0.50
0.02
T9
0.50
6.00
1.00
1.00
3.00
0.50
5.00
5.00
1.00
1.00
2.00
4.00
4.00
0.11
T10
0.50
6.00
1.00
1.00
3.00
0.50
5.00
5.00
1.00
1.00
2.00
4.00
4.00
0.11
T11
0.33
5.00
0.50
0.50
2.00
0.33
4.00
4.00
0.50
0.50
1.00
3.00
3.00
0.07
T12
0.20
3.00
0.25
0.25
0.50
0.20
2.00
2.00
0.25
0.25
0.33
1.00
1.00
0.03
T13
0.20
3.00
0.25
0.25
0.50
0.20
2.00
2.00
0.25
0.25
0.33
1.00
1.00
0.03
Table 7 Weight/priority vector for PVMI
P.VMI
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T13
Weights
T1
1.00
4.00
2.00
4.00
4.00
1.00
2.00
2.00
2.00
2.00
3.00
2.00
4.00
0.15
T2
0.25
1.00
0.33
1.00
1.00
0.25
0.33
0.33
0.33
0.33
0.50
0.33
1.00
0.03
T3
0.50
3.00
1.00
3.00
3.00
0.50
1.00
1.00
1.00
1.00
2.00
1.00
3.00
0.09
T4
0.25
1.00
0.33
1.00
1.00
0.25
0.33
0.33
0.33
0.33
0.50
0.33
1.00
0.03
T5
0.25
1.00
0.33
1.00
1.00
0.25
0.33
0.33
0.33
0.33
0.50
0.33
1.00
0.03
T6
1.00
4.00
2.00
4.00
4.00
1.00
2.00
2.00
2.00
2.00
3.00
2.00
4.00
0.15
T7
0.50
3.00
1.00
3.00
3.00
0.50
1.00
1.00
1.00
1.00
2.00
1.00
3.00
0.09
T8
0.50
3.00
1.00
3.00
3.00
0.50
1.00
1.00
1.00
1.00
2.00
1.00
3.00
0.09
T9
0.50
3.00
1.00
3.00
3.00
0.50
1.00
1.00
1.00
1.00
2.00
1.00
3.00
0.09
T10
0.50
3.00
1.00
3.00
3.00
0.50
1.00
1.00
1.00
1.00
2.00
1.00
3.00
0.09
T11
0.33
2.00
0.50
2.00
2.00
0.33
0.50
0.50
0.50
0.50
1.00
0.50
2.00
0.05
T12
0.50
3.00
1.00
3.00
3.00
0.50
1.00
1.00
1.00
1.00
2.00
1.00
3.00
0.09
T13
0.25
1.00
0.33
1.00
1.00
0.25
0.33
0.33
0.33
0.33
0.50
0.33
1.00
0.03
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supporting technologies assist in determining how well a
particular impairment satisfies the less important conditions of that group. The percentage of optional technologies, which presents a total score of the optional
technologies, can be dropped for these impairment groups.
It can be observed in this figure that the technologies T1,
T3, T6 and T9 are essential for all MI groups, whereas
technology T4 is essential for P.MI and F.MI, T10 is for
F.MI and P.VMI and T12 is essential to P.MI and P.VMI
only.
Another advantage of using AHP is the determination of
overall ranking/weight for each technology. The overall
ranking/weight will be calculated by matrix multiplication
of weight columns in Tables 5, 6 and 7 with impairment
group weight, i.e., {0.33, 0.33, 0.33}. On the basis of these
results, the technologies can be classified as essential
(Wt C 8%), supporting (4% B Wt \ 8%) and optional (Wt.
\ 4%). The significance of having these ranking/weight is
the shortlisting of technologies with respect to diversified
disabilities. The overall weight as percentage for P.MI,
F.MI and P.VMI can be seen in Table 10.
Again, the classification of these technologies on the
basis of total percentage is listed in Table 11 and
Table 8 Consistency measure
Consistency measure
PMI values
FMI values
PVMI values
k
13.12
13.3
13.07
R1
1.56
1.56
1.56
C1
0.01
0.025
0.006
C1/R1 \ 0.1
0.006
0.016
0.004
Fig. 3 Percentage weight for
P.MI, F.MI and P.VMI
graphically presented in Fig. 4. It can be observed that
technologies T1, T3, T4, T6, T9 and T10 are essential for
all MI groups, whereas technologies T5, T7, T8, T11 and T
12 are classified as supporting on the basis of threshold
values defined above. In the end, technologies T2 and T13
are categorized as optional and can be dropped.
