The 11th COE Postdoctoral and Doctoral Researchers
Technical Presentation

List of Contents
Evaluate User Panels for Plant Operations
From the Perceptual Viewpoint
Xiwei Liu

Systems & Control Laboratory

Background
Objectives
Our Evaluation Approach
Perceptual Processor Model
Evaluation Experiments
Summary
Future Workxiwei-l@is.naist.jp

Background
Ubiquitous user panels

Background (Cont'd)
Life cycle of human-computer interface (HCI)
An iterative course including design, prototype, andevaluation
Evaluation techniques(Cognitive Complexity Theory)

Classification of evaluation techniques

Life cycle of HCI(Sandrine Balbo et. al, 1993)

Objectives
Evaluate the visual effect of user panels forplant operations by using human model.
Improve the evaluation based on
psychological experiments.

Our Evaluation Approach
Human model based approach: user panelevaluation is based on human and HCI model.
Human model

Plant model
HCI model
General framework of HCIAdvantages:•All parameters can be set freely for
all kinds of cases.
•Evaluate without human subject.

Disadvantages:
Assumptions might be wrong.
Build human, HCI, and plantmodels.

1Our Evaluation Approach (Cont'd)Model a user panel
All of the iconic and digital information are extracted to atable that includes name, important level, shape, size, color,position, status, and value of every graphic item.

Experiment environment
All real-time data inthe plant simulator
can be accessed toby using the object
linking andembedding forprocess control(OPC) technology.

Our Evaluation Approach (Cont'd)

OPC Server

DistributedControl
SystemUser panel

Dynamic table

OPC ClientPlant
Simulator

HMI Model
Human Model

Hardware Configuration

Our Evaluation Approach (Cont'd)
The human model was built incorporating with the estimationof mental and physical states.
Part aPart b
Part cMotor
Processor

MentalWorkload

Cognitive
Processor

MentalState

Short-term
MemoryAttention

Long-term
MemoryPerceptual Icons, Data
Processor Alarms

Realtime
HumanData
Plant
MachineSimulator
Interface
Model

User panel
Evaluation
fromperceptual
viewpoint

Click, Keypress
Eye Movement

Model structure of a human-machine system for plant operations

Part a: HMI modelPart b: human modelPart c: estimation model of human mental state and attention level

Perceptual Processor Model (Cont'd)Perceptual Processor Model
Visual field
Eye movementPerceptual performance

Operator'sEye

Human visual field is a circle
around a fixed pointδ

R = L tan( Ap × )2L : length
R : visual field radius
Ap: perceptual attention levelδ : visual angle

The default value of L is 75cm.
Ap is set to 0.8, 0.6, and 0.4 according to former
research (Kurooka et al., 2001).
δ is set to 10° based on EPIC cognitive
architecture (Kieras et al., 1997).

Perceptual Processor Model (Cont'd)

Shift span of saccade :• Centric distance of two consecutive
saccades• Scanning can cover all elements in apanel, only when it is smaller than 2 R• Shift trajectory• Overlay level• Fixation and saccade intervals

RL

Human visual field• Eye movement•Shift trajectory•Overlay level•Fixation and saccade intervals

Visual field
Eye movement
Perceptual performance

MonitoredPicture

VisualAngle δ

D=2Rα

D: shift span
α: overlay level

2R

2R

Shift span of saccade• The total duration of fixationand saccade is 70~700
millisecond (Card, et al., 1983)

Eye movement in simulation run2

Perceptual Processor Model (Cont'd)
Visual field
Eye movement
Perceptual performancePerceptual Processor Model (Cont'd)
Visual field
Eye movement
Perceptual performance

TypesCapacity(Card, et al., 1983)

Visual strength of a graphic element within a visual fieldElaboration(Weber-Fechner’s law)
V: visual strength of the element
x: color difference
y: size
z: position
Ap: perceptual attention level

V = f ( x, y, z , Ap )V < θ P (Perceptual threshold)Evaluation Experiments

Panel 2

Evaluation Experiments (Cont'd)
Visual strength curves of panel 1
CommonImportant

Ap=0.8 Ap=0.6

Loss2.5

Evaluation Experiments (Cont'd)CommonVisual strength

Important

2.5

4.5

2

Ap=0.8 Ap=0.6 3.54
Ap=0.4

1.5

CommonImportantCommonVisual strength

Important

2.5

23

1.5

2.5

221

1.5

Loss

Atkinson-Shiffrin Model

Visual strength curves of panel 2

Visual strength

3.5
3Ap=0.4Retrieval

SelectiveAttention

Build HCI model for an objective graphic panel, anddivide the graphic items into two groups—commonand important process variables.
Scan throughout the panel with different perceptual
attention level and record the visual strength of every
graphic item.
Integrate the above evaluation results and find theweak points of the graphic panels and their causes.

