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Presentation at ACM CHI'13 by Antti Oulasvirta (Max Planck Institute for Informatics) and Teemu Roos(University of Helsinki).

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- 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

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