Simulate movement duration of human gestures for a virtual agent system using Fitts ’ Law

1. Simulate movement duration of human gestures for a virtual agent system using Fitts’ Law Quoc Anh Le, Catherine Pelachaud contact: quoc@telecom-paristech.fr 1. INTRODUCTION 2. FITTS’ LAW • Objective: Endow virtual agent Greta with human gestures. • - Issue: How to simulate the movement duration of gestures? • Solution: Use Fitts’ Law function to estimate the duration of linear hand movements in gesture trajectories. • To do: Train the Fitts’ Law with data from real human subjects to find out appropriate parameters (i.e. intercept and slope) for the virtual agent system. • Fitts' Law: empirical model of human muscle movement for predicting the time necessary to move a hand or finger to reach rapidly a target. • - The movement time (MT) is calculated for a movement distance (D) with the width of target (W) as below: • MT = a + b*log2(D/W+1) • where log2(D/W+1) represents the index of difficult (ID) to do the movement and (ID/MT) represents its index of performance (IP). The parameters a and b are empirically determined depending its application. 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 MT = 292.9 + 296.6*ID with R2=0.532 where a = 292.9 and b = 296.6 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 time based on the distance between two wrist positions without considering constraints of human gesture articulations. • Limitations of our approach: • the spatial information annotated from videos is 2D. • the context of gestures has not yet been considered. For instance the age of gesturers, the situation where they made gestures, etc. • S. MacKenzie, A. Sellen, W. Buxton, A Comparison of Input Devices in Elemental Pointing and Dragging Tasks, In Proceedings of the CHI'91 Conference on Human Factors in Computing Systems, pp. 161-166, New York: ACM, 1991. • H. Zhao, Fitts' Law: Modeling Movement Time in HCI. In K. Knudtzon & C. Thomas (Eds); TiChi: Theories in computer human interaction, 2002. • P. M. Fitts, The information capacity of the human motor system in controlling the amplitude of movement, Journal of experimental psychology, USA, 1954. • D. Miniotas, Application of Fitts' law to eye gaze interaction, ACM Conference on Human Factors in Computer Systems (CHI'00), 2000.