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Transcript of Nikolaos Kaklanis - PhD presentation
University of Surrey Department of Computing
A COUPLED USER AND TASK MODELLING
METHODOLOGY FOR ACCESSIBLE PRODUCT DESIGN
PhD Thesis
Nikolaos Kaklanis
Information & Communication Systems Engineer, MSc
University of Surrey Department of Computing
• Disability is part of the human condition– Almost everyone will be temporarily or permanently impaired
at some point in life. • Aging is strongly connected with difficulties in
functioning• The lowest estimate, based on the currently defined
disablement categories, estimates their total number at around 74 Million persons, in the European Union.
• Many products & services are inaccessible for people with functional limitations
The need: Accessible & ergonomic products and services for the elderly and disabled
THE NEEDTHE NEED
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TYPICAL DEVELOPMENT CHAINTYPICAL DEVELOPMENT CHAIN
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DEVELOPMENT CHAIN USING DEVELOPMENT CHAIN USING VIRTUAL USER MODELSVIRTUAL USER MODELS
The
Context of the introduced research
University of Surrey Department of Computing
A new coupled user-task modeling methodology
is needed, which will enable– the detailed description of users with disabilities– the development of Virtual User Models (VUMs)
representing specific population groups
PROBLEM IDENTIFICATIONPROBLEM IDENTIFICATION
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• A novel Virtual User Model (VUM) enabling the detailed description
of the physical, cognitive & behavioural user characteristics with special focus on the elderly and people with functional limitations.
• An innovative methodology (based on statistical analysis) for the development of Virtual User Models that represent specific disabled population groups.
• A framework for the adoption of virtual user models within accessibility simulation and ergonomy assessment processes of virtual designs.
• Design and development of tools for supporting and putting the aforementioned methodologies and frameworks into practice.
RESEARCH CONTRIBUTIONRESEARCH CONTRIBUTION
University of Surrey Department of Computing
STATE OF THE ART IN VIRTUAL USER MODELINGSTATE OF THE ART IN VIRTUAL USER MODELING
• Ontology-based models
– OntobUM (Razmerita et al., 2003)
– GUMO (Heckmann, 2006)
– VICON User Model (Pierre et al., 2011)
• XML-based models
– UserML (Heckmann, 2003)
– GUIDE User Model (Biswas & Langdon, 2012)
– User Models using relational databases
– MyUI User Model (Peissner et al. 2012)
• Personas (Cooper, 1999; Goodwin, 2002; Nielsen, 2002 & 2003; Pruitt & Grudin, 2003)
• Task modeling approaches
• ConcurTaskTree (Paternò, 1999)
• Hierarchical Task Analysis (HTA) (Annett & Duncan, 1967)
• UsiXML (Vanderdonckt, 2005)
University of Surrey Department of Computing
LIMITATIONS OF EXISTING APPROACHESLIMITATIONS OF EXISTING APPROACHES
Existing approach/methodology Limitations Personas Not expressed in a machine-readable format
Focused on specific body parts
Cannot represent in detail physical, cognitive & behavioural characteristics
VUMs in machine-readable format
Disabilities not supported
Focus on the progression of the disability User modelling methodologies Cannot create VUMs representing specific population groups User is defined independently from tasks User & task modelling
methodologies It’s not clear which tasks are affected by the disabilities
University of Surrey Department of Computing
• Introduction
•An innovative user modeling methodology focused on the elderly and disabled
• A methodology for creating virtual user models representing specific population groups
• The proposed user modeling methodology in practice
• Conclusions
An innovative user modeling methodology focused on the
elderly and disabled
University of Surrey Department of Computing
The proposed framework is based on the following seven
major building blocks:
• Abstract User Models– high level description of potential user models
• Generic Virtual User Models– a Generic Virtual User Model (GVUM) describes a set of users having a
specific set of disabilities
• Instance of a Generic Virtual User Model– an instance of a virtual user (e.g., Persona).
AN INNOVATIVE USER MODELING METHODOLOGY FOCUSED ON THE ELDERLY & DISABLEDAN INNOVATIVE USER MODELING METHODOLOGY FOCUSED ON THE ELDERLY & DISABLED
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• Primitive Tasks
– primitive human actions
• Task Models – actions that are being systematically performed in the context of the virtual
prototype to be tested
• Multimodal Interaction Models – alternative ways of a primitive task’s execution
• alternative modalities • assistive devices
• Simulation Models– simulation scenario
AN INNOVATIVE USER MODELING METHODOLOGY FOCUSED ON THE ELDERLY & DISABLEDAN INNOVATIVE USER MODELING METHODOLOGY FOCUSED ON THE ELDERLY & DISABLED
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ARCHITECTURE OF THE PROPOSED FRAMEWORKARCHITECTURE OF THE PROPOSED FRAMEWORK
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• Primitive tasks
– define primitive human actions – are related to the disability
category
• limited but sufficient number of primitive tasks
PRIMITIVE TASKSPRIMITIVE TASKS
Primitive task’s category
Primitive task
Motor Push
Motor Grasp
Cognitive Select
Cognitive Read
University of Surrey Department of Computing
• User tasks are divided in two categories:
– a) primitive (e.g., grasp, pull, walk, etc.)– b) complex (e.g., driving, telephone use, computer use, etc.)
