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1
Augmented Reality Interfaces for Procedural Tasks
Steven J. Henderson
Department of Computer Science - Columbia University
April 14, 2011
2
Procedural Task
A task whose learning requires the integration of two kinds of
human capability–intellectual skills and motor skills [Gagné-77]
Evolution of Procedural Task instruction
- Printed technical manuals
- Interactive Technical Manuals (IETMs) [Connell-78]
- Task Guidance Systems [Ockerman-98] (mobile or wearable IETMs)
1943 Harley-Davidson
Maintenance Manual
Task guidance system for telecom
troubleshooting
Example IETM Wearable IETM [Siegel-01]
Augmented Reality (AR)
AR view of starter installation
Integration of virtual content with a user’s natural view of the
environment, combining real and virtual objects interactively, at
real-time frame rates, and geometrically aligning them with each
other [Azuma-01]
User wearing AR display
4
Research Questions
What are the benefits of using an AR interface to support
procedural tasks?
- Benefits during informational activities
- Benefits during psychomotor activities
How can we develop effective user interaction techniques for
an AR interface supporting procedural tasks?
- Minimize interference with the task environment
- Minimize interference with the worker
What are the general design consideration in developing an
AR interface for supporting procedural tasks?
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Contribution Overview Evaluation of AR in Informational
Phases of Procedural Tasks (Chapter 3)
- Articulated benefits of AR in informational phase
Evaluation of AR in Psychomotor Phases
of Procedural Tasks (Chapter 4)
- Articulated benefits of AR in psychomotor phase
Opportunistic Controls (Chapter 5)
- Proposed and evaluated novel class of interaction
techniques for procedural tasks.
Architecture for Constructing AR Interface for
Procedural Tasks (Chapter 6)
6
Talk Structure
Related Work exploring Procedural Tasks (Chapter 2)
AR Assistance in Informational
Phases of Procedural Tasks
(Chapter 3)
AR Assistance in Psychomotor
Phases of Procedural Tasks
(Chapter 4)
Opportunistic Controls supporting
User Interaction with Procedural Tasks
(Chapter 5)
ARMAR Architecture (Chapter 6)
Conclusions & Future Directions (Chapter 7)
Talk Structure
Related Work exploring Procedural Tasks (Chapter 2)
Procedural Task Instructions How do people think about tasks?
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Nature of Procedural Tasks
- [Gilberth-24], [Drury-90], [Vujosevic-97]
Required Abilities and Skills
- [Fleishman-84],[Bloomfield-03]
Teaching and Learning
- [Gagné-69, Bloom-76, Wetzel-83, Tannenbaum-93]
Fleishman’s Taxonomy
of Psychomotor abilities
Gilbreth and Gilbreth’s Therbligs
Bipartite Nature of Procedural Tasks
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Neumann & Majoros [1998] proposed two-phase cognitive model for AR
applications in Maintenance and Manufacturing:
‒ Informational Phase: Read, comprehend, understand
‒ Work piece Phase: Psychomotor, align, orient, adjust, manipulate
Richardson and colleagues [2004] confirm similar model for assembly tasks:
comprehension
Phases follow egocentric vs. allocentric reference systems [Klatzky-1998]
Ability to visualize within each reference system affects how people
perform tasks [Kozhevnikov-2006]
Egocentric : Ability to imagine taking a different perspective in
space. Required for navigation, tracking, orienting
Allocentric: Ability to mentally manipulate objects from a stationary
point of view. Required for operation, repair, mechanical devices
Procedural Task Instructions What are the ingredients of high-quality instruction?
