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

Antti Oulasvirta, Teemu Roos,
Arttu Modig, Laura Leppänen
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

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

Limitations of the Fitts-TP
Single movement point
Only end point matters
Target areas ﬁxed in the
environment

Multiple movement points
Continuous movement
Shape of movement

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)

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

X Y
Movement sequence Repetition

X Y
h(X) entropy of X

X Y
h(Y) entropy ofY

X Y
I(X;Y) Mutual information between X andY
I(X;Y) = h(X) – h(X|Y) = h(Y) – h(Y|X)

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

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

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

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

Step 4:Temporal alignment (optional)
X
Y
CanonicalTimeWarping CTW

X
Y
CanonicalTimeWarping CTW

Information Capacity of Full-body Movements CanonicalTimeWarping CTW
X
Y

Information Capacity of Full-body Movements [Zhou & De La Torre 2009 NIPS]
Example results

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

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

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

Study 1: Ballerina
21-33
12-15
17-18

Unencumbered 4 kg additional weight
Study 2: Mouse
4 Fitts-bps 2 Fitts-bps

0 kg
4 kg
Low ID High ID
38 bps
24 bps 37 bps
37 bps
umbered 4 kg additional weight

High-ID
TPs decreased when an ISI of 1,000 ms
was imposed
Slow motion

Study 3: Minority Report

PCA-TP 78
PCA-TP 440

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

Bonus study: Expert gamer
SpaceFortress
[Boot et al. 10 Acta Psychologica]
First trials
2 bps
21 bps
After 20 hours trials

Fitts-TP
Aimed movements
This paper
Full-body movements
Information Distance Changes in motion direction
Noise Effective width Variability between repetitions
W W
D

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?

• Analyze information capacity allowed by your design
• Compare designs
• Expose human factors
• Explore best potentials for UIs

infocapacity.hiit.ﬁ
antti.oulasvirta@mpii.de
teemu.roos@cs.helsinki.ﬁ
Implementation for Kinect
Interactivity i401

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

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