• For each complex task, a Task Model is developed– to specify how the complex task can be analyzed into
primitive tasks.
TASK MODELSTASK MODELS
COMPLEX TASK PRIMITIVE TASK BODY PART OBJECT
CLOSE CAR DOOR WHILE
SEATED
REACH ARM DOOR
GRASP HAND INTERIOR DOOR
HANDLE
PULL HAND INTERIOR DOOR
HANDLE
PUSH HAND LOCK BUTTON
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• UsiXML’s taskModel
TASK MODELS - IMPLEMENTATIONTASK MODELS - IMPLEMENTATION
<?xml version="1.0" encoding="UTF-8"?>
<taskmodel>
<task id="st0task0" name="Close_car_door_while_seated" type="abstraction">
<task id="st0task1" name="Reach_door_with_arm" type="interaction"/>
<task id="st0task2" name="Grasp_interior_door_handle_with_hand" type="interaction"/>
<task id="st0task3" name="Pull_interior_door_handle_with_hand" type="interaction"/>
<task id="st0task4" name="Push_lock_button_with_hand" type="interaction"/>
</task>
……
</taskmodel>
UsiXML source code
University of Surrey Department of Computing
• Alternative ways of a primitive task’s execution with respect to
• the different target user groups• the replacement modalities• the possible use of assistive devices.
MULTIMODAL INTERACTION MODELSMULTIMODAL INTERACTION MODELS
Task Body
part
Modality Task
object
Disability Alternative
task(s)
Body part Alternative
modality
Alternative
task object/
assistive
device
Pull Hand
Motor Interior
door
handle
Upper limb
impaired
Speak Mouth Voice control Voice
activated
doors
Push Hand/Elbo
w
Motor Button that
closes door
University of Surrey Department of Computing
• UsiXML’s taskModel
MULTIMODAL INTERACTION MODELS - IMPLEMENTATIONMULTIMODAL INTERACTION MODELS - IMPLEMENTATION
<?xml version="1.0" encoding="UTF-8"?><taskmodel> <task id="st0task0" name="Pull_interior_door_handle" type="abstraction"> <task id="st0task1" name="Pull(modality:motor)(means:hand)(object:interior_door_handle)" type="interaction"/> <task id="st0task2" name="Speak(modality:voice)(means:mouth)(object:voice_activated_doors)" type="interaction"/> <task id="st0task3" name="Push(modality:motor)(means:hand,elbow)(object:button_that_closes_door)" type="interaction"/> </task> <deterministicChoice>
<source sourceId="st0task1"/> <target targetId="st0task2"/>
</deterministicChoice> <deterministicChoice>
<source sourceId="st0task2"/> <target targetId="st0task3"/>
</deterministicChoice></taskmodel>
UsiXML source code
University of Surrey Department of Computing
• A high level description
of potential user models
ABSTRACT USER MODELS ABSTRACT USER MODELS
Disability
category Disability
Short
description Quantitative disability metrics
Functional
limitations
(ICF
Classification)
Age-
relate
d
Motor Spinal
cord
injuries
(Thoracic
injuries)
Spinal cord
injuries
cause
myelopathy
or damage to
nerve roots
or
myelinated
fiber tracts
that carry
signals to
and from the
brain.
Temporal gait variables:
-Gait Cycle (sec):2.17 (1.05)
-Cadence (steps/min): 65.0 (23.1)
-Double support (%): 42.8 (10.2)
-Stride (m): 0.48 (0.13)
-Velocity ((m/sec)/height): 0.27 (0.13)
S120 Spinal cord and related structures,
S1200 Structure of spinal cord,
S12000 Cervical spinal cord,
s12001 Thoracic spinal cord,
s12002 Lumbosacral spinal cord,
s12008 Structure of spinal cord, other specified,
s12009 Structure of spinal cord unspecified,
Could
be
Kinematic variables:
-Hip excursion (˚): 39.3 (9.0)
-Knee excursion (˚): 38.1 (13.2)
-Ankle excursion (˚): 25.0 (4.9)
-Hip velocity (˚/sec): 38.2 (17.5)
-Knee velocity (flexion) (˚/sec): 64.1 (41.8)
-Knee velocity (extension) (˚/sec): 83.8 (54.2)
-Ankle velocity (˚/sec): 48.1 (30.8)
University of Surrey Department of Computing
• Refers to a class of virtual users exhibiting one or more
specific disabilities
• Describes the tasks affected by the disabilities and their associated disability-related parameters.
