Your SlideShare is downloading. ×
Poster WACAI 2012
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.


Saving this for later?

Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime - even offline.

Text the download link to your phone

Standard text messaging rates apply

Poster WACAI 2012


Published on

  • Be the first to comment

  • Be the first to like this

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

No notes for slide


  • 1. Simulate movement duration of human gestures for a virtual agent system using Fitts’ Law Quoc Anh Le, Catherine Pelachaud contact: 1. INTRODUCTION 2. FITTS’ LAW - Objective: Endow virtual agent Greta with -Fitts Law: empirical model of human muscle human gestures. movement for predicting the time necessary to move - Issue: How to simulate the movement a hand or finger to reach rapidly a target. duration of gestures? - The movement time (MT) is calculated for a movement distance (D) with the width of target (W) -Solution: Use Fitts’ Law function to estimate as below: the duration of linear hand movements in MT = a + b*log2(D/W+1) gesture trajectories. where log2(D/W+1) represents the index of difficult D - To do: Train the Fitts’ Law with data from (ID) to do the movement and (ID/MT) represents its W real human subjects to find out appropriate index of performance (IP). The parameters a and b parameters (i.e. intercept and slope) for the are empirically determined depending its application. virtual agent system. 3. RETRIEVE TRAINING DATA -Data Input: a video corpus of storytellers (Martin, LIMSI 2009). -Tool: Anvil (Kipp et al. LREC-08). -Data Output: annotation data of the spatial and temporal information of phases in a gesture: starting and the ending positions of wrist. - We assumed that the small path in a gesture trajectory is linear. FIG. 1 – Spatiotemporal annotation with Anvil tool 4. BUILD REGRESSION LINE EQUATION ID (bits) D (cm) MT (ms) IP (bits/s) 1.36 51.36 600 2.27 -0.24 16.88 200 -1.22 1.06 41.84 470 2.27 0.24 23.58 260 0.91 MT = 292.9 + 296.6*ID with R 2=0.532 0.54 29.03 370 1.45 1.79 69.37 1330 1.35 1.19 45.59 500 2.38 1.15 44.30 500 2.29 where a = 292.9 and b = 296.6 1.78 68.77 530 3.36 0.28 24.25 360 0.77 ... ... ... ... TAB. 1 – Retrieved data from real humans TAB. 2 - Scatter plot and regression line 5. LIMITATIONS 6. REFERENCES -Limitation of the Fitts’ Law method: It calculates the prediction 1. S. MacKenzie, A. Sellen, W. Buxton, A Comparison of Input Devices in time based on the distance between two wrist positions without Elemental Pointing and Dragging Tasks, In Proceedings of the CHI91 Conference on Human Factors in Computing Systems, pp. 161-166, New York: considering constraints of human gesture articulations. ACM, 1991. -Limitations of our approach: 2. H. Zhao, Fitts Law: Modeling Movement Time in HCI. In K. Knudtzon & C. Thomas (Eds); TiChi: Theories in computer human interaction, 2002. -the spatial information annotated from videos is 2D. 3. P. M. Fitts, The information capacity of the human motor system in controlling the -the context of gestures has not yet been considered. For amplitude of movement, Journal of experimental psychology, USA, 1954. instance the age of gesturers, the situation where they made 4. D. Miniotas, Application of Fitts law to eye gaze interaction, ACM Conference on Human Factors in Computer Systems (CHI00), 2000. gestures, etc.TEMPLATE DESIGN ©