2.3 Comparing AHP and MWSM
It is clear from Table 12 that the variance between criteria
weight of AHP and MWSM of PMI is below 3%. However,
as shown in Table 13, for FMI the variance is higher for C1,
C5 and C6, which is between 6 and 7%. Similarly for P.MVI
and as shown in Table 14, the variance of C1 and C6 is high
in the vicinity of 6 and 7%. This indicates that technologies
associated with C1 (text to speech), C5 (sensor and behavior) and C6 (text or digital mapping to speech) are affected
by other technologies, and that explains why the AHP
technique would have different weight for these criteria as it
considers the correlation among different criteria.
3 Research paper assessment for MI
A qualitative approach has also been proposed to determine
the contribution of each technology for addressing the
needs of the PwDs. These approaches have also been validated through their assessment of research papers
addressing assistive technologies for people with motor
impairment. To better understand the applications of the
assessment techniques, explore their implications and
identify their limitations, the review of selected research
20.0%
15.0%
PMI
10.0%
FMI
5.0%
0.0%
PVMI
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T13
Table 9 Technologies classification
Disabilities
Essential
List
P.MI
T1,T3,T4,T6, T9, T12
69.00%
T2,T5,T10, T11
F.MI
P.VMI
T1,T3,T4,T6, T9,T10
T1,T3,T6,T7, T8,T9,T10, T12
76.00%
83.00%
T5, T11
T11
Tech technologies, Per percentage
123
Tech
Total per
Supporting
List
Tech
Total per
Optional
List
Tech
Total per
24.00%
T7,T8,T13
7.00%
12.00%
5.00%
T2, T7, T8, T12, T13
T2,T4,T5, T13
12.00%
12.00%
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articles from the multiple academic disciplines that are
related to different impairment conditions and most notably
science, technology, and health is carried out [40]. The
proposed quantitative and qualitative techniques have also
been used to determine the areas where previous research
was either limited or did not cover at all the needs of
different groups of PwD.
Table 10 Overall priority/weight for technologies
Technologies
Overall weight for MI (%)
T1
14.5
T2
3.7
T3
10.3
T4
8.4
T5
4.0
T6
14.5
T7
4.5
T8
4.3
T9
10.3
T10
8.6
T11
6.2
T12
7.8
T13
3.0
Motor impairment (MI) is a physical disability that can
be related to muscle weakness and fatigue, muscle contracture and spasticity. In the context of the present work, a
total of 15 scientific papers that have tried to find solutions
for disabled people with motor impairment are assessed.
The review of selected research papers from multiple
academic disciplines related to motor impairment conditions, most notably science, technology, and health is
carried out by using the AHP assessment technique. The
essential and important criteria that are relevant to the
motor impairment group are shown in Tables 12 and 13 for
P.MI and F.MI, respectively. These criteria are applied to
the MI group, to better understand the application of the
AHP assessment technique in evaluating these papers.
Table 15 lists the average scores for the 15 papers
assessed by those researchers related to MI according to the
multi-weighted scoring method and the AHP model.