GENERIC VIRTUAL USER MODELSGENERIC VIRTUAL USER MODELS
Disability
category
Disability Affected primitive
tasks
Affected primitive tasks’ parameters
Grasp The user is able to grasp objects, with
size <= 3cm x 3cm x 3cm
Pull The user can pull an object with max_Force: 5N
Gait velocity ranges from 0.18 to 1.03 m/sec
Motor Hemiplegia
Walk
Abnormal step rhythm
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GENERIC VIRTUAL USER MODELS – SUPPORTED CHARACTERISTICSGENERIC VIRTUAL USER MODELS – SUPPORTED CHARACTERISTICS
Anthropometric
Upper Limb (left)
HandFinger
(thumb)
Wrist
Forearm
Elbow
Shoulder
Lower Limb (left)
Hip
Foot Toe
Neck
Spinal Colulmn
Gait
Finger (baby)
Finger (ring)
Finger (index)Finger
(middle)
Thigh
Knee
Ankle
Upper Limb (right)
HandFinger
(thumb)
Wrist
Forearm
Elbow
Shoulder
Finger (baby)
Finger (ring)
Finger (index) Finger
(middle)
Foot Toe
Foot Toe
Foot Toe
Foot Toe
Lower Limb (right)
Hip
Foot Toe
Thigh
Knee
Ankle
Foot Toe
Foot Toe
Foot Toe
Foot Toe
University of Surrey Department of Computing
GENERIC VIRTUAL USER MODELS - IMPLEMENTATIONGENERIC VIRTUAL USER MODELS - IMPLEMENTATION
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GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (BASIC CONTAINERS)GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (BASIC CONTAINERS)
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GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (HEAD ELEMENT)GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (HEAD ELEMENT)
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GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (DISABILITY MODEL)GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (DISABILITY MODEL)
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GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (GENERAL PARAMETERS)GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (GENERAL PARAMETERS)
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GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (GENERAL PREFERENCES)GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (GENERAL PREFERENCES)
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GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (ANTHROPOMETRIC PARAMETERS)GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (ANTHROPOMETRIC PARAMETERS)
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GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (UPPER LIMB PARAMETERS)GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (UPPER LIMB PARAMETERS)
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GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (LOWER LIMB PARAMETERS)GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (LOWER LIMB PARAMETERS)
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GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (NECK PARAMETERS)GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (NECK PARAMETERS)
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GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (SPINAL COLUMN PARAMETERS)GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (SPINAL COLUMN PARAMETERS)
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GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (GAIT PARAMETERS)GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (GAIT PARAMETERS)
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GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (VISUAL PARAMETERS)GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (VISUAL PARAMETERS)
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GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (HEARING PARAMETERS)GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (HEARING PARAMETERS)
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GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (SPEECH PARAMETERS)GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (SPEECH PARAMETERS)
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GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (COGNITIVE PARAMETERS)GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (COGNITIVE PARAMETERS)
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GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (BEHAVIOURAL PARAMETERS)GENERIC VIRTUAL USER MODELS – IMPLEMENTATION (BEHAVIOURAL PARAMETERS)
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INSTANCES OF GENERIC VIRTUAL USER MODELSINSTANCES OF GENERIC VIRTUAL USER MODELS
• A specific virtual user with specific disability related parameters including:
– disabilities
– affected primitive tasks
User ID Disability
category
Disability Affected primitive
tasks
Affected primitive tasks’ parameters
Grasp The user is able to grasp objects, with
size <= 2.5cm x 2.5cm x 2.5cm
Pull The user can pull an object with max_Force: 3N
Gait velocity : 0.9 m/s
User 1 Motor Hemiplegia
Walk
Abnormal step rhythm
University of Surrey Department of Computing
INSTANCES OF GENERIC VIRTUAL USER MODELS - IMPLEMENTATIONINSTANCES OF GENERIC VIRTUAL USER MODELS - IMPLEMENTATION
<?xml version="1.0" encoding="UTF-8"?><userModel xmlns="http://www.usixml.org" xmlns:xsi="http://www.w3.org/2001/XMLSchema- instance" xsi:schemaLocation="http://www.usixml.org/spec/UsiXML-ui_model.xsd" id="User_Model" name="Adam Brown" creationDate="2013/02/02 20:31:06" schemaVersion="1.8.0"> <head> <version modifDate="2010/10/17 20:31:06">0.0</version> <authorName>Automatically generated byethe User Model Generator</authorName> <comment>This model has been generated using the User Model Generator</comment> </head> <disabilityModel> <disability name="Spinal cord injuries" ageRelated="true" typesICF="b147, b760, b7603, b780"> <disabilityDetails>Spinal cord injuries cause myelopathy or damage to nerve roots or myelinated fiber tracts that carry signals to and from the brain. Depending on its classification and severity, this type of traumatic injury could also damage the grey matter in the central part of the cord, causing segmental losses of interneurons and motorneurons. </disabilityDetails> <affectedTasks> <affectedTask idTask="walking_ID" type="motor" name="walking"
details="inability to effectively transfer weight between legs, abnormal step rhythm, excessive plantar flexion during swing phase, falling during activities" />
<affectedTask idTask="driving_ID" type="motor" name="driving" details="automatic transmission and buttons on the steering wheel instead of pedals for brake and accelerator needed" /> </affectedTasks> </disability> </disabilityModel> <capabilityModel> <general> <gender>male</gender> <ageGroup>30-49</ageGroup> </general> <generalPreferences /> <anthropometric> <weight measureUnits="kgr" value="81.023056"/> <stature measureUnits="cm" value="174.586060"/> <headLength measureUnits="cm" value="19.533623"/>…
GVUM: range of values
Instance of a GVUM: absolute values
while…
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COMPARISON BETWEEN THE PROPOSED VUM & EXISTING MODELSCOMPARISON BETWEEN THE PROPOSED VUM & EXISTING MODELS
Number of variables of the proposed VUM
Number of variables of the GUIDE VUM
Number of variables of the MyUI VUM
Number of variables of the VICON VUM
• total: over 250 • total: 27• included in the
proposed VUM: 21
• total: 30• included in the
proposed VUM: 10
• total: 33• included in the
proposed VUM: 11
Why some variables of the others VUMs do not exist in the proposed VUM?
