SlideShare a Scribd company logo
1 of 39
Download to read offline
Antti Oulasvirta, Teemu Roos,
Arttu Modig, Laura Leppänen
Information Capacity of Full-body Movements
Information Capacity of Full-body Movements
Aimed movements are common
motor responses in HCI
Information capacity is measured in
repeated aimed movements
W W
D
[Fitts 54 JEP, Soukoreff & MacKenzie 2004 HCI]
ID
Information Capacity of Full-body Movements
W W
i ii iii iv v vi vii viii ix x
D
Throughput (TP, bits/s) is the rate
with which a user could have sent messages
D
We We
i ii iii iv v vi
Effective width WeInformation Capacity of Full-body Movements [Fitts 54 JEP, Soukoreff & MacKenzie 2004 HCI]
TP = ID / MT = log2(1 + D/W) / MT
[Soukoreff & MacKenzie 2004 HCI]
TP is used for comparing input devices
Information Capacity of Full-body Movements
Fitts-TP
3-10 bps
Information Capacity of Full-body Movements
Limitations of the Fitts-TP
Single movement point
Only end point matters
Target areas fixed in the
environment
Information Capacity of Full-body Movements
Multiple movement points
Continuous movement
Shape of movement
Information Capacity of Full-body Movements
Information capacity is the ability to
produce complex movement at will
“ Since the measurable aspects of motor responses [...]
are continuous variables, their information capacity is
limited only by the amount of statistical variability, or
noise, that is characteristic of repeated efforts to produce
the same response. ”
Paul Fitts (1954)
Information Capacity of Full-body Movements
Challenges
What is complexity?
How to compute information capacity?
Match between two sequences?
How to decorrelate mutual dependencies?
How to capture full-body movement?
X
Movement sequence
Information Capacity of Full-body Movements
X Y
Movement sequence Repetition
Information Capacity of Full-body Movements
X Y
h(X) entropy of X
Information Capacity of Full-body Movements
X Y
h(Y) entropy ofY
Information Capacity of Full-body Movements
X Y
I(X;Y) Mutual information between X andY
I(X;Y) = h(X) – h(X|Y) = h(Y) – h(Y|X)
Information Capacity of Full-body Movements
Information Capacity of Full-body Movements
Computational pipeline
x"
y"
Autoregression+ rx"
ry"
Gaussian+process+
r’x"
r’y"
II Complexity estimation
rxp1 rxp2 rxp3 rxp4 rxp5 rxp6
ryp1 ryp2 ryp3 ryp4 ryp5 ryp6
TP
V Mutual informationIII Dimension reduction
ρyx"
Correla2ons+
I Capture
Canonical+2me+warping+
ix,y"
IV Temporal alignment
xt xt+1 xt+2 xt+3 xt+4 xt+5
εt
(x) εt
(x) εt
(x) εt
(x)
εt
(x) εt
(x)
εt
(y)
yt yt+1 yt+2 yt+3 yt+4 yt+5
εt
(y) εt
(y) εt
(y)
εt
(y) εt
(y)
Step 1: Performance in intended
repetitions is captured
[CMU Mocap DB]
X Y
Information Capacity of Full-body Movements
Step 2: Complexity estimation is done
with 2nd order autoregression
εt-1
y)
xt-1 xt xt+1 xt+2 xt+3 xt+4
yt-1 yt yt+1 yt+2 yt+3 yt+4
εt-1
(x) εt
(x) εt+1
(x) εt+2
(x)
εt+3
(x) εt+4
(x)
εt
(y) εt+1
(y) εt+2
(y)
εt+3
(y) εt+4
(y)
Residuals
X
Y
Information Capacity of Full-body Movements
Step 3: Dimensionality reduction is done
with PCA or GP-LVM
[Lawrence 05 JLMR]
GP-LVM manifold for two dances in the ballet
data (3 latent dimensions)
X Y
Information Capacity of Full-body Movements
Selection of dimensions
0.00
0.05
0.10
0.15
