Whom to talk to? Estimating user interest from movement trajectories

Abstract

Correctly identifying people who are interested in an interaction with a mobile robot is an essential task for a smart Human-Robot Interaction.In this paper an approach is presented for selecting suitable trajectory features in a task specific manner from a huge amount of different forms of possible representations. Different sub-sampling techniques are proposed to generate trajectory sequences from which features are extracted. The trajectory data was generated in real world experiments that include extensive user interviews to acquire information about user behaviors and intentions. Using those feature vectors in a classification method enables the robot to estimate the user's interaction interest.For generating low-dimensional feature vectors, a common method, the Principle Component Analysis, is applied. The selection and combination of useful features out of a set of possible features is carried out by an information theoretic approach based on the Mutual Information and Joint Mutual Information with respect to the user's interaction interest. The introduced procedure is evaluated with neural classifiers, which are trained with the extracted features of the trajectories and the user behavior gained by observation as well as user interviewing. The results achieved indicate that an estimation of the user's interaction interest using trajectory information is feasible.

Multimodal real-time contingency detection for HRI

“…To tackle real interaction scenarios, approaches using visual motion have been promising. Muller et al [11] use motion trajectories and focused on selecting features from the raw information to classify engagment. More recent work from Lee at al.…”

Multi-cue Contingency Detection

“…Previously in [3], we implemented contingency detection using single vision-based cues and demonstrated the application of our contingency detection module as a perceptual component for engagement detection. Other work has focused on processing other individual channels, independently demonstrating the significance of gaze shift [14,15], agent trajectory [16,17], or audio cues [18] as contingent reactions.…”

Advanced information processing of MEMS motion sensors for gesture interaction

“…The usage and feeling for this facility, however, are still diminished by the fact that the user has to hold the Wiimote with his or her hand (Holzinger et al, 2010). Flexible gesture interaction applications and systems with enough dexterity still need accurate motion and trajectory information (Muller et al, 2008;Lin and Ding, 2013). Therefore, this research applies multiple-axis sensors with powerful microcontroller assistance provided by MEMS and silicon manufacturers to gesture interaction.…”