• they describe qualitative (not quantitative) characteristics and, thus, cannot be directly simulated (e.g., ability to move the hand precisely)
• describe the result of the simulation process (e.g., number of successful interactions with the system)
• are not actually user characteristics (e.g., amount of ambient light at the users place)
University of Surrey Department of Computing
• The scenario to be followed during the simulation process
• A Simulation Model may include three different types of tasks: – a) abstract tasks
– b) interaction tasks
– c) application tasks
SIMULATION MODELSSIMULATION MODELS
Scenario Main tasks Subtasks
Pull handbrake Use handbrake
Release handbrake
Open storage
compartment
Automotive simulation:
assess the accessibility of the
handbrake and the storage compartment Use storage
compartment
Close storage compartment
University of Surrey Department of Computing
• UsiXML’s taskModel
SIMULATION MODELS - IMPLEMENTATIONSIMULATION MODELS - IMPLEMENTATION
University of Surrey Department of Computing
• Introduction
•An innovative user modeling methodology focused on the elderly and disabled
• A methodology for creating virtual user models representing specific population groups
• The proposed user modeling methodology in practice
• Conclusions
• The proposed methodology offers• detailed definition of
• user (including disabilities)• user tasks• simulation scenarios
• Mapping/linkage between all these definitions
University of Surrey Department of Computing
• Introduction
•An innovative user modeling methodology focused on the elderly and disabled
• A methodology for creating virtual user models representing specific population groups
• The proposed user modeling methodology in practice
• Conclusions
A methodology for creating virtual user models
representing specific population groups
University of Surrey Department of Computing
1. Selection of disability parameters
– trying to model the most characteristic functional limitations (Abstract User Model) caused by each disability
• e.g., disability parameters for Gonarthrosis (osteoarthritis of the knee) include: knee flexion/extension and gait parameters, like cadence, step length, etc.
2. Acquire raw data (real measurements)
3. Estimate the probability density function (pdf) of each disability parameter
• Parametric regression• Non-parametric regression• An innovative hybrid regression method able to handle small sample sizes• All the above regression methods are applied and the one with the smallest Mean
Squared Error (MSE) is chosen as the most suitable
4. Validation and optimization of the regression model– Bootstrap method (Adèr et al., 2008)
MODELING A DISABILITY – GENERAL STEPSMODELING A DISABILITY – GENERAL STEPS
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STEP 2: MEASUREMENTS CONDUCTEDSTEP 2: MEASUREMENTS CONDUCTED
• Measurements were obtained from total 164 subjects
• Parkinson’s disease (17 subjects)
• Stroke (36 subjects)
• Multiple Sclerosis (20 subjects)
• Cerebral Palsy (17 subjects)
• Coxarthrosis (7 subjects)
• Gonarthrosis (5 subjects)
• Lower Limb Amputation (3 subjects)
• Elderly people (59 subjects)
University of Surrey Department of Computing
STEP3: PARAMETRIC REGRESSIONSTEP3: PARAMETRIC REGRESSION
2
2
2
)(
22
1)(
x
exf
Distribution Type PDF Example
Gaussian Hip flexion of the elderly
Distribution Type PDF Example
Gaussian mixture Fingers’ extension of people with stroke
Distribution Type
PDF Example
Exponential Gait cycle of stroke patients
Distribution Type
PDF Example
Lévy Double support of stroke patients
Distribution Type
PDF Example
Uniform Gait velocity of patients with cerebral palsy
n
i
x
i
ii
i
ewxf1
2
)(
2
2
2
2
1)(
xk ex
kkxf
1
)()(
1)(
23
)(2
)(2)(
x
ecxf
x
c
elsewhere
baxabxf
,0
],[,1
)(
University of Surrey Department of Computing
• PDF is unknown
STEP3: NON-PARAMETRIC REGRESSIONSTEP3: NON-PARAMETRIC REGRESSION
Kernel regression
Knee angular velocity of the stroke patients
Local polynomial multiple regression
Step width of the elderly
•Seems to follow a Lévy distribution. •This can be proved by examining the MSE resulting from parametric regression:
•Lévy: MSE: 0.00204•Gaussian: MSE: 0.00256•Gaussian mixture: MSE: 0.00290•Uniform: MSE: 0.00367•Exponential: MSE: 0.07297
•Seems to follow a Gaussian distribution.•This can be proved by examining the MSE resulting from parametric regression:
•Gaussian: MSE: 0.94464•Gaussian mixture: MSE: 1.02365•Uniform: MSE: 1.26263•Exponential: MSE: 1.29327•Lévy: MSE: 2.69961
University of Surrey Department of Computing
• Sample size is important! • BUT…the task of taking real measurements from disabled people is
– very difficult – needs much resources
• Estimating the PDF of a disability parameter using a limited set of subjects could result in great variance.
We introduce the concept of hybrid regression that exploits– information found on the literature – real measurements– virtual measurements
STEP3: HYBRID REGRESSIONSTEP3: HYBRID REGRESSION
Solution
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• The probability distribution followed by a disability parameter is
defined using an ε-contaminated class of priors, namely Γ. – The contaminated class controls the balance of the determinate
priors, , and other possible priors, , through an ε-level of weight.