0.20
AverageRMSE ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
0
50
100
150
200
250
AverageThroughput(bps)
2 4 6 8 12 16 20
Latent Dimensions
●
RMSE
Throughput
Information Capacity of Full-body Movements
Step 4:Temporal alignment (optional)
X
Y
CanonicalTimeWarping CTW
Information Capacity of Full-body Movements
X
Y
Step 4:Temporal alignment (optional)
CanonicalTimeWarping CTW
Information Capacity of Full-body Movements CanonicalTimeWarping CTW
X
Y
Step 4:Temporal alignment (optional)
Information Capacity of Full-body Movements [Zhou & De La Torre 2009 NIPS]
Example results
Information Capacity of Full-body Movements
Step 5: Mutual information is calculated
from estimated correlation of residuals
[Kendall & Stuart 68]
Mutual information is determined by the correlation of residuals:
We estimate this and add a bias correction:
Throughput is now mutual information per second
Information Capacity of Full-body Movements
First feasibility tests
Standing still
0 bps
Balancing with one leg
0 bps
Rapid caging of the palm
289 bps
43 bps
without CTW
PhaseSpace full-body suit and glove
Information Capacity of Full-body Movements
Sensitivity to noise in recording
instrument
●
●
●
●
●
●
●
●
● ● ● ● ● ● ● ●
0 0.0005 0.0015 0.0025
02004006008001200
Noise Factor
Throughput(bps)
●
TP(1|2)
TP(2|1)
PCA-TP
Information Capacity of Full-body Movements
Study 1: Ballerina
21-33
12-15
17-18
Information Capacity of Full-body Movements
Unencumbered 4 kg additional weight
Study 2: Mouse
4 Fitts-bps 2 Fitts-bps
Information Capacity of Full-body Movements
0 kg
4 kg
Low ID High ID
38 bps
24 bps 37 bps
37 bps
Unencumbered 4 kg additional weight
umbered 4 kg additional weight
Information Capacity of Full-body Movements
Unencumbered 4 kg additional weight
High-ID
TPs decreased when an ISI of 1,000 ms
was imposed
Slow motion
Information Capacity of Full-body Movements
Study 3: Minority Report
Information Capacity of Full-body Movements
PCA-TP 78
PCA-TP 440
Information Capacity of Full-body Movements
Results replicate a known perceptual
distraction in bimanual motor control
313 bps
353 bps
289 bps
[Meschner et al. 01 Nature]
Sweet spot at ~60 cm
Information Capacity of Full-body Movements
Bonus study: Expert gamer
SpaceFortress
[Boot et al. 10 Acta Psychologica]
First trials
2 bps
21 bps
After 20 hours trials
Information Capacity of Full-body Movements
Fitts-TP
Aimed movements
This paper
Full-body movements
Information Distance Changes in motion direction
Noise Effective width Variability between repetitions
W W
D
Information Capacity of Full-body Movements
Solutions
☐✓
☐
☐
☐
✓
✓
✓
Step 4:Time warping
Step 2:Autoregression
Step 3: Dimension reduction
Step 5: Mutual information
☐✓ Step 1: Optical capture
What is movement complexity?
How to compute information capacity?
Match between two sequences?
How to decorrelate mutual dependencies?
Capturing full-body movement?
Information Capacity of Full-body Movements
• Analyze information capacity allowed by your design
• Compare designs
• Expose human factors
• Explore best potentials for UIs
Information Capacity of Full-body Movements
infocapacity.hiit.fi
antti.oulasvirta@mpii.de
teemu.roos@cs.helsinki.fi
Implementation for Kinect
Interactivity i401