Low density with 33 items

4

Short TermMemory

Evaluation scenarios

We evaluated 6 user panels, and show two of them as examples.

Visual strength

Sensing
Sensory Memory

Evaluation Experiments (Cont'd)Target user panels

Panel 1Long Term Memory

Environment

If the number ofgraphic items is morethan 17, some itemswith low visual
strength might be lost.

The element might not be captured
and sent to the short-term memory.

High density with 75 items

Duration

Sensory Iconic memory [7~17] letter [70-1000] msmemory
Echoic memory [4.4~6.2] letter [900-3500] ms

11.5110.50.50.50.5001 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47
User panel: overview1

1 35 7

9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47
User panel: overview1

Graphic items
Visual strength

CommonGraphic items

Important

012345678 9 10 11 12 13 14 15 16 17 18 19 20 21
User panel: overview201234567

Graphic items

8 9 10 11 12 13 14 15 16 17 18 19 20 21
User panel: overview2

Graphic items

CommonVisual strength

0.900

Important

1.000

0.800

0.900

0.700

0.800

0.600

0.700
0.600

0.500

0.500

0.400

0.400

0.300

0.300

0.200

0.200

0.100

0.100

0.000
147

10 13 16 19 22 25 28 31 34 37 40 43 46
User panel: overview1

Graphic items

0.000
12

34

567

8 9 10 11 12 13 14 15 16 17 18 19 20 21
User panel: overview2

Graphic items

3

Evaluation Experiments (Cont'd)
Evaluation pointsDensity check
Visual strength curves
Visual strength of every item
Average visual strength

Evaluation Experiments (Cont'd)
The number of items
33

Ap=0.8
Important items

2.5

2.5

22

1.5

1.5

11

0.5

0.5

00 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8

V<0.3

400.1

Ap=0.8
Common items

V<0.25

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Visual strength

Two histograms of the distribution of visual strength for panel 1z Weak point 1: The number of items within every

visual field exceeds 17.
z Weak point 2: The visual strength curve of Important
items is lower than that of common onesz Weak point 3
Ap=0.8Ap=0.6

Ap=0.4

Condition

Ap=0.8Ap=0.6

Ap=0.4

Important item

V<0.3

V<0.2

V<0.1

Process overview

Vt<0.6

Vt<0.3Vt<0.15

Common item

V<0.25

V<0.15

V<0.05

Operational panel

Vt<0.8Vt<0.4

Vt<0.2

V: Visual strength

Evaluation Experiments (Cont'd)
Evaluation results:Density is proper—no visual field can hold more than17 items.
The important items in panel 2 are not given suitableemphasis.
There are 20 and 3 weak items in panel 1 and 2
respectively.
The average visual strengths of both panel 1 and 2 areproper.

Future Workz Weak point 4Condition

Vt: Average visual strength

Summary
We proposed a human model to user panelevaluation from the viewpoint of perception.
The perceptual processor model is introduced.
Through simulation runs, we showed the weak
points of user panels.

Future Work (Cont'd)Based on eye mark recorder, we improve thesimulation fidelity by

A psychological experiment to obtain eye movement trajectoryof a human subject

obtaining the trajectory of eye movement
modifying the algorithm of visual strength
validating the simulation

Experiment environment: ask the subject to scan throughout
the user panel 1, and try to relaxedly cover all graphic items.
Experiment results: extract the coordinates of fixation points
and fixation and saccade intervals.

4

Future Work (Cont'd)

Future Work (Cont'd)
Simulation program based on the human subject’s eye movement

The characteristics of human eye movement.
Try to focus on graphic items.
Fixation point is moved based on the understanding of user panel.
Fixation time is decided by the complexity of graphic items withina visual field.
A problem of the former simulation:
The actual trajectory is not a zigzag line as the assumption in the former
simulation run. Accordingly, the fixation points in simulation are different
to the real case. Such difference affects the position factor in the calculationof visual strength.

Thank you very much for your attention!

5