• The ε-contaminated class is formulated as:
– where D is our initial data set and
– ε denotes the level of uncertainty
HYBRID REGRESSION – HYBRID REGRESSION – Ε-Ε-CONTAMINATED CLASS OF PRIORSCONTAMINATED CLASS OF PRIORS
s'0 sq'
)}(),,|()1()|({ 20 yqDyDy
Parametric regression on the original sample
User defined
We tested the following values:
0, 0.01, 0.05, 0.10, 0.20
Gaussian
University of Surrey Department of Computing
• Let variable Y represent the real measurements
• Assumption: Y is be the response of the simple linear regression model
– where is the predictor value
– is the intercept,
– is the slope and
– are independent and identically distributed random variables from
HYBRID REGRESSION – LINEARITY ASSUMPTIONHYBRID REGRESSION – LINEARITY ASSUMPTION
iii XY 1
iX0
1
i ),0( 2N
University of Surrey Department of Computing
• Our initial data set consists of measurements , where:
– each is a real measurement
– ‘s are calculated as follows:• from Gaussian pdf found in the literature
– to keep a relevance between ‘s and ‘s, we get random samples ( ‘s) that meet
• When there is no available information in the literature, we calculate the ‘s using the following formula:
With this process, ‘s are actually coming from a random movement (within a specific range) of the initial samples ‘s.
HYBRID REGRESSION – INITIAL DATA SETHYBRID REGRESSION – INITIAL DATA SET
D n niXY ii ,...1),,(
iY
iX),( 2
litlitN
iY iXiX
410 YYX litii
iX
kY
kYYX
ii
iXiY
Random value within [0.95, 1.05]
University of Surrey Department of Computing
• A virtual data set consisting of m pairs is constructed
as follows:– ‘s are random samples from the pdf of ‘s.
– ‘s are random samples of , assuming that is a Gaussian distribution with
and
where and
HYBRID REGRESSION – VIRTUAL SAMPLESHYBRID REGRESSION – VIRTUAL SAMPLES
AD
AjX iXA
jY 0
AA
YXYA
01
00
n
iiiY
xyn
DMSEA
1
201
00
2 )(1
)(
n
i
n
iii
n
ii
n
ii
n
iii
Xn
X
YXn
YX
1
2
1
2
11101
1
1
XY 01
00
),( Aj
Aj XY
User-selected valueWe tested the following values:0, 0.5n, n, 2n, 3n
University of Surrey Department of Computing
• At this stage, we have available:
– The initial data set
– A virtual data set
• Both data sets are used for the fine tuning of the parameters of
, which describes the distribution of disability parameter .
• Now, for the definition of , we perform Gaussian parametric regression on the real measurements (Yi’s).
HYBRID REGRESSION – FINE TUNING OF PARAMETERS OF HYBRID REGRESSION – FINE TUNING OF PARAMETERS OF ΓΓ
AD
D
y
0
This is what we are searching for…
University of Surrey Department of Computing
• The maximum likelihood estimate taken into account both the
original and the virtual sample sets ( and , correspondingly) is given by:
• Using the EM algorithm for parameter estimation concludes after k iterations with and , which are used for the optimisation of the Gaussian distribution . – More specifically,
• now becomes
• and becomes
HYBRID REGRESSION – EM ALGORITHMHYBRID REGRESSION – EM ALGORITHM
D AD
mn
ni
Ai
n
iiMLE DyDy
11, )|()|(maxarg
k0 k
1
0
AA
YXYA
01
00 AkkA
YXYA 10
n
iiiY
xyn
DMSEA
1
201
00
2 )(1
)(
n
ii
kkiY
xyn
DMSEA
1
210
2 )(1
)(
University of Surrey Department of Computing
• The combination of
– ε (uncertainty) and – m (number of virtual samples)
that results to the minimum MSE is used to form the final Γ.
HUBRID REGRESSION – RESULTED HUBRID REGRESSION – RESULTED ΓΓ
)}(),,|()1()|({ 20 yqDyDy
University of Surrey Department of Computing
HYBRID REGRESSION EXAMPLE - STEP WIDTH OF THE ELDERLYHYBRID REGRESSION EXAMPLE - STEP WIDTH OF THE ELDERLY
Purple line: data taken from the MSP
Green line: virtual samples generated considering the MSP data.