More Related Content

More from Aalto University

Neuromechanics of a Button Press: A talk at CHI 2018, April 2018
Neuromechanics of a Button Press: A talk at CHI 2018, April 2018Neuromechanics of a Button Press: A talk at CHI 2018, April 2018
Neuromechanics of a Button Press: A talk at CHI 2018, April 2018Aalto University
 
"Computational Support for Functionality Selection in Interaction Design" CHI...
"Computational Support for Functionality Selection in Interaction Design" CHI..."Computational Support for Functionality Selection in Interaction Design" CHI...
"Computational Support for Functionality Selection in Interaction Design" CHI...Aalto University
 
User Interfaces that Design Themselves: Talk given at Data-Driven Design Day ...
User Interfaces that Design Themselves: Talk given at Data-Driven Design Day ...User Interfaces that Design Themselves: Talk given at Data-Driven Design Day ...
User Interfaces that Design Themselves: Talk given at Data-Driven Design Day ...Aalto University
 
Inverse Modeling for Cognitive Science "in the Wild"
Inverse Modeling for Cognitive Science "in the Wild"Inverse Modeling for Cognitive Science "in the Wild"
Inverse Modeling for Cognitive Science "in the Wild"Aalto University
 
Computational Rationality I - a Lecture at Aalto University by Antti Oulasvirta
Computational Rationality I - a Lecture at Aalto University by Antti OulasvirtaComputational Rationality I - a Lecture at Aalto University by Antti Oulasvirta
Computational Rationality I - a Lecture at Aalto University by Antti OulasvirtaAalto University
 
HCI Research as Problem-Solving [CHI'16, presentation slides]
HCI Research as Problem-Solving [CHI'16, presentation slides] HCI Research as Problem-Solving [CHI'16, presentation slides]
HCI Research as Problem-Solving [CHI'16, presentation slides] Aalto University
 
Can Computers Design? Presented at interaction16, March 2, 2016, Helsinki by ...
Can Computers Design? Presented at interaction16, March 2, 2016, Helsinki by ...Can Computers Design? Presented at interaction16, March 2, 2016, Helsinki by ...
Can Computers Design? Presented at interaction16, March 2, 2016, Helsinki by ...Aalto University
 
Model-Based User Interface Optimization: Part V: DISCUSSION - At SICSA Summer...
Model-Based User Interface Optimization: Part V: DISCUSSION - At SICSA Summer...Model-Based User Interface Optimization: Part V: DISCUSSION - At SICSA Summer...
Model-Based User Interface Optimization: Part V: DISCUSSION - At SICSA Summer...Aalto University
 
Model-Based User Interface Optimization: Part IV: ADVANCED TOPICS - At SICSA ...
Model-Based User Interface Optimization: Part IV: ADVANCED TOPICS - At SICSA ...Model-Based User Interface Optimization: Part IV: ADVANCED TOPICS - At SICSA ...
Model-Based User Interface Optimization: Part IV: ADVANCED TOPICS - At SICSA ...Aalto University
 
Model-Based User Interface Optimization: Part III: SOLVING REAL PROBLEMS - At...
Model-Based User Interface Optimization: Part III: SOLVING REAL PROBLEMS - At...Model-Based User Interface Optimization: Part III: SOLVING REAL PROBLEMS - At...
Model-Based User Interface Optimization: Part III: SOLVING REAL PROBLEMS - At...Aalto University
 
Model-Based User Interface Optimization: Part II: LETTER ASSIGNMENT - At SICS...
Model-Based User Interface Optimization: Part II: LETTER ASSIGNMENT - At SICS...Model-Based User Interface Optimization: Part II: LETTER ASSIGNMENT - At SICS...
Model-Based User Interface Optimization: Part II: LETTER ASSIGNMENT - At SICS...Aalto University
 
Model-Based User Interface Optimization: Part I INTRODUCTION - At SICSA Summe...
Model-Based User Interface Optimization: Part I INTRODUCTION - At SICSA Summe...Model-Based User Interface Optimization: Part I INTRODUCTION - At SICSA Summe...
Model-Based User Interface Optimization: Part I INTRODUCTION - At SICSA Summe...Aalto University
 
CHI 2014 talk by Antti Oulasvirta: Automated Nonlinear Regression Modeling fo...
CHI 2014 talk by Antti Oulasvirta: Automated Nonlinear Regression Modeling fo...CHI 2014 talk by Antti Oulasvirta: Automated Nonlinear Regression Modeling fo...
CHI 2014 talk by Antti Oulasvirta: Automated Nonlinear Regression Modeling fo...Aalto University
 
Improving Two-Thumb Text Entry on Touchscreen Devices
Improving Two-Thumb Text Entry on Touchscreen DevicesImproving Two-Thumb Text Entry on Touchscreen Devices
Improving Two-Thumb Text Entry on Touchscreen DevicesAalto University
 