As shown in Table 15, the average of the overall percentage covering the essential criteria for P.MI and F.MI
was 62 and 57%, respectively. The number of papers
covering the essential criteria with average C60% for P.MI
is 7 out of 15 or 47%, and for F.MI is also 5 out of 15 or
33%. These results indicate that acceptable percentage of
the papers for P.MI covered the essential criteria well for
Table 11 Overall technologies classification
P.MI, F.MI &
P.VMI
Essential
List
Tech
Total percentage
(%)
Supporting
List
Tech
Total percentage
(%)
Optional
List
Tech
Total percentage
(%)
T1, T3, T4, T6,T9,
T10
67.00
T5, T7, T8, T11,
T12
26.00
T2, T13
7.00
Tech technologies
Fig. 4 Importance for MI
P.MI, F.MI and P.VMI
16.0%
14.0%
12.0%
10.0%
8.0%
6.0%
4.0%
2.0%
0.0%
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T13
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Table 12 Criteria weight comparison between AHP and MWSM for P.MI
C1
C2
C3
C4
C5
C6
MWSM
0.1
0.07
0.1
0.09
0.07
0.1
0.04
AHP
0.12
0.07
0.12
0.12
0.04
0.12
0.03
Variance (%)
2
0
2
3
-3
C7
2
C8
-1
C9
C10
C11
C12
0.04
0.1
0.1
0.1
0.1
0.02
0.12
0.07
0.07
0.12
C13
0.04
0.03
-2
2
-3
-3
2
-1
C8
C9
C10
C11
C12
C13
Table 13 Criteria weight comparison between AHP and MWSM for F.MI
C1
C2
C3
C4
C5
C6
MWSM
0.11
0.02
0.11
0.11
0.11
0.1
0.03
0.03
0.11
0.08
0.11
0.04
AHP
0.17
0.02
0.11
0.11
0.05
0.17
0.02
0.02
0.11
0.11
0.07
0.03
Variance (%)
6
0
0
0
0
3
-6
C7
7
-1
-1
-4
-1
0.04
0.03
-1
Table 14 Criteria weight comparison between AHP and MWSM for P.VMI
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
MWSM
0.09
0.06
0.09
0.06
0.06
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
AHP
0.15
0.03
0.09
0.03
0.03
0.15
0.09
0.09
0.09
0.09
0.05
0.09
0.15
Variance (%)
6
6
0
0
0
0
0
6
-3
0
-3
-3
-4
Table 15 Assessment table for P.MI and F.MI
Criteria
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
Total score for each paper
(Percentage: per; coverage: cov; Essential:
Ess)
Weight
(PMI)
Weight
(FMI)
0.12
0.07
0.12
0.12
0.04
0.12
0.03
0.02
0.12
0.07
0.07
0.12
0.03
0.17
0.02
0.11
0.11
0.05
0.17
0.02
0.02
0.11
0.11
0.07
0.03
0.03
Per of ess cov (C1 or
C3 or C4, and C6, or
C9, and C12) PMI
(%)
Per of ess cov (C1 or
C3 or C4 and C6 or
C9 or C10) FMI (%)
MI-1
[20]
0.0
0.0
9.7
0.0
3.3
9.7
8.7
0.0
10.0
1.0
0.0
0.0
0.0
66
79
MI-2
[21]
10.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
3.5
0.0
0.0
0.0
0.0
45
61
MI-3
[22]
0.0
0.0
6.5
0.0
4.5
0.0
0.0
0.0
10.0
0.0
6.0
7.5
0.0
80
52
MI-4
[23]
9.0
0.0
0.0
8.5
0.0
0.0
0.0
0.0
8.0
0.0
0.0
5.5
0.0
75
70
MI-5
[24]
0.0
0.0
8.0
0.0
8.0
0.0
0.0
0.0
9.5
0.0
0.0
0.0
0.0
58
55
MI-6
[25]
0.0
0.0
7.5
0.0
2.0
9.0
0.0
0.0
10.0
0.0
0.0
0.0
0.0
58
69
MI-7
[26]
0.0
1.5
9.0
0.0
1.0
0.0
0.0
1.5
10.0
0.0
0.0
0.0
0.0
63
60
MI-8
[27]
0.0
1.5
8.5
0.0
1.5
0.0
0.0
1.5
10.0
0.0
0.0
0.0
0.0
62
58
MI-9
[28]
0.0
0.0
6.0
0.0
1.5
0.0
5.0
0.0
10.0
0.0
0.0
0.0
0.0
53
51
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157
Table 15 continued
Criteria
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
Total score for each paper
(Percentage: per; coverage: cov; Essential:
Ess)
Weight
(PMI)
Weight
(FMI)
0.12
0.07
0.12
0.12
0.04
0.12
0.03
0.02
0.12
0.07
0.07
0.12
0.03
0.17
0.02
0.11
0.11
0.05
0.17
0.02
0.02
0.11
0.11
0.07
0.03
0.03
Per of ess cov (C1 or
C3 or C4, and C6, or
C9, and C12) PMI
(%)
Per of ess cov (C1 or
C3 or C4 and C6 or
C9 or C10) FMI (%)