Yellow line: virtual samples generated considering the data coming from the literature
Light blue line: pdf reported in the literature
Dark blue line: represents all the samples (both real and virtual)
Red line: final estimated pdf
Regression type MSE
Parametric – Gaussian 0.74377
Parametric – Lévy 2.73860
Parametric – Gaussian mixture
0.91292
Parametric – Exponential 1.46577
Parametric – Uniform 1.13034
Nonparametric – Kernel 0.5102
Nonparametric – Polynomial
0.6414
Hybrid 0.00642
University of Surrey Department of Computing
• The definition of , enables the calculation of disability parameter values for different population groups. – e.g. , to find the value of wrist flexion that corresponds to the
90% of people with arthritis, we have:
CALCULATING THE DISABILITY PARAMETER VALUES FROM THE ESTIMATED PDFCALCULATING THE DISABILITY PARAMETER VALUES FROM THE ESTIMATED PDF
b
a
jijiijijji aFbFdxxf )()()( ,,,
jiF ,
9.0)0()( ,, onwristFlexiarthritisonwristFlexiarthritis FkF
PDF of parameter j of disability i
Finite integral:
The value of wrist flexion corresponding to the 90% of
people with arthritis
University of Surrey Department of Computing
REGRESSION MODELS - INDICATIVE RESULTS (STROKE PATIENTS)REGRESSION MODELS - INDICATIVE RESULTS (STROKE PATIENTS)
Parameter Name
Before validation & optimisation After optimisation
Regression Type
Distribution Type PDF
Parameters Distribution Type
PDF Parameters
Gait Cycle Parametric
Levy
23
)(2
)(2)(
x
ecxf
x
c
μ = 0,918
c = 0,29893
Gaussian
2
2
2
)(
22
1)(
x
exf
μ = 1,81818
σ = 0,609692
Push Force
Hybrid )()()1()( 0 xqxxf
Π0 (Gaussian):
μ=65,47 σ=67,54
q (Exponential):
λ=0,02 ε = 0,05
Gaussian
2
2
2
)(
22
1)(
x
exf
μ = 78,5959
σ = 61,9811
Hip Extension
Right Parametric
Gaussian Mixture
n
i
x
i
ii
i
ewxf1
2
)(
2
2
2
2
1)(
μ1=32,81111 σ1=5,10935 w1=0,34615
μ2=15,82353 σ2=4,21936 w2=0,65385
Gaussian Mixture
n
i
x
i
ii
i
ewxf1
2
)(
2
2
2
2
1)(
μ1 = 32,597 σ1 = 5,682615 w1 = 0,343074
μ2 = 15,86191 σ2 = 4,316446 w2 = 0,656926
University of Surrey Department of Computing
• Introduction
•An innovative user modeling methodology focused on the elderly and disabled
• A methodology for creating virtual user models representing specific population groups
• The proposed user modeling methodology in practice
• Conclusions
The proposed user modeling methodology in practice
University of Surrey Department of Computing
• VERITAS simulation
platform • Automatic simulated
accessibility and ergonomy assessment of designs
• Its development was based on the proposed user modeling methodology
ACCESSIBILITY AND ERGONOMY EVALUATION OF DESIGNSACCESSIBILITY AND ERGONOMY EVALUATION OF DESIGNSPhysical characteristics Normal values Elderly (60 to 84) Rheumatoid arthritis
Hand maximum pull force (N) 335 76.8
Wrist extension (°) 0 - 60
Wrist radial deviation (°) 0 - 27.5 0 – 19
Wrist ulnar deviation (°) 0 – 35 0 – 26
Forearm supination (°) 0 – 85 0 – 74
Forearm pronation (°) 0 – 85 0 – 71
Elbow flexion (°) 0 – 142.5
Elbow hyper-extension (°) 0 – 10 0 – 4
Shoulder flexion (°) 0 – 160 0 – 10
Shoulder abduction (°) 0 – 85 0 – 67 0 – 15
Shoulder internal rotation (°) 0 – 80 0 – 63
Shoulder external rotation (°) 0 – 45 0 - 15
Spinal column flexion (°) 0 - 90 0 – 23.6
Spinal column extension (°) 0 – 30 0 - 17
Spinal column left lateral flexion (°) 0 – 25 0 – 19
Spinal column right lateral flexion (°) 0 – 25 0 - 20
University of Surrey Department of Computing
ACCESSIBILITY AND ERGONOMY EVALUATION OF DESIGNS – STAPLER USEACCESSIBILITY AND ERGONOMY EVALUATION OF DESIGNS – STAPLER USE
Stapler torque-resistance (Nm) Normal Elderly Rheumatoid arthritis
2.5 Pass Pass Pass
5.0 Pass Pass Pass
10 Pass Pass Pass
15 Pass Fail Pass
20 Pass Fail Fail
25 Pass Fail Fail
30 Fail Fail Fail
University of Surrey Department of Computing
• DIAS tool: Impairments
simulator
• It has been extended to support the proposed VUMs.
IMPAIRMENT SIMULATION OVER JAVA, MOBILE AND WEB APPLICATIONSIMPAIRMENT SIMULATION OVER JAVA, MOBILE AND WEB APPLICATIONS
University of Surrey Department of Computing
• ACT-R: a cognitive
architecture showing how human cognition works.
• The proposed VUM includes ACT-R parameters.
• Reading task example – a non-stressed user
• visual-attention-latency: 0.085 sec
• reading task – total time: 3.57 sec
– a user with acute stress• visual-attention-latency: 0.0978 sec
• reading task – total time: 3.726 sec
COGNITIVE SIMULATION – THE PROPOSED VUMs WITHIN ACT-RCOGNITIVE SIMULATION – THE PROPOSED VUMs WITHIN ACT-R
University of Surrey Department of Computing
• Introduction
•A new User Model able to describe elderly and disabled people
•An innovative user modeling methodology focused on the elderly and disabled
• A methodology for creating virtual user models representing specific population groups
• The proposed user modeling methodology in practice
• Conclusions
Conclusions – Future Work
University of Surrey Department of Computing
1. A new Virtual User Model was introduced, able to describe a variety of
user characteristics, including physical, cognitive and behavioural.