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Use of Pictures
- [Booher-75],[Ellis-96]
Use of Text
- [Wright-77, Wright-81],[Smith-84]
Use of Animation
- [Tversky-02]
Cognitive design principles
- [Heiser-04]
[Heiser-04]
[Booher-75]
Computerized Instructions
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IBIS [Seligmann-91]
Computer Generated Assembly
Instructions [Agrawala-03]
IBIS [Seligmann-91]
[Agrawala-03]
Creation
IETMs
- [Connel-78], [Rainey-91],[Boose-03]
Wearable task guidance systems
- [Siegel-96, 97, 01]
- [Ockermann-98]
Presentation
IETM
Talk Structure
AR Assistance in Informational
Phases of Procedural Tasks
(Chapter 3)
13
Maintenance and Repair Procedural Tasks
Well-formed design space for
application of AR
Current maintenance and repair
systems feature paper or Interactive
Electronic Technical Manuals (IETMs)
- Spatially disconnected assistance
- Static views and diagrams
- Cumbersome user interface
- Requires hands and job space
Leverage AR for
- Localization
- Hidden information
- In-situ Instructions and Training
IETM Interface
14
Related Work
Boeing Wireframe Bundle
- [Caudell-93],[Curtis-98]
Printer Servicing
- [Feiner-92, Feiner-93]
Toy Block Assemblies
- [Tang-03],[Robertson-08]
Dedicated Research Consortia
- 1999-2003 : ARVIKA
- 2001-2004 : STAR
- 2004-2006 : ARTESAS
ARVIKA
Printer Servicing [Feiner-93]
Boeing Wireframe Bundle [Curtis-98]
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Informational Prototype Target Domain
LAV25A1 Turret Interior
LAV25A1 Turret (Extracted)
LAV25A1 Armored Personnel Carrier
LAV25A1 Turret Entry Hatches
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Informational Prototype
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Informational Prototype Software
Built using ARMAR Architecture
3D scene generation
- Managed by Valve Source game engine
- Video merged via external application
Content
- 3D models (static & animated)
- 2D close-up drawings and photos
- 2D Text in screen-fixed HUD
- Attention-directing graphics
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Informational Prototype Hardware
Localization Sequence
Built using ARMAR Architecture
Displays
- Custom-built video see-through (VST)
Head-worn display (HWD)
- NVIS optical see-through (OST) HWD
Tracking
- OptiTrack optical tracker w/ active markers
- Fiducial-based optical tracking
Interaction
- Android phone-based wrist worn controller
19
User Study Tasks and Conditions
Preceded by 2 pilot studies
18 LAV25A1 maintenance tasks
- Actual tasks from IETM
- Arbitrary ordering to mitigate
familiarity and learning effects
- Installations/Removals
- Placement of Switches & Levers
- Inspections
Baseline comparison
techniques
- Fixed LCD Display (LCD)
"Improved" IETM
- Untracked HWD (HUD)
HUD Condition LCD Condition
20
Within-subject design
Counterbalanced start condition
Fixed, arbitrary task sequence
6 subjects (male, age 18-28),
in USMC LAV mechanic course
- 4 additional subjects in pilot study
Included subjective evaluation
Hypotheses
H1: AR faster completion than HUD, LCD
H2: AR faster localization than HUD, LCD
H3: AR produces fewer errors than HUD, LCD
H4: AR less head rotation than HUD, LCD
H5: AR less head translation than HUD, LCD
H6: AR faster head movement than HUD, LCD
User Study Experiment Design
21
User Study Results Localization and Completion
Localization time:
- Display condition produced significant
effect on task localization time
- Post-hoc comparison:
AR localization time 53% of LCD*
AR localization time 44% of HUD*
- Confirms H2
Task completion times:
- No significant effect of display condition
on task completion time
- Fails to confirm H1
*Statistically significant (p < 0.05) Completion Time
Localization Time
22
User Study Results Rotational Head Movement and Velocity
AR rotational movement:
- Pitch: 38% that of LCD*
- Roll: 25% that of LCD*
- Yaw: 35% that of LCD*
- Partially confirms H4 (AR vs. LCD)
AR rotational velocities:
- Roll: 1.48 times that of HUD*
- Yaw: 1.95 times that of HUD*
- Partially confirms H6 (rotational velocity)
LCD rotational velocities:
- Yaw: 1.74 times that of AR*
*Statistically significant (p < 0.05)
23
User Study Results Translational Head Movement
Mean translational head exertion:
AR 37% that of LCD*
AR 69% that of HUD
Partially confirms H5 (AR vs. LCD)
Mean translational head velocity:
AR 1.6 times that of HUD
LCD 1.7 times that of AR