MI-10
[29]
0.0
0.0
8.0
0.0
1.5
0.0
0.0
0.0
10.0
0.0
0.0
0.0
0.0
60
57
MI-11
[30]
2.0
10.0
8.0
0.0
0.0
0.0
0.0
0.0
9.5
0.0
0.0
10.0
0.0
92
55
MI-12
[31]
0.0
9.0
9.0
0.0
0.0
0.0
0.0
0.0
7.5
0.0
0.0
0.0
0.0
55
52
MI-13
[32]
0.0
0.0
9.5
0.0
0.0
0.0
0.0
0.0
2.0
0.0
0.0
0.0
0.0
38
36
MI-14
[33]
2.5
6.5
0.0
8.5
0.0
5.5
5.5
5.5
8.5
5.0
5.0
10.0
0.0
90
54
MI-15
[34]
0.0
0.0
9.0
0.0
4.0
0.0
0.0
0.0
6.0
0.0
0.0
0.0
0.0
50
47
63
57
Average percentage result: total score for each paper F.MI = 36%, and for P.MI = 26%
Table 16 Assessment results of P.MI and F.MI
Assessment parameters
C1
(%)
C3
(%)
C4
(%)
C6
(%)
C9
(%)
C10
(%)
C12
(%)
Percentage of coverage level of essential criteria relative to maximum value for P.MI by
applying equation (5) of [17]
16
66
11
16
83
2
22
Percentage of coverage level of essential criteria relative to maximum value for F.MI
value by applying equation (5) of [17]
16
66
11
16
83
4
Table 17 AHP weight—assessment table for P.VMI
Assistive technology criteria for evaluation for the visual and motor impairment (VMI) group
Percentage: Per; Essential: Ess; Coverage: Cov; Important: Imp; Criteria: Cri
Criteria
Criteria
weight
(P.VMI)
C1
0.09
C2
0.06
C3
0.09
C4
0.06
C5
0.06
C6
0.09
C7
0.09
C8
0.09
C9
0.09
C10
0.09
C11
0.09
C12
0.09
C13
0.06
Per. of all Cri.
of individual
paper (%)
Per. of Ess. Cov. (C1,
C3, and C6 to C10 and
C12) P.VMI (%)
VMI-1
[35]
6.0
7.7
0.0
0.0
0.0
5.7
7.7
7.7
9.0 7.7
0.0
7.7
0.0
64
62
VMI-2
[36]
0.0
8.3
0.0
0.0
8.3
0.0
8.3
2.7
10.0
0.0
5.3
0.0
0.0
36
22
VMI-3
[37]
9.0
8.5
0.0
0.0
0.0
9.5
0.0
0.0
2.5 9.0
0.0
5.0
1.0
48
83
VMI-4
[38]
10.0
0.0
0.0
0.0
3.0
0.0
0.0
0.0
0.0 0.0
0.0
0.0
5.0
19
39
VMI-5
[39]
7.0
0.0
7.0
0.0
0.0
0.0
0.0
0.0
10.0
0.0
0.0
0.0
29
50
39
51
Average percentage of overall 5 papers
0.0
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Table 18 AHP weight assessment results
Relevant essential criteria for partial VMI
C1
(%)
C3
(%)
C6
(%)
C7
(%)
C8
(%)
C9
(%)
C10
(%)
C12
(%)
Percentage of coverage level of essential criteria relative to maximum
value for P.MVI.
64
14
30
32
21
63
33
25
the MI group, which would satisfy the needs of this group.
However, 33% of these papers covered the essential criteria
for F.MI. In terms of overall coverage of criteria, both P.MI
and F.MI have a low score of 26 and 36%, respectively.
Hence, further research is needed to cover multiple technologies with more comprehensive solutions to provide
extra flexibility to MI users. The support of multiple
technologies allows MI users to select the relevant technology to substitute their missing sensation caused by their
motor impairment conditions. The technologies associated
with relevant criteria for MI that require further investigation are analyzed and determined in the subsequent
Sect. (3.1).
address the needs of the P.VMI group through the support
of multiple technologies associated with important criteria.
The technologies associated with relevant criteria for
P.VMI that need further investigation are analyzed and
determined in the subsequent Sect. 4.1.
3.1 Assessing the coverage or relevant criteria
in relation to the maximum value for MI
The comparison of the coverage of each criterion in relation to other criteria is shown in Table 16 that illustrates
the results after applying the essential criteria relative to
the maximum value and by applying equation (5) of [17].