Studying interaction with 3D mobile maps
Studying interaction with 3D mobile mapsStudying interaction with 3D mobile maps
Studying interaction with 3D mobile mapsAalto University
 

More from Aalto University (15)

Neuromechanics of a Button Press: A talk at CHI 2018, April 2018
Neuromechanics of a Button Press: A talk at CHI 2018, April 2018Neuromechanics of a Button Press: A talk at CHI 2018, April 2018
Neuromechanics of a Button Press: A talk at CHI 2018, April 2018
 
"Computational Support for Functionality Selection in Interaction Design" CHI...
"Computational Support for Functionality Selection in Interaction Design" CHI..."Computational Support for Functionality Selection in Interaction Design" CHI...
"Computational Support for Functionality Selection in Interaction Design" CHI...
 
User Interfaces that Design Themselves: Talk given at Data-Driven Design Day ...
User Interfaces that Design Themselves: Talk given at Data-Driven Design Day ...User Interfaces that Design Themselves: Talk given at Data-Driven Design Day ...
User Interfaces that Design Themselves: Talk given at Data-Driven Design Day ...
 
Inverse Modeling for Cognitive Science "in the Wild"
Inverse Modeling for Cognitive Science "in the Wild"Inverse Modeling for Cognitive Science "in the Wild"
Inverse Modeling for Cognitive Science "in the Wild"
 
Computational Rationality I - a Lecture at Aalto University by Antti Oulasvirta
Computational Rationality I - a Lecture at Aalto University by Antti OulasvirtaComputational Rationality I - a Lecture at Aalto University by Antti Oulasvirta
Computational Rationality I - a Lecture at Aalto University by Antti Oulasvirta
 
HCI Research as Problem-Solving [CHI'16, presentation slides]
HCI Research as Problem-Solving [CHI'16, presentation slides] HCI Research as Problem-Solving [CHI'16, presentation slides]
HCI Research as Problem-Solving [CHI'16, presentation slides]
 
Can Computers Design? Presented at interaction16, March 2, 2016, Helsinki by ...
Can Computers Design? Presented at interaction16, March 2, 2016, Helsinki by ...Can Computers Design? Presented at interaction16, March 2, 2016, Helsinki by ...
Can Computers Design? Presented at interaction16, March 2, 2016, Helsinki by ...
 
Model-Based User Interface Optimization: Part V: DISCUSSION - At SICSA Summer...
Model-Based User Interface Optimization: Part V: DISCUSSION - At SICSA Summer...Model-Based User Interface Optimization: Part V: DISCUSSION - At SICSA Summer...
Model-Based User Interface Optimization: Part V: DISCUSSION - At SICSA Summer...
 
Model-Based User Interface Optimization: Part IV: ADVANCED TOPICS - At SICSA ...
Model-Based User Interface Optimization: Part IV: ADVANCED TOPICS - At SICSA ...Model-Based User Interface Optimization: Part IV: ADVANCED TOPICS - At SICSA ...
Model-Based User Interface Optimization: Part IV: ADVANCED TOPICS - At SICSA ...
 
Model-Based User Interface Optimization: Part III: SOLVING REAL PROBLEMS - At...
Model-Based User Interface Optimization: Part III: SOLVING REAL PROBLEMS - At...Model-Based User Interface Optimization: Part III: SOLVING REAL PROBLEMS - At...
Model-Based User Interface Optimization: Part III: SOLVING REAL PROBLEMS - At...
 
Model-Based User Interface Optimization: Part II: LETTER ASSIGNMENT - At SICS...
Model-Based User Interface Optimization: Part II: LETTER ASSIGNMENT - At SICS...Model-Based User Interface Optimization: Part II: LETTER ASSIGNMENT - At SICS...
Model-Based User Interface Optimization: Part II: LETTER ASSIGNMENT - At SICS...
 
Model-Based User Interface Optimization: Part I INTRODUCTION - At SICSA Summe...
Model-Based User Interface Optimization: Part I INTRODUCTION - At SICSA Summe...Model-Based User Interface Optimization: Part I INTRODUCTION - At SICSA Summe...
Model-Based User Interface Optimization: Part I INTRODUCTION - At SICSA Summe...
 