Fails to confirm H6
(translational velocity)
*Statistically significant (p < 0.05)
2D Histograms of Head Position
Supporting Task Focus
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User Study Results User Survey
Median responses to survey questions:
- AR most satisfying
- LCD easiest to use
- AR as intuitive as LCD Difficult Easy
Satisfaction
Ease of Use
Intuitiveness
Most Preferred Condition:
- 4 of 6 subjects selected LCD
- Significant main effect (Friedman test)
- Only LCD-HUD significant (Wilcoxon test)
Most Intuitive Condition:
- 4 of 6 subjects selected AR
- No significant effect (Friedman test)
User comments:
"Enjoyed [AR] system the most..easy to navigate"
"I liked [AR] system..all I had to do was follow the
red line"
"Will be successful with better picture"
"Display gets in the way"
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Summary of Findings
1. AR allowed mechanics to localize more quickly than LCD
- Mechanics can begin work more quickly
- Reduces transition time between tasks
2. AR allowed mechanics to localize more quickly than HUD
- AR visualization adds value beyond untracked HUD displays
3. AR reduced head movement compared to LCD
- Could mean less stress and fatigue
- Must adjust for effects of wearing HWD
4. Mechanics found AR system intuitive and satisfying
5. Mechanics made few errors under any condition
Talk Structure
AR Assistance in Psychomotor
Phases of Procedural Tasks
(Chapter 4)
28
Related Work
AR Systems that track user and objects
- [Feiner-93],[Zauner-03],[Salonen-08]
Limited use of object tracking data
- Needle Biopsy Systems
[State-96],[Rosenthal-02],[Wacker-06]
Track needle throughout task for
internal visualization
Not prescriptive in nature
- Obstetrician trainer [Blum-07]
Depicts expert motions to emulating trainee
Offline learning tool; Not evaluated
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Psychomotor Prototype
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Psychomotor Prototype Software
Built using ARMAR Architecture
3D scene generation
- Managed by Goblin XNA
Content - Dynamic, prescriptive 3D arrows
- Dynamic Highlights
- Dynamic Billboard labels
- Motion Paths
- Assistance updated based on
changes to task environment
Dynamic, Prescriptive Arrows
Dynamic Highlights
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Psychomotor Prototype Hardware
Built using ARMAR Architecture
Display
- NVIS optical see-through (OST) HWD
Tracking
- OptiTrack optical tracker w/ active markers
- Fiducial-based optical tracking
Interaction
- Proactive computing model
- Custom USB button, Wiimote controller
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User Study Task
Assembly Task
Dart 510 Combustion Chamber
- 7 Chambers on engine
- Each chamber consists of:
Upper section “cone”
Lower section “can”
- Each chamber requires unique
pairing and alignment of cone and can
Workbench Assembly Environment
- Designed to generate statistical power
- Supports multiple iterations of assembly
Dart 510 Engine
Cone
Can Assembled
Chamber
Workbench Assembly Environment
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User Study Task
Step Description Activity Type
1 Locate Can X in Bin W Locate
2 Move Can X to Turntable Position
3 Locate Cone Y in Bin V Locate
4 Place Cone Y on Can X Position
5 Align Cone Y with Can X; Insert pins Align & Pin (Psychomotor)
6 Move assembly XY to Bin Z Position
Single trial: 6-step procedure attaching Can X and Cone Y
Analyzed by major activity type
Align & Pin step represents psychomotor phase
Trial repeated 14 times in fixed combinations of cans & cones
- Each combination appears at least once
- Pseudo-random selection of paired holes
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User Study Display Conditions
AR Condition LCD Condition
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User Study Experiment Design
Within-subject design
Counter-balanced start condition
Recruited 28 subjects
- 7 female, 21 male; Age 18–44, 𝑋 =26
- First 6 participants comprised pilot study
Included subjective evaluation
Hypotheses
- H1: AR faster technique during psychomotor activities
- H2: AR more accurate than LCD during psychomotor activities
- H3: AR most preferred technique
- H4: AR ranked as most intuitive
36
User Study Results Completion Time
Display condition significant main
effect on Align Activity completion
time.