2. This Virtual User Model is a part of an innovative coupled user and task modeling methodology that aims to be used mainly in simulation frameworks performing accessibility assessment of virtual designs.
3. A methodology for creating virtual user models able to represent specific disabled population groups
4. Tools for putting the proposed user modeling methodology into practice
CONCLUSIONSCONCLUSIONS
University of Surrey Department of Computing
• The proposed VUM goes one step beyond the state-of-the-art, as it
enables the detailed description of the functioning of the elderly and disabled
• The proposed coupled user & task modeling methodology can be a valuable tool for the designers/developers towards the development of accessible products
• The proposed methodology for creating virtual user models able to represent specific disabled population groups offers designers with the opportunity to define an accessibility threshold for their designs.
IMPORTANCE OF THE RESEARCH CONTRIBUTIONIMPORTANCE OF THE RESEARCH CONTRIBUTION
University of Surrey Department of Computing
• The accuracy of a VUM representing a specific population group
depends on the sample size.
• However, the proposed methodology can result to the production of very accurate personas.
CRITICAL DISCUSSIONCRITICAL DISCUSSION
University of Surrey Department of Computing
• Fine-tuning of the resulted regression models using
• more data coming from the literature
• more real measurements
• More disabilities could be modeled
• A coupled physical-cognitive user modeling approach could be considered
• Continue the dissemination through– the VUMS cluster of projects (http://www.veritas-project.eu/vums/) – the activities of the W3C MBUI Working Group
FUTURE WORKFUTURE WORK
University of Surrey Department of Computing
1. Kaklanis, N., Moschonas, P., Moustakas, K., Tzovaras, D., “Virtual User Models for the elderly and disabled
for automatic simulated accessibility and ergonomy evaluation of designs”, Universal Access in the Information Society, Special Issue: Accessibility aspects in UIDLs, Springer
2. Giakoumis, D., Kaklanis, N., Votis, K., Tzovaras, D., “Enabling user interface developers to understand accessibility limitations through visual, hearing, physical and cognitive impairment simulation”, Universal Access in the Information Society, Springer
3. Kaklanis, N., Votis, K., Tzovaras, D., ”Open Touch/Sound Maps: A system to convey street data through haptic and auditory feedback”, Computers & Geosciences, Elsevier (accepted for publication)
4. Kaklanis, N., Votis, K., Tzovaras, D., “An online multimodal audio-haptic framework for visually impaired users accessing OpenStreetMap data”, Universal Access in the Information Society, Special Issue: 3rd generation accessibility: Information and Communication Technologies towards universal access, Springer (under review)
5. Kaklanis, N., Biswas, P., Mohamad, Y., Gonzalez, M.F., Peissner, M., Langdon, P., Tzovaras, D., Jung, C, “Towards Standardization of User Models for Simulation and Adaptation Purposes”, Universal Access in the Information Society, Special Issue: 3rd generation accessibility: Information and Communication Technologies towards universal access, Springer (under review)
6. Koutkias, V., Kaklanis, N., Votis, K., Tzovaras, D., Maglaveras, N., “An Integrated Semantic Framework Supporting Universal Accessibility to ICT”, Universal Access in the Information Society, Special Issue: 3rd generation accessibility: Information and Communication Technologies towards universal access, Springer (under review)
7. Kaklanis, N., Stavropoulos, G., Tzovaras, D., “Modeling disabilities through regression analysis for the development of accurate virtual user models”, User Modeling and User-Adapted Interaction, The Journal of Personalization Research (under review)
PUBLICATIONS – SCIENTIFIC JOURNALSPUBLICATIONS – SCIENTIFIC JOURNALS
University of Surrey Department of Computing
8. Kaklanis, N., Votis, K., Moustakas, K., Tzovaras, D. 3D HapticWebBrowser: Towards Universal Web
Navigation for the Visually Impaired, 7th International Conference on Web Accessibility, W4A, 26-27 April 2010, Raleigh, North Carolina, USA
This paper won the "Judges Award” at the “Web Accessibility Challenge” (sponsored by Microsoft)
9. Kaklanis, N., Moustakas, K., Votis, K., Tzovaras, D. A framework for accessibility testing of virtual environments based on UsiXML”, ACM SIGCHI Symposium on Engineering Interactive Computing Systems, UsiXML-EICS 2010, Berlin, June 2010
10. Kaklanis, N. Moschonas, P., Moustakas, K., Tzovaras, D., Enforcing accessible design of products and services through simulated accessibility evaluation, CONFIDENCE 2010, Jyväskylä, Finland, 9-10 December 2010
11. Kaklanis, N., Moustakas, K., Tzovaras, D. A framework for automatic simulated accessibility assessment in virtual environments, HCI International 2011, Orlando, Florida, USA, 9-14 July 2011