CHI 2014 talk by Antti Oulasvirta: Automated Nonlinear Regression Modeling fo...
CHI 2014 talk by Antti Oulasvirta: Automated Nonlinear Regression Modeling fo...CHI 2014 talk by Antti Oulasvirta: Automated Nonlinear Regression Modeling fo...
CHI 2014 talk by Antti Oulasvirta: Automated Nonlinear Regression Modeling fo...
 
Improving Two-Thumb Text Entry on Touchscreen Devices
Improving Two-Thumb Text Entry on Touchscreen DevicesImproving Two-Thumb Text Entry on Touchscreen Devices
Improving Two-Thumb Text Entry on Touchscreen Devices
 
Studying interaction with 3D mobile maps
Studying interaction with 3D mobile mapsStudying interaction with 3D mobile maps
Studying interaction with 3D mobile maps
 

Recently uploaded

Designing for Hardware Accessibility at Comcast
Designing for Hardware Accessibility at ComcastDesigning for Hardware Accessibility at Comcast
Designing for Hardware Accessibility at ComcastUXDXConf
 
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...FIDO Alliance
 
A Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyA Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyUXDXConf
 
SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...
SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...
SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...CzechDreamin
 
Demystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyDemystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyJohn Staveley
 
Powerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara LaskowskaPowerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara LaskowskaCzechDreamin
 
Enterprise Knowledge Graphs - Data Summit 2024
Enterprise Knowledge Graphs - Data Summit 2024Enterprise Knowledge Graphs - Data Summit 2024
Enterprise Knowledge Graphs - Data Summit 2024Enterprise Knowledge
 
AI revolution and Salesforce, Jiří Karpíšek
AI revolution and Salesforce, Jiří KarpíšekAI revolution and Salesforce, Jiří Karpíšek
AI revolution and Salesforce, Jiří KarpíšekCzechDreamin
 
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya HalderCustom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya HalderCzechDreamin
 
Speed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in MinutesSpeed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in Minutesconfluent
 
AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101vincent683379
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Patrick Viafore
 
Optimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through ObservabilityOptimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through ObservabilityScyllaDB
 
Structuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessStructuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessUXDXConf
 
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfWhere to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfFIDO Alliance
 
Intro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераIntro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераMark Opanasiuk
 
THE BEST IPTV in GERMANY for 2024: IPTVreel
THE BEST IPTV in  GERMANY for 2024: IPTVreelTHE BEST IPTV in  GERMANY for 2024: IPTVreel
THE BEST IPTV in GERMANY for 2024: IPTVreelreely ones
 
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...FIDO Alliance
 
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfIntroduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfFIDO Alliance
 
UiPath Test Automation using UiPath Test Suite series, part 1
UiPath Test Automation using UiPath Test Suite series, part 1UiPath Test Automation using UiPath Test Suite series, part 1
UiPath Test Automation using UiPath Test Suite series, part 1DianaGray10
 

Recently uploaded (20)

Designing for Hardware Accessibility at Comcast
Designing for Hardware Accessibility at ComcastDesigning for Hardware Accessibility at Comcast
Designing for Hardware Accessibility at Comcast
 
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
 
A Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyA Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System Strategy
 
SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...
SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...
SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...
 
Demystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyDemystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John Staveley
 
Powerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara LaskowskaPowerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara Laskowska
 
Enterprise Knowledge Graphs - Data Summit 2024
Enterprise Knowledge Graphs - Data Summit 2024Enterprise Knowledge Graphs - Data Summit 2024
Enterprise Knowledge Graphs - Data Summit 2024
 
AI revolution and Salesforce, Jiří Karpíšek
AI revolution and Salesforce, Jiří KarpíšekAI revolution and Salesforce, Jiří Karpíšek
AI revolution and Salesforce, Jiří Karpíšek
 
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya HalderCustom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
 
Speed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in MinutesSpeed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in Minutes
 
AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024
 
Optimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through ObservabilityOptimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through Observability
 
Structuring Teams and Portfolios for Success
Structuring Teams and Portfolios for SuccessStructuring Teams and Portfolios for Success
Structuring Teams and Portfolios for Success
 
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfWhere to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
 
Intro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераIntro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджера
 
THE BEST IPTV in GERMANY for 2024: IPTVreel
THE BEST IPTV in  GERMANY for 2024: IPTVreelTHE BEST IPTV in  GERMANY for 2024: IPTVreel
THE BEST IPTV in GERMANY for 2024: IPTVreel
 
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
Secure Zero Touch enabled Edge compute with Dell NativeEdge via FDO _ Brad at...
 