- AR 21.3 seconds faster than LCD
Statistically significant (p < 0.05)
- Confirms H1
37
User Study Results Accuracy
Display condition significant main
effect on mean alignment error as
measured at task completion
Accuracy rating
- Binary measure of correct alignment
AR: 95.3% mean accuracy rate
LCD: 61.7% mean accuracy rate
Significant difference in mean accuracy rate
(p < 0.001)
Angular displacement
- Average angular difference between can and cone at task completion
AR: 0.08 radians (0.25 inter-hole widths)
LCD: 0.36 radians (1.15 inter-hole widths)
- AR average displacement 22% that of LCD; Statistically significant (p < 0.05)
Confirm hypothesis H2
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User Study Results Qualitative Results
Participants assigned higher ratings for
the AR condition in Ease of Use,
Satisfaction, and Intuitiveness
compared to LCD ratings
Most Preferred Condition
- 20 of 22 participants ranked AR as most preferred
(Significant, p < 0.001)
- Confirms hypothesis H3
Most Intuitive Condition
19 of 22 participants ranked AR as most preferred
(Significant, p < 0.001)
Confirms hypothesis H4
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Comparison to Physical Labels
Follow-up pilot study to test AR
against idealized baseline
Printed labels attached to
assembled components
Not always possible or
realistic
Study
- Same task; Substituted PRINTED for LCD
- 6 participants; all male; Age 19–27, 𝑋 =23.5
Results:
- Display condition failed to exhibit significant main effect on task
completion time for task completion time
Printed Condition
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Findings
1. AR allowed participants to complete align and pin activity
more quickly than LCD
2. AR allowed participants to complete align and pin activity
more accurately than LCD
3. AR most preferred technique
4. AR most intuitive technique
5. No evidence to suggest AR technique differs from idealized
condition
Talk Structure
Opportunistic Controls supporting
User Interaction with Procedural Tasks
(Chapter 5)
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Motivation
Many procedural tasks pose competing constraints:
- Constrained use of eyes and/or hands
- Hands not visible
- Cannot modify environment
Photo courtesy European Space Agency
43
Tangible UI harvested from the environment
Comprised of:
-A physical affordance
-A 3D widget
-One or more gestures
Opportunistic Control
44
Related Work 2D haptically discriminable widgets
- [Buxton-85]
3D virtual buttons on undifferentiated
surface
- [Weimer-89]
Tangible bits
- [Ishii-97]
Passive real-world interface props
- [Hinckley-94]
Light Widgets
- [Fails-02]
[Weimer-89]
[Buxton-85]
[Fails-02]
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Opportunistic Control Components
A naturally occurring affordance
A 3D Widget
Mapping between 3D widget and the affordance
A grammar of hand gestures
Mapping between gesture grammar and 3D Widget
Transformation between gesture and affordance spaces
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Affordances Supporting OCs
Button
Based
OCs
Valuator
Based
OCs
Movable
OCs
47
Affordance Rules of Thumb
Do not overload meaningful affordances
Do not endanger user in another context
Do not damage affordance in another context
48
Gesture Recognition
Each affordance is defined by a convex polyhedron
bounding the usable geometry of the OC
Vision algorithm locates user's hand in camera coordinates
using appearance-based techniques
Hand location mapped to affordance coordinate space
Hand’s placement/movement in affordance space identifies
gesture
Tracked segmentation:
- Gesture to affordance mapping in real time
- Reduced segmentation workload
- Entire OC can move
49
Widgets
Provides visual feedback linked to gesture/affordance
Geometry of widget tightly coupled with affordance
Modeled as 3D Widgets [Conner-92]
Augmented transition network (ATN) of widget responds to
user gestures and alters 3D model
Slider Widget ATN 3D Model of Slider Widget
50
User Observation Study
Examined 3 questions:
- How do users perceive potential OC affordances?
- How to redirect user thinking to view affordances as OCs?
- What heuristics determine the best affordances for OCs?