For partial MI, criteria C9 (display) and C3 (gesture
control) are covered well among these 15 papers with
average score of 83 and 66%, respectively, while other
relevant criteria C1 (speech to text), C4 (tracking location),
C6 (text to speech), C10 (voice control) and C12 (mobile
support) have low score as shown in Table 16 and, therefore, are not covered well. This leads to the conclusion that
further research in relation to C1, C4, C6, C10 and C12 is
needed to improve the conditions for people with PMI.
4 Research paper assessment for P.VMI
Table 17 lists the average scores of those researchers for
the five papers related to P.VMI according to the MWSM.
As shown, the average of overall percentage covering the
essential criteria for P.VMI is 51%, while covering all
criteria is 39%. These results indicate that none of these
papers cover the essential criteria above 60% for P.VMI.
Hence, further research is needed to cover multiple technologies for system input to provide extra flexibility to
P.VMI users. Furthermore, more research needs to be
carried out to reach a more comprehensive solution that can
123
4.1 Assessing the coverage of relevant criteria
in relation to the maximum value for VMI
Table 18 illustrates the average percentage of individual
criteria results obtained by applying essential criteria relative to the maximum value and by applying equation (5)
of [17].
For P.VMI, criteria C1 (speech to text) and C9 (display)
are covered well among these five papers with values of 64
and 63%, respectively. This is followed up by C6 (text to
speech), C7 (magnification) and C10 (adjustable speaker)
with relatively acceptable scores of greater than 30%,
while criteria C3 (gesture control), C8 (color control) and
C12 (mobile support) have a lower score. This leads to the
conclusion that further research in relation to C3, C8 and
C12 should be conducted to improve the conditions for
people with P.VMI.
5 Conclusion
The assessment models applied in this article have not only
successfully quantified the necessity of various technologies for each impairment group separately but also articulated the overall requirement of these technologies among
different impairment groups. The assessment methodology
of this article is primarily based on quantitative and qualitative assessment of various technologies in order to
identify the applicability of the different proposed technological solutions in addressing the needs of the PwD.
Two types of assessment models are applied in this study.
The AHP scale is used to gather the assessment data for all
thirteen technologies from experts, whereas MWSM is
used to assess user rating. Afterward, MWSM rating would
be converted to AHP scale for compatibility. The AHP
model is implemented on the basis of relative importance
of each technology depending on average scores of expert
and user ratings. The significance of using the AHP model
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Univ Access Inf Soc (2018) 17:147–160
is not only to give priority ratings for each technology with
respect to disability but also to give overall scores to each
technology for diversified disabilities. The AHP model
assists in identifying more accurate weight of the criteria
that are associated with these technologies since it considers the rating from experts and PwDs as well as determining the auto-correlation impacts among these criteria. It
has also been observed that technologies T1, T3 (help in
communication) can be ranked as the most essential in all
kinds of physical impairment groups and technologies T3
(gesture control) and T9 (display system) can be ranked
after these technologies. The criteria weights identified
through the AHP model are then used to assess 15 scientific
papers related to the different impairment groups related to
MI and P.VMI of PwD by using the MWSM technique.
The results of MI assessment indicate that some essential criteria are covered well in the literature as listed in
Table 16, especially C3 (gesture control) and C9 (display),
while other criteria associated with text to speech technologies (C1), tracking location (C4), speech to text (C6),
speaker control (C10) and mobile support (C12) require
further research in the literature, as their scores were low.
The assessment result of P.VMI indicates that some
essential criteria were covered well in the literature as
listed in Table 18, especially C1 (text to speech) and C9
(display). In contrast, the rest of the criteria gesture control
(C3), color control (C8) and mobile support (C12) which
are relevant to P.VMI require further research, as their
scores were relatively low.
Since the main focus of this paper is to identify accurate
criteria weight that can be used for assessment and then
apply these criteria to assess existing research papers, the
proposed techniques were only applied to a limited number
of research papers for MI and VMI impairment groups for
showcase purpose. Furthermore, the lack of comprehensive
assistive technology solutions focusing on the specific
solution to universally address the needs of the impairment
group makes it difficult to identify many research papers
consisting of several impairment conditions in the present
literature review.
Acknowledgements Funding was provided by King Abdulaziz City
for Science and Technology (Grant No. 12-ELE3220–02).
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