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfIntroduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
 
UiPath Test Automation using UiPath Test Suite series, part 1
UiPath Test Automation using UiPath Test Suite series, part 1UiPath Test Automation using UiPath Test Suite series, part 1
UiPath Test Automation using UiPath Test Suite series, part 1
 

Information Capacity of Full-body Movements (CHI'13)

  • 1. Antti Oulasvirta, Teemu Roos, Arttu Modig, Laura Leppänen Information Capacity of Full-body Movements
  • 2. Information Capacity of Full-body Movements Aimed movements are common motor responses in HCI
  • 3. Information capacity is measured in repeated aimed movements W W D [Fitts 54 JEP, Soukoreff & MacKenzie 2004 HCI] ID Information Capacity of Full-body Movements
  • 4. W W i ii iii iv v vi vii viii ix x D Throughput (TP, bits/s) is the rate with which a user could have sent messages D We We i ii iii iv v vi Effective width WeInformation Capacity of Full-body Movements [Fitts 54 JEP, Soukoreff & MacKenzie 2004 HCI] TP = ID / MT = log2(1 + D/W) / MT
  • 5. [Soukoreff & MacKenzie 2004 HCI] TP is used for comparing input devices Information Capacity of Full-body Movements
  • 7. Information Capacity of Full-body Movements Limitations of the Fitts-TP Single movement point Only end point matters Target areas fixed in the environment
  • 8. Information Capacity of Full-body Movements Multiple movement points Continuous movement Shape of movement
  • 9. Information Capacity of Full-body Movements Information capacity is the ability to produce complex movement at will “ Since the measurable aspects of motor responses [...] are continuous variables, their information capacity is limited only by the amount of statistical variability, or noise, that is characteristic of repeated efforts to produce the same response. ” Paul Fitts (1954)
  • 10. Information Capacity of Full-body Movements Challenges What is complexity? How to compute information capacity? Match between two sequences? How to decorrelate mutual dependencies? How to capture full-body movement?
  • 12. X Y Movement sequence Repetition Information Capacity of Full-body Movements
  • 13. X Y h(X) entropy of X Information Capacity of Full-body Movements
  • 14. X Y h(Y) entropy ofY Information Capacity of Full-body Movements
  • 15. X Y I(X;Y) Mutual information between X andY I(X;Y) = h(X) – h(X|Y) = h(Y) – h(Y|X) Information Capacity of Full-body Movements
  • 16. Information Capacity of Full-body Movements Computational pipeline x" y" Autoregression+ rx" ry" Gaussian+process+ r’x" r’y" II Complexity estimation rxp1 rxp2 rxp3 rxp4 rxp5 rxp6 ryp1 ryp2 ryp3 ryp4 ryp5 ryp6 TP V Mutual informationIII Dimension reduction ρyx" Correla2ons+ I Capture Canonical+2me+warping+ ix,y" IV Temporal alignment xt xt+1 xt+2 xt+3 xt+4 xt+5 εt (x) εt (x) εt (x) εt (x) εt (x) εt (x) εt (y) yt yt+1 yt+2 yt+3 yt+4 yt+5 εt (y) εt (y) εt (y) εt (y) εt (y)
  • 17. Step 1: Performance in intended repetitions is captured [CMU Mocap DB] X Y
  • 18. Information Capacity of Full-body Movements Step 2: Complexity estimation is done with 2nd order autoregression εt-1 y) xt-1 xt xt+1 xt+2 xt+3 xt+4 yt-1 yt yt+1 yt+2 yt+3 yt+4 εt-1 (x) εt (x) εt+1 (x) εt+2 (x) εt+3 (x) εt+4 (x) εt (y) εt+1 (y) εt+2 (y) εt+3 (y) εt+4 (y) Residuals X Y
  • 19. Information Capacity of Full-body Movements Step 3: Dimensionality reduction is done with PCA or GP-LVM [Lawrence 05 JLMR] GP-LVM manifold for two dances in the ballet data (3 latent dimensions) X Y