Study
- Fifteen subjects (11 male, 4 female)
- Subjects presented with common interface tasks [Dachselt-2005]
- Tasks associated with two domains
- Subjects selected any affordance to create a UI for accomplishing tasks
- “Wizard of Oz” feedback presented on hand held AR display
Using a connector for 3D
Object manipulation
(view through AR display)
51
User Observation Study
Maintenance Domain Home Entertainment
Domain
Discrete Valuator Task Menu Selection Task 3D Object Selection Task
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User Observation Study
53
User Observation Study Results
Subjects selected a plurality of valuator-based affordances in
both domains
Subjects used button-based and valuator-based affordance
interchangeably
Subjects combined affordances to accomplish tasks
General omission of movable OCs
Task Type Button Based Valuator Based Moveable Button Based Valuator Based Movable
3D object selection 13% 60% 27% 47% 40% 20%
3D object manipulation 7% 87% 20% 20% 67% 13%
3D scene control 13% 73% 27% 20% 53% 33%
2D document visualization 7% 67% 27% 20% 67% 13%
Discrete valuators 67% 20% 13% 67% 33% 7%
Continuous valuators 33% 40% 33% 47% 53% 13%
Menu selection 47% 53% 13% 53% 33% 0%
All Tasks 27% 57% 23% 39% 50% 14%
Maintenance Interaction Domain (MA) Home Entertainment Domain (HE) Task Type Button Based Valuator Based Moveable Button Based Valuator Based Movable
3D object selection 13% 60% 27% 47% 40% 20%
3D object manipulation 7% 87% 20% 20% 67% 13%
3D scene control 13% 73% 27% 20% 53% 33%
2D document visualization 7% 67% 27% 20% 67% 13%
Discrete valuators 67% 20% 13% 67% 33% 7%
Continuous valuators 33% 40% 33% 47% 53% 13%
Menu selection 47% 53% 13% 53% 33% 0%
All Tasks 27% 57% 23% 39% 50% 14%
Maintenance Interaction Domain (MA) Home Entertainment Domain (HE)
54
Preferred Physical Features
Top 3 features in each
domain were all located
roughly at eye level
Suggests importance of
location in selecting OC
affordances (minimize
physical exertion by user)
MA Domain HE Domain
20%
18%
11%
20%
11%
8%
55
Additional Findings
Influence of Surrounding Context
- Some users verbalized hesitancy to respond to task. Examples:
“I don’t know how to hook up a VCR”
“I’m not a mechanically inclined person”
- Suggests surrounding context might cloud OC perception
- OC implementations could/should use virtual content to mitigate this effect
(e.g., hide backgrounds, highlight affordance)
User Suggested Designs
56
Prototype
User’s view through HMD
Segmentation from overhead camera
Built using ARMAR architecture
Tracked overhead camera for gesture recognition
Tracked stereo video see-through HWD
Button, valuator, and movable OCs
Close up of Affordances
57
Movable OC
58
Interface Technique
User Study
Selection task
Five button based OCs used for selection
Baseline comparison technique (BL) :
Virtual buttons on flat, undifferentiated surface
Hypotheses
H1. OC faster than BL
H2. OC more accurate than BL
Included subjective evaluation
15 subjects (11 male, 4 female)
Counterbalanced, within-subject design
10 inspections (trials) x 5 locations
BL
OC
59
Interface Technique User Study (OC Condition)
60
Performance User Study Results
Quantitative Results
- OC completion time 86% that of BL
(statistically significant, =0.0125)
- Did not identify significant effects on error rates
Completion Time Errors
61
Performance User Study Results
Qualitative Results
- 73% of users preferred OC over FL (Significant ranking, p=0.02)
- Users liked ability to do
“eyes-free” interactions
- No significant differences in
mean response to ease of use,
satisfaction, or intuitiveness Likert
scale questions
Talk Structure
ARMAR Architecture (Chapter 6)
ARMAR Architecture
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Practical Rules of Thumb
Tracking & Registration
- Head tracking sufficient when accuracy requirements > 1cm
Object tracking recommended when requirement < 1cm
- Use soft edges to hide registration errors
- Sharing tracking information from networked cameras tracking
fiducial markers highly susceptible to distortion
Requires robust calibration whenever cameras change position
Displays
- Stereoscopic viewing preferred when hand tools are required
- Individualized calibration of head worn displays important when
requirement < 2cm
64
Talk Structure
Conclusions & Future Directions (Chapter 7)
66
Summary of Contributions
Evaluation of AR in Informational Phases of Procedural Tasks
- Explored ability of AR to support task focus during procedural tasks
IEEE TVCG 2011 (In press)
- Showed AR is faster at localizing mechanic during procedural tasks
- Showed AR reduces some types of head movement
IEEE ISMAR 2009 (Best Paper)
Evaluation of AR in Psychomotor Phases of Procedural Tasks
- Showed users aided by active, prescriptive assistance rendered using AR
were faster in completing psychomotor portion of realistic assembly task
- Showed users preferred AR over LCD condition
Submitting to IEEE ISMAR 2011 (Deadline May 2011)
67
Summary of Contributions
Opportunistic Controls for Procedural Tasks
- Explored user affinities for OC affordances
IEEE TVCG 2010
- Showed OCs are faster than undifferentiated baseline in supporting
selection task
ACM VRST 2008 (Best Paper)
Architecture for Constructing AR Interface for Procedural Tasks
- Showed architecture is sufficient and useful for constructing AR interfaces
for procedural tasks
USAFRL Tech Report 2007, ACM VRST 2008 , IEEE ISMAR 2009,
IEEE TVCG 2010, IEEE TVCG 2011
Future Directions View Pose Management
68
Attention directing without View Pose Management
Attention directing with View Pose Management
Future Directions
Psychomotor assistance
- What are the set of possible visualization techniques to aid common
psychomotor activities? Which are optimal?