12. Kaklanis, N., Votis, K., Moschonas, P., Tzovaras, D. HapticRiaMaps: Towards Interactive exploration of Web World maps for the Visually Impaired”, W4A 2011, Hyderabad, India, March 2011
13. Oikonomou, T., Kaklanis, N., Votis, K., Kastori, G.E., Partarakis, N., Tzovaras, D. WaaT: Personalised Web Accessibility Evaluation Tool, W4A 2011, Hyderabad, India, March 2011
14. Oikonomou, T., Kaklanis, N., Votis, K., Tzovaras, D. An Accessibility Assessment Framework for Improving Designers Experience in Web Applications, HCII 2011, Orlando, Florida, USA, July 2011
15. Partarakis, N., Doulgeraki, C., Antona, M., Oikonomou, T., Kaklanis, N., Votis, K., Kastori, G.E., Tzovaras, D. A Unified Environment for Accessing a Suite of Accessibility Evaluation Facilities, HCII 2011, Orlando, Florida, USA, July 2011
16. Moschonas, P., Kaklanis, N., Tsakiris, A., Moustakas, K., Tzovaras, D., “An open simulation framework for automated and immersive accessibility engineering”, 6th Cambridge workshop on universal access and assistive technology, CWUAAT 2012, Fitzwilliam College, University of Cambridge, March, 2012
PUBLICATIONS – INTERNATIONAL CONFERENCESPUBLICATIONS – INTERNATIONAL CONFERENCES
University of Surrey Department of Computing
17. Korn, P., Kaklanis, N., Votis, K., Tzovaras, D., “Using the AEGIS OAF: making an accessible RIA”, 27th
Annual International Technology and Persons with Disabilities Conference, CSUN 2012, March, 2012, San Diego, CA
18. Moschonas, P., Kaklanis, N., Tzovaras, D., “Novel Human Factors for Ergonomy Evaluation in Virtual Environments Using Virtual User Models”, 10th International Conference on Virtual Reality Continuum and its Applications in Industry, VRCAI’ 2011, December, 2011, Hong Kong, China
19. Kaklanis, N., Moustakas, K., Tzovaras, D., "An extension of UsiXML enabling the detailed description of users including elderly and disabled", in International Workshop on Software Support for User Interface Description Language, Interact 2011, Lisbon, September, 2011
20. Mohamad, Y., Biswas, P., Langdon, P., Peissner, M., Dangelmaier, M., Jung, C., Wolf P., Kaklanis, N., “Standardisation of user models for designing and using inclusive products”, Joint Virtual Reality Conference (JVRC), 20-21 September, 2011, Nottingham, UK
21. Kaklanis, N., Moustakas, K., Tzovaras, D., 2012. A methodology for generating virtual user models of elderly and disabled for the accessibility assessment of new products. In Proceedings of the 13th international conference on Computers Helping People with Special Needs - Volume Part I (ICCHP'12), Klaus Miesenberger, Arthur Karshmer, Petr Penaz, and Wolfgang Zagler (Eds.), Vol. Part I. Springer-Verlag, Berlin, Heidelberg, 295-302. DOI=10.1007/978-3-642-31522-0_44 http://dx.doi.org/10.1007/978-3-642-31522-0_44
22. Moschonas, P., Tsakiris., A., Kaklanis, N., Stavropoulos, G., Tzovaras, D., “Holistic accessibility evaluation using VR simulation of users with special needs”, UMAP 2012, Montreal, Canada, July, 2012
23. Kaklanis, N., Mohamad, Y., Peissner, M., Biswas, P., Langdon, P., Tzovaras, D., ”An Interoperable and Inclusive User Modelling concept for Simulation and Adaptation”, UMAP 2012, Montreal, Canada, July, 2012
24. Kaklanis, N., Votis, K., Tzovaras, D., “Touching OpenStreetMap Data in Mobile Context for the Visually Impaired”, CHI 2013 Mobile Accessibility Workshop, April 28, 2013, Paris, France (accepted)
PUBLICATIONS – INTERNATIONAL CONFERENCESPUBLICATIONS – INTERNATIONAL CONFERENCES
University of Surrey Department of Computing
25. Kaklanis, N., Votis, K., Tzovaras, D., “A Mobile Interactive Maps Application for a Visually Impaired
Audience”, The Paciello Group Web Accessibility Challenge - W4A 2013, 10th International Cross-Disciplinary Conference on Web Accessibility, 13-15 May 2013, Rio de Janeiro, Brazil (accepted)
26. Tsakiris, A., Kaklanis, N., Paliokas, I., Stavropoulos G., and Tzovaras, D. Cognitive Impairments Simulation in a Holistic GUI Accessibility Assessment Framework, 12th European AAATE Conference, 19-22 September 2013, Vilamoura, Algarve, Portugal (under review)
PUBLICATIONS – INTERNATIONAL CONFERENCESPUBLICATIONS – INTERNATIONAL CONFERENCES
University of Surrey Department of Computing
27. Kaklanis, N., Moustakas, K., Tzovaras, D. Haptic Rendering of HTML components and 2D maps included in
web pages, in G. Ghinea, F. Andres, S. Gulliver (Eds) Multiple Sensorial Media Advances and Applications: New Developments in MulSeMedia, 2010
28. Biswas, P., Kaklanis, N., Mohamad, Y., Peissner, M., Langdon, P., Tzovaras, D., Jung, C., “An Interoperable and Inclusive User Modeling concept for Simulation and Adaptation”, A Multimodal End-2-End Approach to Accessible Computing, Springer
PUBLICATIONS – BOOK CHAPTERSPUBLICATIONS – BOOK CHAPTERS
University of Surrey Department of Computing
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