  • 20. Information Capacity of Full-body Movements Selection of dimensions 0.00 0.05 0.10 0.15 0.20 AverageRMSE ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 50 100 150 200 250 AverageThroughput(bps) 2 4 6 8 12 16 20 Latent Dimensions ● RMSE Throughput
  • 21. Information Capacity of Full-body Movements Step 4:Temporal alignment (optional) X Y CanonicalTimeWarping CTW
  • 22. Information Capacity of Full-body Movements X Y Step 4:Temporal alignment (optional) CanonicalTimeWarping CTW
  • 23. Information Capacity of Full-body Movements CanonicalTimeWarping CTW X Y Step 4:Temporal alignment (optional)
  • 24. Information Capacity of Full-body Movements [Zhou & De La Torre 2009 NIPS] Example results
  • 25. Information Capacity of Full-body Movements Step 5: Mutual information is calculated from estimated correlation of residuals [Kendall & Stuart 68] Mutual information is determined by the correlation of residuals: We estimate this and add a bias correction: Throughput is now mutual information per second
  • 26. Information Capacity of Full-body Movements First feasibility tests Standing still 0 bps Balancing with one leg 0 bps Rapid caging of the palm 289 bps 43 bps without CTW PhaseSpace full-body suit and glove
  • 27. Information Capacity of Full-body Movements Sensitivity to noise in recording instrument ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 0.0005 0.0015 0.0025 02004006008001200 Noise Factor Throughput(bps) ● TP(1|2) TP(2|1) PCA-TP
  • 28. Information Capacity of Full-body Movements Study 1: Ballerina 21-33 12-15 17-18
  • 29. Information Capacity of Full-body Movements Unencumbered 4 kg additional weight Study 2: Mouse 4 Fitts-bps 2 Fitts-bps
  • 30. Information Capacity of Full-body Movements 0 kg 4 kg Low ID High ID 38 bps 24 bps 37 bps 37 bps Unencumbered 4 kg additional weight umbered 4 kg additional weight
  • 31. Information Capacity of Full-body Movements Unencumbered 4 kg additional weight High-ID TPs decreased when an ISI of 1,000 ms was imposed Slow motion
  • 32. Information Capacity of Full-body Movements Study 3: Minority Report
  • 33. Information Capacity of Full-body Movements PCA-TP 78 PCA-TP 440
  • 34. Information Capacity of Full-body Movements Results replicate a known perceptual distraction in bimanual motor control 313 bps 353 bps 289 bps [Meschner et al. 01 Nature] Sweet spot at ~60 cm
  • 35. Information Capacity of Full-body Movements Bonus study: Expert gamer SpaceFortress [Boot et al. 10 Acta Psychologica] First trials 2 bps 21 bps After 20 hours trials
  • 36. Information Capacity of Full-body Movements Fitts-TP Aimed movements This paper Full-body movements Information Distance Changes in motion direction Noise Effective width Variability between repetitions W W D
  • 37. Information Capacity of Full-body Movements Solutions ☐✓ ☐ ☐ ☐ ✓ ✓ ✓ Step 4:Time warping Step 2:Autoregression Step 3: Dimension reduction Step 5: Mutual information ☐✓ Step 1: Optical capture What is movement complexity? How to compute information capacity? Match between two sequences? How to decorrelate mutual dependencies? Capturing full-body movement?
  • 38. Information Capacity of Full-body Movements • Analyze information capacity allowed by your design • Compare designs • Expose human factors • Explore best potentials for UIs
  • 39. Information Capacity of Full-body Movements infocapacity.hiit.fi antti.oulasvirta@mpii.de teemu.roos@cs.helsinki.fi Implementation for Kinect Interactivity i401