69 Extract of taxonomy proposed by Guo and Tucker [96] with added estimates for psychomotor activities
Future Directions
Opportunistic Controls
- Add depth filter to allow hovering and clutching
- Feature-based tracking
- Automatically detect and analyze affordances to use as OCs
- Allow user to quickly indicate affordances for OCs
Training vs. Assistance
- Interaction
How best to promote learning while assisting?
Gradually relaxed assistance; remain as sentinel
- Which to apply?
Can one jump directly into assistance without any prior learning?
What types of tasks demand training? Which can suffice as assisted?
70
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Acknowledgments
Steve Feiner
Peter Allen, John Kender
Barbara Tversky, Mark Livingston
Thanks to:
- Maria, Eva, Anna
- Ohan Oda, Sean White, Lauren Wilcox, Nick Dedual, Mengu Sukan,
Christian Holz, Hrvoje Benko, Eddie Ishak, Dale Henderson, Paul Blaer, Quy O
- Department of Systems Engineering, USMA (West Point)
- Bengt-Olaf Schneider (NVIDIA) for technical assistance
- USMC Cadre and students at Aberdeen Proving Ground, MD
- Engineers at the Marine Corps Logistics Base, Albany, GA
- David Madigan, Kyle Johnsen, and Magnus Axholt
- Generous gifts from NVIDIA and Google
This research was funded in part by:
Office of Naval Research Grant N00014-04-1-0005
US Air Force Research Lab Grant FA8650-05-2-6647
US Army Advanced Civil Schooling Program
Questions?
72
Backup
73
74
User Study Results Completion time by
Task Type
76
User Study Results Localization and Completion
Mean localization times:
- AR 47% faster than LCD*
- AR 56% faster than HUD*
- Confirms H1
Mean task completion times:
- AR 23% faster than HUD
- LCD 18% faster than AR
- Fails to confirm H2
*Statistically significant (p < 0.05)
Completion Time
Localization Time
77
User Study Results Translational Head Movement
Mean translational head exertion:
AR 62% less movement than LCD*
AR 29% less movement than HUD
Partially confirms H3 (AR vs. LCD)
Mean translational head velocity:
AR 60% faster than HUD*
LCD 40% faster than AR*
Partially confirms H5 (AR vs. HUD)
*Statistically significant (p < 0.05)
Translational Head Exertion
Translational Head Velocity
78
Procedural Tasks
A task whose learning requires the integration of two kinds
of human capability–intellectual skills and motor skills
[Gagne-77]
Characteristics:
- Ordered set of prescribed activities
- Varying:
Level of required planning
Number of steps
Number of decision points
Required cuing
Flexibility of step ordering
Type of goal
Sandwich Assembly
Aircraft Assembly
Procedural Task Instructions Non-automated forms of Instruction
79
Workcard Lube chart Single-sheet Instructions
Manual
80
User Study Results Rotational Head Movement
Rotation about neck (yaw) greatest source of rotational head
movement
AR resulted in smaller ranges in head yaw in 15 of 18 tasks when
compared to LCD
Could equate to less stress on neck and shoulders (depending on
effects of HWD)
-70 -20 30 80 130
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
T15
T16
T17
T18
Ta
sk
Normalized Yaw Direction (degrees)
LCD
HUD
AR-70 -20 30 80 130
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
T11
T12
T13
T14
T15
T16
T17
T18
Ta
sk
Normalized Yaw Direction (degrees)
LCD
HUD
AR
APG Errors?
81
82
Talk Structure
Related Work exploring Procedural Tasks (Chapter 2)
AR Assistance in Informational
Phases of Procedural Tasks
(Chapter 3)
AR Assistance in Psychomotor
Phases of Procedural Tasks
(Chapter 4)
Opportunistic Controls supporting
User Interaction with Procedural Tasks
(Chapter 5)
ARMAR Architecture (Chapter 6)
Conclusions & Future Directions (Chapter 7)